subject:(AdaBoost)

1. Prata, Thiago Lessa. Rastreamento de vídeo com aprendizagem em tempo real .

Degree: 2014, Universidade Federal de Pernambuco

URL: http://repositorio.ufpe.br/handle/123456789/11833

► Em visão computacional, a área de rastreamento de objetos tem crescido enormemente. O aumento do poder computacional na última década tem permitido que aplicações em… (more)

▼ Em visão computacional, a área de rastreamento de objetos tem crescido enormemente. O aumento do poder computacional na última década tem permitido que aplicações em tempo real sejam agora possíveis. Em particular, o ramo de rastreamento de objetos tem se beneficiado com essa evolução e agora é utilizado em diversas aplicações desde a área de segurança até a de entretenimento. As primeiras técnicas se baseiam principalmente no vetor de movimento de sub-regiões da imagem e comparação entre as sub-regiões de um quadro do vídeo com o seguinte. Com isso, uma pontuação é computada para cada posição no quadro seguinte no qual o objeto alvo tem maior probabilidade de estar e a posição com maior valor é escolhida como a sua nova posição. Esses rastreadores normalmente são chamados de rastreadores de curto prazo, isso porque uma vez que o objeto é perdido de vista não é possível que ele volte a ser rastreado. Em contrapartida, visando continuar o rastreamento mesmo quando ele é perdido por algum tempo, nos últimos anos uma nova classe de rastreadores foi criada: os rastreadores por detecção. Nestes métodos, uma fase de rastreamento define a posição do objeto em um quadro a partir da sua posição no quadro anterior. Além da fase de rastreamento, uma fase de detecção visa encontrar o objeto sem que haja qualquer dependência com o seu histórico de posicionamento. A resposta de cada uma das duas técnicas é combinada de forma que a nova posição seja determinada. Quando o rastreamento é perdido por causa de alguma condição de ruído (como oclusão ou algum movimento rápido), a detecção é utilizada para reinicializar o rastreamento, o que possibilita a criação de um rastreador de longo prazo. Visando construir tal tipo de rastreador, o presente trabalho elabora um método de rastreamento por detecção. Mais especificamente, o principal objetivo da técnica elaborada é rastrear um objeto em um cenário complexo onde existam outros objetos semelhantes com problemas de difícil tratamento como oclusão, mudança de escala e mudança de pose. Para que isso seja possível, foi utilizado um esquema baseado em detecção, rastreamento e aprendizagem. Na fase de rastreamento, um rastreador de curto prazo comum e consolidado é utilizado. A fase de aprendizagem tem a função de selecionar amostras para o treinamento do módulo de detecção. A fase de detecção é constituída por quatro classificadores em cascata. Dentre eles, o classificador online cascade boosted classifier (OCBC) é utilizado, uma das principais contribuições deste trabalho. O OCBC é um detector do tipo cascata que possui um treinamento em tempo de execução. O método criado foi testado utilizando várias bases de rastreamento de faces com diversos níveis de dificuldade e os resultados mostraram um avanço em relação ao estado da arte. Advisors/Committee Members: Ren, Tsang Ing (advisor), Cavalcanti, George Darmiton da Cunha (advisor).

Subjects/Keywords: Rastreamento; Detecção; Aprendizagem em Tempo Real; Adaboost

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Prata, T. L. (2014). Rastreamento de vídeo com aprendizagem em tempo real . (Thesis). Universidade Federal de Pernambuco. Retrieved from http://repositorio.ufpe.br/handle/123456789/11833

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Prata, Thiago Lessa. “Rastreamento de vídeo com aprendizagem em tempo real .” 2014. Thesis, Universidade Federal de Pernambuco. Accessed January 25, 2021. http://repositorio.ufpe.br/handle/123456789/11833.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Prata, Thiago Lessa. “Rastreamento de vídeo com aprendizagem em tempo real .” 2014. Web. 25 Jan 2021.

Vancouver:

Prata TL. Rastreamento de vídeo com aprendizagem em tempo real . [Internet] [Thesis]. Universidade Federal de Pernambuco; 2014. [cited 2021 Jan 25]. Available from: http://repositorio.ufpe.br/handle/123456789/11833.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Prata TL. Rastreamento de vídeo com aprendizagem em tempo real . [Thesis]. Universidade Federal de Pernambuco; 2014. Available from: http://repositorio.ufpe.br/handle/123456789/11833

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

2. Wrhel, Vladimír. Rozpoznávání vzorů v obraze pomocí AdaBoost: Pattern Recognition Using AdaBoost.

Degree: 2018, Brno University of Technology

URL: http://hdl.handle.net/11012/54359

► This paper deals about AdaBoost algorithm, which is used to create a strong classification function using a number of weak classifiers. We familiarize ourselves with… (more)

Subjects/Keywords: AdaBoost; Real AdaBoost; FloatBoost; WaldBoost; TCAcu; klasifikace; silný klasifikátor; slabý klasifikátor; OpenCV; AdaLib; AdaBoost; Real Adaboost; Floatboot; Waldboost; TCAcu; classification; strong classifier; weak classifier; OpenCV; AdaLib

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wrhel, V. (2018). Rozpoznávání vzorů v obraze pomocí AdaBoost: Pattern Recognition Using AdaBoost. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/54359

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wrhel, Vladimír. “Rozpoznávání vzorů v obraze pomocí AdaBoost: Pattern Recognition Using AdaBoost.” 2018. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/54359.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wrhel, Vladimír. “Rozpoznávání vzorů v obraze pomocí AdaBoost: Pattern Recognition Using AdaBoost.” 2018. Web. 25 Jan 2021.

Vancouver:

Wrhel V. Rozpoznávání vzorů v obraze pomocí AdaBoost: Pattern Recognition Using AdaBoost. [Internet] [Thesis]. Brno University of Technology; 2018. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/54359.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wrhel V. Rozpoznávání vzorů v obraze pomocí AdaBoost: Pattern Recognition Using AdaBoost. [Thesis]. Brno University of Technology; 2018. Available from: http://hdl.handle.net/11012/54359

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Missouri University of Science and Technology

3. Novokhodko, Alexander. Neural networks with categorical valued inputs and applications to intrusion detection.

Degree: PhD, Computer Engineering, Missouri University of Science and Technology

URL: https://scholarsmine.mst.edu/doctoral_dissertations/1594

► "The main contribution of this study is a neural network intrusion detector, built with a specific goal to minimize false alarms. The obtained performance, 0%… (more)

Subjects/Keywords: AdaBoost; Computer Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Novokhodko, A. (n.d.). Neural networks with categorical valued inputs and applications to intrusion detection. (Doctoral Dissertation). Missouri University of Science and Technology. Retrieved from https://scholarsmine.mst.edu/doctoral_dissertations/1594

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Chicago Manual of Style (16^th Edition):

Novokhodko, Alexander. “Neural networks with categorical valued inputs and applications to intrusion detection.” Doctoral Dissertation, Missouri University of Science and Technology. Accessed January 25, 2021. https://scholarsmine.mst.edu/doctoral_dissertations/1594.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

MLA Handbook (7^th Edition):

Novokhodko, Alexander. “Neural networks with categorical valued inputs and applications to intrusion detection.” Web. 25 Jan 2021.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Novokhodko A. Neural networks with categorical valued inputs and applications to intrusion detection. [Internet] [Doctoral dissertation]. Missouri University of Science and Technology; [cited 2021 Jan 25]. Available from: https://scholarsmine.mst.edu/doctoral_dissertations/1594.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Council of Science Editors:

Novokhodko A. Neural networks with categorical valued inputs and applications to intrusion detection. [Doctoral Dissertation]. Missouri University of Science and Technology; Available from: https://scholarsmine.mst.edu/doctoral_dissertations/1594

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Brno University of Technology

4. Melo, Jakub. RoboAuto - Detekce automobilů: RoboAuto - Car Detection.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/56136

► This thesis deals with detection and tracking of cars viewed from behind. For the detection, Adaboost algorithm is used. The tracking is done using Kalman… (more)

Subjects/Keywords: Detekce automobilů; Sledování objektů; Adaboost; Kalmanův filtr; Car detection; Object tracking; Adaboost; Kalman filter

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Melo, J. (2019). RoboAuto - Detekce automobilů: RoboAuto - Car Detection. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/56136

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Melo, Jakub. “RoboAuto - Detekce automobilů: RoboAuto - Car Detection.” 2019. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/56136.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Melo, Jakub. “RoboAuto - Detekce automobilů: RoboAuto - Car Detection.” 2019. Web. 25 Jan 2021.

Vancouver:

Melo J. RoboAuto - Detekce automobilů: RoboAuto - Car Detection. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/56136.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Melo J. RoboAuto - Detekce automobilů: RoboAuto - Car Detection. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/56136

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

5. Hradiš, Michal. Sdílení lokální informace pro rychlejší detekci objektů: Sharing Local Information for Faster Scanning-Window Object Detection.

Degree: 2018, Brno University of Technology

URL: http://hdl.handle.net/11012/63246

► This thesis aims to improve existing scanning-window object detectors by exploiting information shared among neighboring image windows. This goal is realized by two novel methods… (more)

Subjects/Keywords: Detekce objektů; AdaBoost; WaldBoost; EnMS; Object detection; AdaBoost; WaldBoost; EnMS; neighborhood suppression; scanning-window

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Hradiš, M. (2018). Sdílení lokální informace pro rychlejší detekci objektů: Sharing Local Information for Faster Scanning-Window Object Detection. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/63246

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Hradiš, Michal. “Sdílení lokální informace pro rychlejší detekci objektů: Sharing Local Information for Faster Scanning-Window Object Detection.” 2018. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/63246.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hradiš, Michal. “Sdílení lokální informace pro rychlejší detekci objektů: Sharing Local Information for Faster Scanning-Window Object Detection.” 2018. Web. 25 Jan 2021.

Vancouver:

Hradiš M. Sdílení lokální informace pro rychlejší detekci objektů: Sharing Local Information for Faster Scanning-Window Object Detection. [Internet] [Thesis]. Brno University of Technology; 2018. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/63246.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hradiš M. Sdílení lokální informace pro rychlejší detekci objektů: Sharing Local Information for Faster Scanning-Window Object Detection. [Thesis]. Brno University of Technology; 2018. Available from: http://hdl.handle.net/11012/63246

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

6. Chaves, Bruno Butilhão. Estudo do algoritmo AdaBoost de aprendizagem de máquina aplicado a sensores e sistemas embarcados.

Degree: Mestrado, Engenharia de Controle e Automação Mecânica, 2011, University of São Paulo

URL: http://www.teses.usp.br/teses/disponiveis/3/3152/tde-12062012-163740/ ;

►

O estudo da Inteligência Artificial e de suas técnicas tem trazido grandes resultados para a evolução da tecnologia em diversas áreas. Técnicas já conhecidas como… (more)

▼

O estudo da Inteligência Artificial e de suas técnicas tem trazido grandes resultados para a evolução da tecnologia em diversas áreas. Técnicas já conhecidas como as Redes Neurais e Árvores de Decisão vêm sendo aprimoradas por técnicas de Boosting como o Adaptive Boosting. Esta técnica é uma das que apresenta maior perspectiva de crescimento devido a seu potencial, flexibilidade e simplicidade para ser implementada em diferentes cenários, como por exemplo, no tratamento de imagens para reconhecimento de padrões. Um mercado com grande potencial para se beneficiar da técnica de Boosting, e em especial do AdaBoost, é o mercado de sensores. É cada vez mais comum a utilização de sensores isolados ou sistemas de múltiplos sensores trabalhando concomitantemente para se atingir um objetivo comum. Na utilização de sistemas embarcados compostos por sensores para realização de análises e tomadas de decisão são cada vez mais requisitados, principalmente onde se requer algum tipo de reconhecimento de padrão. O objetivo desta dissertação é estudar e desenvolver o conhecimento do algoritmo AdaBoost para aplicação em sensores, de forma a aprimorar a sensibilidade e precisão das medições, tanto de sensores isolados como de sistemas complexos com vários sensores, sem que seja necessário realizar modificações no próprio sensor. O estudo estende-se também em como implementar o algoritmo inteligente a um dispositivo autônomo composto por sensores e um microprocessador que contenha um classificador embarcado de reconhecimento de padrões. Para demonstrar a utilidade da técnica, foi realizado um estudo de caso utilizando um sistema composto de sensores capacitivos interdigitalizados e microfabricados, sensores de temperatura e sensor a fibra óptica, para verificar adulterações em combustíveis automotivos, em especial, do etanol combustível. Sete experimentos são apresentados no trabalho. Índices acima de 90% de classificações corretas foram obtidos, indicando a viabilidade da utilização do algoritmo para calibração de sensores ou rede de sensores. Por fim, foi desenvolvida com sucesso uma forma de embarcar o classificador treinado em um microprocessador, confirmando assim ser possível desenvolver dispositivos embarcados contendo essa tecnologia.

Studies on Artificial Intelligence and its techniques have provided great results for the whole technology evolution in several areas. Techniques known as Neural Networks and Decision Trees have been improved by Boosting techniques such as Adaptive Boosting. This particular technique presents great growth prospects due to its potential, flexibility and simplicity to be implemented in different scenarios, such as image analysis for pattern recognition. A specific market that can greatly benefit from the technique of Boosting and particularly AdaBoost is the sensor market. The use of isolated …

Advisors/Committee Members: Torikai, Delson.

Subjects/Keywords: AdaBoost; AdaBoost; Adulteração de combustível; Aprendizagem de máquina; Boosting; Boosting; Dispositivos embarcados; Embedded; Machine learning; Pattern recognition; Reconhecimento de padrão; Sensores

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chaves, B. B. (2011). Estudo do algoritmo AdaBoost de aprendizagem de máquina aplicado a sensores e sistemas embarcados. (Masters Thesis). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/3/3152/tde-12062012-163740/ ;

Chicago Manual of Style (16^th Edition):

Chaves, Bruno Butilhão. “Estudo do algoritmo AdaBoost de aprendizagem de máquina aplicado a sensores e sistemas embarcados.” 2011. Masters Thesis, University of São Paulo. Accessed January 25, 2021. http://www.teses.usp.br/teses/disponiveis/3/3152/tde-12062012-163740/ ;.

MLA Handbook (7^th Edition):

Chaves, Bruno Butilhão. “Estudo do algoritmo AdaBoost de aprendizagem de máquina aplicado a sensores e sistemas embarcados.” 2011. Web. 25 Jan 2021.

Vancouver:

Chaves BB. Estudo do algoritmo AdaBoost de aprendizagem de máquina aplicado a sensores e sistemas embarcados. [Internet] [Masters thesis]. University of São Paulo; 2011. [cited 2021 Jan 25]. Available from: http://www.teses.usp.br/teses/disponiveis/3/3152/tde-12062012-163740/ ;.

Council of Science Editors:

Chaves BB. Estudo do algoritmo AdaBoost de aprendizagem de máquina aplicado a sensores e sistemas embarcados. [Masters Thesis]. University of São Paulo; 2011. Available from: http://www.teses.usp.br/teses/disponiveis/3/3152/tde-12062012-163740/ ;

7. Penne, Thomas. Développement d'un système de tracking vidéo sur caméra robotisée : Development of a video tracking system on a robotic camera.

Degree: Docteur es, Informatique, 2011, Université Blaise-Pascale, Clermont-Ferrand II

URL: http://www.theses.fr/2011CLF22167

►

Ces dernières années se caractérisent par la prolifération des systèmes de vidéo-surveillance et par l’automatisation des traitements que ceux-ci intègrent. Parallèlement, le problème du suivi… (more)

▼

Ces dernières années se caractérisent par la prolifération des systèmes de vidéo-surveillance et par l’automatisation des traitements que ceux-ci intègrent. Parallèlement, le problème du suivi d’objets est devenu en quelques années un problème récurrent dans de nombreux domaines et notamment en vidéo-surveillance. Dans le cadre de cette thèse, nous proposons une nouvelle méthode de suivi d’objet, basée sur la méthode Ensemble Tracking et intégrant deux améliorations majeures. La première repose sur une séparation de l’espace hétérogène des caractéristiques en un ensemble de sous-espaces homogènes appelés modules et sur l’application, sur chacun d’eux, d’un algorithme basé Ensemble Tracking. La seconde adresse, quant à elle, l’apport d’une solution à la nouvelle problématique de suivi induite par cette séparation des espaces, à savoir la construction d’un filtre particulaire spécifique exploitant une pondération des différents modules utilisés afin d’estimer à la fois, pour chaque image de la séquence, la position et les dimensions de l’objet suivi, ainsi que la combinaison linéaire des différentes décisions modulaires conduisant à l’observation la plus discriminante. Les différents résultats que nous présentons illustrent le bon fonctionnement global et individuel de l’ensemble des propriétés spécifiques de la méthode et permettent de comparer son efficacité à celle de plusieurs algorithmes de suivi de référence. De plus, l’ensemble des travaux a fait l’objet d’un développement industriel sur les consoles de traitement de la société partenaire. En conclusion de ces travaux, nous présentons les perspectives que laissent entrevoir ces développements originaux, notamment en exploitant les possibilités offertes par la modularité de l’algorithme ou encore en rendant dynamique le choix des modules utilisés en fonction de l’efficacité de chacun dans une situation donnée.

Recent years have been characterized by the overgrowth of video-surveillance systems and by automation of treatments they integrate. At the same time, object tracking has become, within years, a recurring problem in many domains and particularly in video-surveillance. In this dissertation, we propose a new object tracking method, based on the Ensemble Tracking method and integrating two main improvements. The first one lies on the separation of the heterogeneous feature space into a set of homogenous sub-spaces called modules and on the application, on each of them, of an Ensemble Tracking-based algorithm. The second one deals with the new tracking problem induced by this separation by building a specific particle filter. This filter weights each used module in order to estimate, for each frame in the sequence, both position and dimensions of the tracked object and the linear combination of modular decisions leading to the most discriminative observation. The results we present illustrate the global and individual efficiency of all the specific properties of our method and allow comparing this efficiency with the one of several reference tracking…

Advisors/Committee Members: Barra, Vincent (thesis director), Tilmant, Christophe (thesis director).

Subjects/Keywords: Suivi d’objet; Classification; Adaboost; Ensemble Tracking; Espaces de caractéristiques; Filtrage particulaire; Object tracking; Classification; Adaboost; Ensemble Tracking; Feature spaces; Particle filtering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Penne, T. (2011). Développement d'un système de tracking vidéo sur caméra robotisée : Development of a video tracking system on a robotic camera. (Doctoral Dissertation). Université Blaise-Pascale, Clermont-Ferrand II. Retrieved from http://www.theses.fr/2011CLF22167

Chicago Manual of Style (16^th Edition):

Penne, Thomas. “Développement d'un système de tracking vidéo sur caméra robotisée : Development of a video tracking system on a robotic camera.” 2011. Doctoral Dissertation, Université Blaise-Pascale, Clermont-Ferrand II. Accessed January 25, 2021. http://www.theses.fr/2011CLF22167.

MLA Handbook (7^th Edition):

Penne, Thomas. “Développement d'un système de tracking vidéo sur caméra robotisée : Development of a video tracking system on a robotic camera.” 2011. Web. 25 Jan 2021.

Vancouver:

Penne T. Développement d'un système de tracking vidéo sur caméra robotisée : Development of a video tracking system on a robotic camera. [Internet] [Doctoral dissertation]. Université Blaise-Pascale, Clermont-Ferrand II; 2011. [cited 2021 Jan 25]. Available from: http://www.theses.fr/2011CLF22167.

Council of Science Editors:

Penne T. Développement d'un système de tracking vidéo sur caméra robotisée : Development of a video tracking system on a robotic camera. [Doctoral Dissertation]. Université Blaise-Pascale, Clermont-Ferrand II; 2011. Available from: http://www.theses.fr/2011CLF22167

Brno University of Technology

8. Chmiel, Filip. Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position.

Degree: 2020, Brno University of Technology

URL: http://hdl.handle.net/11012/189682

► This paper presents the method for a head pose and orientation detection based on computer vision techniques. The method is based on the AdaBoost approach… (more)

Subjects/Keywords: uživatelská rozhraní; detekce obličeje; počítačové vidění; AdaBoost; optický tok; user interface; face detection; computer vision; AdaBoost; optical flow

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chmiel, F. (2020). Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/189682

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chmiel, Filip. “Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position.” 2020. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/189682.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chmiel, Filip. “Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position.” 2020. Web. 25 Jan 2021.

Vancouver:

Chmiel F. Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/189682.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chmiel F. Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/189682

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

9. Vojáček, Cyril. Rozpoznání obličeje: Face Recognition.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/53386

► This thesis is about face detection and recognition from video. Main emphasis is on computational speed, so it can be used for a real-time processing.… (more)

Subjects/Keywords: obličej; detekce; rozpoznávání; NPD příznak; Viola & Jones; Adaboost; OpenTLD; face; detection; recognition; NPD feature; Viola & Jones; Adaboost; OpenTLD

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Vojáček, C. (2019). Rozpoznání obličeje: Face Recognition. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/53386

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Vojáček, Cyril. “Rozpoznání obličeje: Face Recognition.” 2019. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/53386.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Vojáček, Cyril. “Rozpoznání obličeje: Face Recognition.” 2019. Web. 25 Jan 2021.

Vancouver:

Vojáček C. Rozpoznání obličeje: Face Recognition. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/53386.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vojáček C. Rozpoznání obličeje: Face Recognition. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/53386

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

10. Pomykal, Antonín. Detekce automobilů v obraze: Detection of Vehicles in Image.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/56021

► This work deals with the possibility of detection of cars in the image using the characteristics of cars with custom created image features , which… (more)

Subjects/Keywords: detekce automobilů; OpenCV; Adaboost; Haarovy příznaky; obrazové příznaky; car detection; OpenCV; Adaboost; Haar features; image features

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APA (6^th Edition):

Pomykal, A. (2019). Detekce automobilů v obraze: Detection of Vehicles in Image. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/56021

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Pomykal, Antonín. “Detekce automobilů v obraze: Detection of Vehicles in Image.” 2019. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/56021.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Pomykal, Antonín. “Detekce automobilů v obraze: Detection of Vehicles in Image.” 2019. Web. 25 Jan 2021.

Vancouver:

Pomykal A. Detekce automobilů v obraze: Detection of Vehicles in Image. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/56021.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pomykal A. Detekce automobilů v obraze: Detection of Vehicles in Image. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/56021

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

11. Raszka, Aleš. Klasifikace vozidel s použitím radaru: Vehicle Classification Using Radar.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/69590

► This Master thesis deals with usage of radar signal for vehicle classification. The thesis uses radar modules with continuous wave based on Doppler effect. Radar… (more)

Subjects/Keywords: Dopplerův jev; radar; zpracování signálu; klasifikace; vozidlo; SVM; Adaboost; Doppler effect; radar; signal processing; classification; vehicle; SVM; Adaboost

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APA (6^th Edition):

Raszka, A. (2019). Klasifikace vozidel s použitím radaru: Vehicle Classification Using Radar. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/69590

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Raszka, Aleš. “Klasifikace vozidel s použitím radaru: Vehicle Classification Using Radar.” 2019. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/69590.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Raszka, Aleš. “Klasifikace vozidel s použitím radaru: Vehicle Classification Using Radar.” 2019. Web. 25 Jan 2021.

Vancouver:

Raszka A. Klasifikace vozidel s použitím radaru: Vehicle Classification Using Radar. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/69590.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Raszka A. Klasifikace vozidel s použitím radaru: Vehicle Classification Using Radar. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/69590

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

12. Tretter, Zdeněk. Klasifikátor biometrických obrazových dat: Biometric Image Data Classifier.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/56618

► The aim of this thesis is to design and implement fingerprint classifier, which classifies the fingerprints based on the scanner used. Reader is presented with… (more)

Subjects/Keywords: otisk prstu; klasifikace; AdaBoost; kaskáda klasifikátorů; obrazové filtry; C++; OpenCV; fingerprint; classification; AdaBoost; cascade of classifiers; image filters; C++; OpenCV

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Tretter, Z. (2019). Klasifikátor biometrických obrazových dat: Biometric Image Data Classifier. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/56618

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Tretter, Zdeněk. “Klasifikátor biometrických obrazových dat: Biometric Image Data Classifier.” 2019. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/56618.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Tretter, Zdeněk. “Klasifikátor biometrických obrazových dat: Biometric Image Data Classifier.” 2019. Web. 25 Jan 2021.

Vancouver:

Tretter Z. Klasifikátor biometrických obrazových dat: Biometric Image Data Classifier. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/56618.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tretter Z. Klasifikátor biometrických obrazových dat: Biometric Image Data Classifier. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/56618

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

13. Charvát, Jaroslav. Ovládání počítače pomocí gest: Human-Machine Interface Based on Gestures.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/54065

► Master's thesis "Human-Machine Interface Based on Gestures" depicts the theoretical background of the computer vision and gesture recognition. It describes more in detail different methods… (more)

Subjects/Keywords: Počítačové vidění; detekce obličeje; rozpoznávání gest; AdaBoost; PCA.; Computer vision; face detection; gesture recognition; AdaBoost; PCA.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Charvát, J. (2019). Ovládání počítače pomocí gest: Human-Machine Interface Based on Gestures. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/54065

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Charvát, Jaroslav. “Ovládání počítače pomocí gest: Human-Machine Interface Based on Gestures.” 2019. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/54065.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Charvát, Jaroslav. “Ovládání počítače pomocí gest: Human-Machine Interface Based on Gestures.” 2019. Web. 25 Jan 2021.

Vancouver:

Charvát J. Ovládání počítače pomocí gest: Human-Machine Interface Based on Gestures. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/54065.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Charvát J. Ovládání počítače pomocí gest: Human-Machine Interface Based on Gestures. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/54065

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

14. Juránek, Roman. Akcelerace detekce objektů pomocí klasifikátorů: Acceleration of Object Detection Using Classifiers.

Degree: 2018, Brno University of Technology

URL: http://hdl.handle.net/11012/63272

► Detection of objects in computer vision is a complex task. One of most popular and well explored approaches is the use of statistical classifiers and… (more)

Subjects/Keywords: Detekce objektů; AdaBoost; WaldBoost; Akcelerace; SIMD; Minimalizace ceny; Object Detection; AdaBoost; WaldBoost; Acceleration; SIMD; Cost Minimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Juránek, R. (2018). Akcelerace detekce objektů pomocí klasifikátorů: Acceleration of Object Detection Using Classifiers. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/63272

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Juránek, Roman. “Akcelerace detekce objektů pomocí klasifikátorů: Acceleration of Object Detection Using Classifiers.” 2018. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/63272.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Juránek, Roman. “Akcelerace detekce objektů pomocí klasifikátorů: Acceleration of Object Detection Using Classifiers.” 2018. Web. 25 Jan 2021.

Vancouver:

Juránek R. Akcelerace detekce objektů pomocí klasifikátorů: Acceleration of Object Detection Using Classifiers. [Internet] [Thesis]. Brno University of Technology; 2018. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/63272.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Juránek R. Akcelerace detekce objektů pomocí klasifikátorů: Acceleration of Object Detection Using Classifiers. [Thesis]. Brno University of Technology; 2018. Available from: http://hdl.handle.net/11012/63272

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

15. Čepl, Radek. Optimalizace algoritmů pro zpracování obrazu v C++ pomocí šablon: Image Processing Algorithms Optimization Using C++ Templates.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/55594

► Bachelor's thesis deals with image processing algorithm AdaBoost optimalization using C++ templates. Head aim of this thesis is effective evaluation of Haar Features with constant… (more)

Subjects/Keywords: AdaBoost; Haarovy příznaky; Integrální obraz; Klasifikátor; C++ šablony; AdaBoost; Haar features; Integral image; Classifier; C++ templates

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Čepl, R. (2019). Optimalizace algoritmů pro zpracování obrazu v C++ pomocí šablon: Image Processing Algorithms Optimization Using C++ Templates. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/55594

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Čepl, Radek. “Optimalizace algoritmů pro zpracování obrazu v C++ pomocí šablon: Image Processing Algorithms Optimization Using C++ Templates.” 2019. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/55594.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Čepl, Radek. “Optimalizace algoritmů pro zpracování obrazu v C++ pomocí šablon: Image Processing Algorithms Optimization Using C++ Templates.” 2019. Web. 25 Jan 2021.

Vancouver:

Čepl R. Optimalizace algoritmů pro zpracování obrazu v C++ pomocí šablon: Image Processing Algorithms Optimization Using C++ Templates. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/55594.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Čepl R. Optimalizace algoritmů pro zpracování obrazu v C++ pomocí šablon: Image Processing Algorithms Optimization Using C++ Templates. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/55594

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

16. Šašinka, Ondřej. Detekce obličeje: Face Detection.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/53827

► This MSc Thesis deals with face detection in image. In this approach, facial features (eyes, nose, mouth corners) are detected first and then joined to… (more)

Subjects/Keywords: Detekce obličeje; detekce obličejových rysů; AdaBoost; Haarovy vlnky; integrální obraz.; Face detection; facial features detection; AdaBoost; Haar wavelets; integral image.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Šašinka, O. (2019). Detekce obličeje: Face Detection. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/53827

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Šašinka, Ondřej. “Detekce obličeje: Face Detection.” 2019. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/53827.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Šašinka, Ondřej. “Detekce obličeje: Face Detection.” 2019. Web. 25 Jan 2021.

Vancouver:

Šašinka O. Detekce obličeje: Face Detection. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/53827.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Šašinka O. Detekce obličeje: Face Detection. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/53827

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

17. Pospíšil, Josef. Digitalizace historických map: Ancient Maps Digitizing.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/53177

This work is about processing of historical maps, especially their digitizing and vectorization. The main focuses of this project are maps from the second historical military mapping and finding methods useful for removing texts from these maps. Advisors/Committee Members: Španěl, Michal (advisor), Šilhavá, Jana (referee).

Subjects/Keywords: historické mapy; druhé vojenské mapování; digitalizace; AdaBoost; historical maps; the second historical military mapping; digitize; AdaBoost

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Pospíšil, J. (2019). Digitalizace historických map: Ancient Maps Digitizing. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/53177

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Pospíšil, Josef. “Digitalizace historických map: Ancient Maps Digitizing.” 2019. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/53177.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Pospíšil, Josef. “Digitalizace historických map: Ancient Maps Digitizing.” 2019. Web. 25 Jan 2021.

Vancouver:

Pospíšil J. Digitalizace historických map: Ancient Maps Digitizing. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/53177.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pospíšil J. Digitalizace historických map: Ancient Maps Digitizing. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/53177

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

18. Juránek, Roman. Rozpoznání vzorů v obraze pomocí klasifikátorů: Pattern Recognition in Image Using Classifiers.

Degree: 2020, Brno University of Technology

URL: http://hdl.handle.net/11012/187992

► An AdaBoost algorithm for construction of strong classifier from several weak hypotesis will be presented in this work. Theoretical background of the algorithm and the… (more)

Subjects/Keywords: Rozpoznávání vzorů; AdaBoost; WaldBoost; Klasifikace; Detekce; Obrazové Příznaky; Pattern recognition; AdaBoost; WaldBoost; Classification; Detection; Image Features

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Juránek, R. (2020). Rozpoznání vzorů v obraze pomocí klasifikátorů: Pattern Recognition in Image Using Classifiers. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/187992

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Juránek, Roman. “Rozpoznání vzorů v obraze pomocí klasifikátorů: Pattern Recognition in Image Using Classifiers.” 2020. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/187992.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Juránek, Roman. “Rozpoznání vzorů v obraze pomocí klasifikátorů: Pattern Recognition in Image Using Classifiers.” 2020. Web. 25 Jan 2021.

Vancouver:

Juránek R. Rozpoznání vzorů v obraze pomocí klasifikátorů: Pattern Recognition in Image Using Classifiers. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/187992.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Juránek R. Rozpoznání vzorů v obraze pomocí klasifikátorů: Pattern Recognition in Image Using Classifiers. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/187992

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

19. Juránek, Roman. Rozpoznání vzorů v obraze pomocí klasifikátorů: Pattern Recognition in Image Using Classifiers.

Degree: 2020, Brno University of Technology

URL: http://hdl.handle.net/11012/53944

► An AdaBoost algorithm for construction of strong classifier from several weak hypotesis will be presented in this work. Theoretical background of the algorithm and the… (more)

Subjects/Keywords: Rozpoznávání vzorů; AdaBoost; WaldBoost; Klasifikace; Detekce; Obrazové Příznaky; Pattern recognition; AdaBoost; WaldBoost; Classification; Detection; Image Features

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Juránek, R. (2020). Rozpoznání vzorů v obraze pomocí klasifikátorů: Pattern Recognition in Image Using Classifiers. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/53944

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Juránek, Roman. “Rozpoznání vzorů v obraze pomocí klasifikátorů: Pattern Recognition in Image Using Classifiers.” 2020. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/53944.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Juránek, Roman. “Rozpoznání vzorů v obraze pomocí klasifikátorů: Pattern Recognition in Image Using Classifiers.” 2020. Web. 25 Jan 2021.

Vancouver:

Juránek R. Rozpoznání vzorů v obraze pomocí klasifikátorů: Pattern Recognition in Image Using Classifiers. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/53944.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Juránek R. Rozpoznání vzorů v obraze pomocí klasifikátorů: Pattern Recognition in Image Using Classifiers. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/53944

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

20. Mrnuštík, Michal. Boosting a evoluční algoritmy: Boosting and Evolution.

Degree: 2020, Brno University of Technology

URL: http://hdl.handle.net/11012/188193

► This thesis introduces combination of the AdaBoost and the evolutionary algorithm. The evolutionary algorithm is used to find linear combination of Haar features. This linear… (more)

Subjects/Keywords: boosting; adaboost; evoluční algoritmy; rozpoznávání vzorů; haarovy příznaky; boosting; adaboost; evolutionary algorithms; pattern recognition; haar features

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Mrnuštík, M. (2020). Boosting a evoluční algoritmy: Boosting and Evolution. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/188193

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Mrnuštík, Michal. “Boosting a evoluční algoritmy: Boosting and Evolution.” 2020. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/188193.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Mrnuštík, Michal. “Boosting a evoluční algoritmy: Boosting and Evolution.” 2020. Web. 25 Jan 2021.

Vancouver:

Mrnuštík M. Boosting a evoluční algoritmy: Boosting and Evolution. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/188193.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mrnuštík M. Boosting a evoluční algoritmy: Boosting and Evolution. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/188193

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

21. Chmiel, Filip. Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position.

Degree: 2020, Brno University of Technology

URL: http://hdl.handle.net/11012/188257

► This paper presents the method for a head pose and orientation detection based on computer vision techniques. The method is based on the AdaBoost approach… (more)

Subjects/Keywords: uživatelská rozhraní; detekce obličeje; počítačové vidění; AdaBoost; optický tok; user interface; face detection; computer vision; AdaBoost; optical flow

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chmiel, F. (2020). Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/188257

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chmiel, Filip. “Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position.” 2020. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/188257.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chmiel, Filip. “Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position.” 2020. Web. 25 Jan 2021.

Vancouver:

Chmiel F. Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/188257.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chmiel F. Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/188257

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

22. Králík, Martin. Detekce objektů v obraze s pomocí rozšířené sady Haarových příznaků a histogramu: Object detection in images using extended set of Haar-like features and histogram-based method.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/9497

► This diploma thesis is focused on detection in images using extended set of Haar-like features and histogram-based method. At first is introduced a basic concept… (more)

Subjects/Keywords: Adaboost; počítačové vidění; Diffusion distance; příznaky; histogramy; chodci; tepny; Adaboost; computer vision; Diffusion distance; features; histogram; pedestrians; artery

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APA (6^th Edition):

Králík, M. (2019). Detekce objektů v obraze s pomocí rozšířené sady Haarových příznaků a histogramu: Object detection in images using extended set of Haar-like features and histogram-based method. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/9497

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Králík, Martin. “Detekce objektů v obraze s pomocí rozšířené sady Haarových příznaků a histogramu: Object detection in images using extended set of Haar-like features and histogram-based method.” 2019. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/9497.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Králík, Martin. “Detekce objektů v obraze s pomocí rozšířené sady Haarových příznaků a histogramu: Object detection in images using extended set of Haar-like features and histogram-based method.” 2019. Web. 25 Jan 2021.

Vancouver:

Králík M. Detekce objektů v obraze s pomocí rozšířené sady Haarových příznaků a histogramu: Object detection in images using extended set of Haar-like features and histogram-based method. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/9497.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Králík M. Detekce objektů v obraze s pomocí rozšířené sady Haarových příznaků a histogramu: Object detection in images using extended set of Haar-like features and histogram-based method. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/9497

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

23. Wiewiorka, Petr. Ovládání počítače gesty: Video-Based Human-Computer Interface.

Degree: 2016, Brno University of Technology

URL: http://hdl.handle.net/11012/55441

► This bachelor's thesis descripts a way of controlling a computer by hand gestures. Picture is taken from web camera, in which search for a position… (more)

Subjects/Keywords: Zpracování obrazu; ovládání počítače gesty; adaboost; haartraining; OpenCV; Image processing; gesture computer controlling; adaboost; haartraining; OpenCV

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APA (6^th Edition):

Wiewiorka, P. (2016). Ovládání počítače gesty: Video-Based Human-Computer Interface. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/55441

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wiewiorka, Petr. “Ovládání počítače gesty: Video-Based Human-Computer Interface.” 2016. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/55441.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wiewiorka, Petr. “Ovládání počítače gesty: Video-Based Human-Computer Interface.” 2016. Web. 25 Jan 2021.

Vancouver:

Wiewiorka P. Ovládání počítače gesty: Video-Based Human-Computer Interface. [Internet] [Thesis]. Brno University of Technology; 2016. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/55441.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wiewiorka P. Ovládání počítače gesty: Video-Based Human-Computer Interface. [Thesis]. Brno University of Technology; 2016. Available from: http://hdl.handle.net/11012/55441

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

24. Vykoupil, Pavel. Inteligentní klasifikace příznaků pro podporu diagnostiky glaukomu: Intelligent features classification aimed to support diagnosis of glaucoma.

Degree: 2018, Brno University of Technology

URL: http://hdl.handle.net/11012/12401

► This bachelor thesis deals with inteligent features classification aimed to support diagnosis of glaucoma. First part focuses on eye anatomy and disease called glaucoma. In… (more)

Subjects/Keywords: klasifikace; příznaky; diagnostika; glaukom; texturní analýza; neuronové sítě; HoKashyap; AdaBoost.; classification; attributes; diagnostics; glaucoma; texture analysis; neural networks; HoKashyap; AdaBoost.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Vykoupil, P. (2018). Inteligentní klasifikace příznaků pro podporu diagnostiky glaukomu: Intelligent features classification aimed to support diagnosis of glaucoma. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/12401

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Vykoupil, Pavel. “Inteligentní klasifikace příznaků pro podporu diagnostiky glaukomu: Intelligent features classification aimed to support diagnosis of glaucoma.” 2018. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/12401.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Vykoupil, Pavel. “Inteligentní klasifikace příznaků pro podporu diagnostiky glaukomu: Intelligent features classification aimed to support diagnosis of glaucoma.” 2018. Web. 25 Jan 2021.

Vancouver:

Vykoupil P. Inteligentní klasifikace příznaků pro podporu diagnostiky glaukomu: Intelligent features classification aimed to support diagnosis of glaucoma. [Internet] [Thesis]. Brno University of Technology; 2018. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/12401.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vykoupil P. Inteligentní klasifikace příznaků pro podporu diagnostiky glaukomu: Intelligent features classification aimed to support diagnosis of glaucoma. [Thesis]. Brno University of Technology; 2018. Available from: http://hdl.handle.net/11012/12401

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

25. Mrnuštík, Michal. Boosting a evoluční algoritmy: Boosting and Evolution.

Degree: 2020, Brno University of Technology

URL: http://hdl.handle.net/11012/55453

► This thesis introduces combination of the AdaBoost and the evolutionary algorithm. The evolutionary algorithm is used to find linear combination of Haar features. This linear… (more)

Subjects/Keywords: boosting; adaboost; evoluční algoritmy; rozpoznávání vzorů; haarovy příznaky; boosting; adaboost; evolutionary algorithms; pattern recognition; haar features

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Mrnuštík, M. (2020). Boosting a evoluční algoritmy: Boosting and Evolution. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/55453

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Mrnuštík, Michal. “Boosting a evoluční algoritmy: Boosting and Evolution.” 2020. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/55453.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Mrnuštík, Michal. “Boosting a evoluční algoritmy: Boosting and Evolution.” 2020. Web. 25 Jan 2021.

Vancouver:

Mrnuštík M. Boosting a evoluční algoritmy: Boosting and Evolution. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/55453.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mrnuštík M. Boosting a evoluční algoritmy: Boosting and Evolution. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/55453

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

26. Chmiel, Filip. Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position.

Degree: 2020, Brno University of Technology

URL: http://hdl.handle.net/11012/55810

► This paper presents the method for a head pose and orientation detection based on computer vision techniques. The method is based on the AdaBoost approach… (more)

Subjects/Keywords: uživatelská rozhraní; detekce obličeje; počítačové vidění; AdaBoost; optický tok; user interface; face detection; computer vision; AdaBoost; optical flow

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chmiel, F. (2020). Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/55810

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chmiel, Filip. “Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position.” 2020. Thesis, Brno University of Technology. Accessed January 25, 2021. http://hdl.handle.net/11012/55810.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chmiel, Filip. “Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position.” 2020. Web. 25 Jan 2021.

Vancouver:

Chmiel F. Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Jan 25]. Available from: http://hdl.handle.net/11012/55810.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chmiel F. Rozhraní počítače využívající polohu hlavy uživatele: Computer Interface Based on User's Head Position. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/55810

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

27. Ciou, Yue-Sheng. Object Recognition System Design in Regions of Interest Based on AdaBoost Algorithm.

Degree: Master, Mechanical and Electro-Mechanical Engineering, 2015, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0719114-230542

► In recent years, vehicle safety has become an important issue in modern automotive technology. This research proposes an object recognition system based on AdaBoost algorithm,… (more)

Subjects/Keywords: neural network; image processing; AdaBoost; OpenCV; system integration

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APA (6^th Edition):

Ciou, Y. (2015). Object Recognition System Design in Regions of Interest Based on AdaBoost Algorithm. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0719114-230542

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ciou, Yue-Sheng. “Object Recognition System Design in Regions of Interest Based on AdaBoost Algorithm.” 2015. Thesis, NSYSU. Accessed January 25, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0719114-230542.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ciou, Yue-Sheng. “Object Recognition System Design in Regions of Interest Based on AdaBoost Algorithm.” 2015. Web. 25 Jan 2021.

Vancouver:

Ciou Y. Object Recognition System Design in Regions of Interest Based on AdaBoost Algorithm. [Internet] [Thesis]. NSYSU; 2015. [cited 2021 Jan 25]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0719114-230542.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ciou Y. Object Recognition System Design in Regions of Interest Based on AdaBoost Algorithm. [Thesis]. NSYSU; 2015. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0719114-230542

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

28. Lin, Hung-shyuan. Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning.

Degree: Master, Electrical Engineering, 2015, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021

► Itâs a study on Reinforcement Learning, learning interaction of agents and dynamic environment to get reward function R, and update the policy, converge learning and… (more)

Subjects/Keywords: Inverse reinforcement learning; Reward function; Fuzzy; Reinforcement learning; AdaBoost; Apprenticeship learning

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APA (6^th Edition):

Lin, H. (2015). Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lin, Hung-shyuan. “Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning.” 2015. Thesis, NSYSU. Accessed January 25, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lin, Hung-shyuan. “Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning.” 2015. Web. 25 Jan 2021.

Vancouver:

Lin H. Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning. [Internet] [Thesis]. NSYSU; 2015. [cited 2021 Jan 25]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lin H. Applying The Concept of Fuzzy Logic to Inverse Reinforcement Learning. [Thesis]. NSYSU; 2015. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1025115-185021

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Tampere University

29. PAUKKERI, RAUNO. Tilastolliset oppimisyhdistelmät asiakasvaihtuvuuden ennustamisessa .

Degree: 2013, Tampere University

URL: https://trepo.tuni.fi/handle/10024/94855

► Tämän tutkielman tarkoituksena on selvittää ja vertailla erilaisia tilastollisia oppimisyhdistelmiä erään suomalaisen teleoperaattorin asiakasvaihtuvuuden ennustamisessa. Tutkielmassa tarkastellaan myös, onko opetusaineiston painottamisella vaikutusta asiakasvaihtuvuuden ennustamisen tarkkuuteen.… (more)

Subjects/Keywords: Bagging; Boosting; Real AdaBoost; Gradient Boosting; satunnaismetsä; päätöspuut

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APA (6^th Edition):

PAUKKERI, R. (2013). Tilastolliset oppimisyhdistelmät asiakasvaihtuvuuden ennustamisessa . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/94855

Chicago Manual of Style (16^th Edition):

PAUKKERI, RAUNO. “Tilastolliset oppimisyhdistelmät asiakasvaihtuvuuden ennustamisessa .” 2013. Masters Thesis, Tampere University. Accessed January 25, 2021. https://trepo.tuni.fi/handle/10024/94855.

MLA Handbook (7^th Edition):

PAUKKERI, RAUNO. “Tilastolliset oppimisyhdistelmät asiakasvaihtuvuuden ennustamisessa .” 2013. Web. 25 Jan 2021.

Vancouver:

PAUKKERI R. Tilastolliset oppimisyhdistelmät asiakasvaihtuvuuden ennustamisessa . [Internet] [Masters thesis]. Tampere University; 2013. [cited 2021 Jan 25]. Available from: https://trepo.tuni.fi/handle/10024/94855.

Council of Science Editors:

PAUKKERI R. Tilastolliset oppimisyhdistelmät asiakasvaihtuvuuden ennustamisessa . [Masters Thesis]. Tampere University; 2013. Available from: https://trepo.tuni.fi/handle/10024/94855

Uppsala University

30. Virkkala, Linda. Modelling of patterns between operational data, diagnostic trouble codes and workshop history using big data and machine learning.

Degree: Computing Science, 2016, Uppsala University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-279823

► The work presented in this thesis is part of a large research and development project on condition-based maintenance for heavy trucks and buses at… (more)

▼ The work presented in this thesis is part of a large research and development project on condition-based maintenance for heavy trucks and buses at Scania. The aim of this thesis was to be able to predict the status of a component (the starter motor) using data mining methods and to create models that can predict the failure of that component. Based on workshop history data, error codes and operational data, three sets of classification models were built and evaluated. The first model aims to find patterns in a set of error codes, to see which codes are related to a starter motor failure. The second model aims to see if there are patterns in operational data that lead to the occurrence of an error code. Finally, the two data sets were merged and a classifier was trained and evaluated on this larger data set. Two machine learning algorithms were used and compared throughout the model building: AdaBoost and random forest. There is no statistically significant difference in their performance, and both algorithms had an error rate around ~13%, ~5% and ~13% for the three classification models respectively. However, random forest is much faster, and is therefore the preferable option for an industrial implementation. Variable analysis was conducted for the error codes and operational data, resulting in rankings of informative variables. From the evaluation metric precision, it can be derived that if our random forest model predicts a starter motor failure, there is a 85.7% chance that it actually has failed. This model finds 32% (the models recall) of the failed starter motors. It is also shown that four error codes; 2481, 2639, 2657 and 2597 have the highest predictive power for starter motor failure classification. For the operational data, variables that concern the starter motor lifetime and battery health are generally ranked as important by the models. The random forest model finds 81.9% of the cases where the 2481 error code occurs. If the random forest model predicts that the error code 2481 will occur, there is a 88.2% chance that it will. The classification performance was not increased when the two data sets were merged, indicating that the patterns detected by the two first classification models do not add value toone another.

Subjects/Keywords: Data mining; random forest; adaboost; error codes; Computer Sciences; Datavetenskap (datalogi)

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APA (6^th Edition):

Virkkala, L. (2016). Modelling of patterns between operational data, diagnostic trouble codes and workshop history using big data and machine learning. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-279823

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Virkkala, Linda. “Modelling of patterns between operational data, diagnostic trouble codes and workshop history using big data and machine learning.” 2016. Thesis, Uppsala University. Accessed January 25, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-279823.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Virkkala, Linda. “Modelling of patterns between operational data, diagnostic trouble codes and workshop history using big data and machine learning.” 2016. Web. 25 Jan 2021.

Vancouver:

Virkkala L. Modelling of patterns between operational data, diagnostic trouble codes and workshop history using big data and machine learning. [Internet] [Thesis]. Uppsala University; 2016. [cited 2021 Jan 25]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-279823.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Virkkala L. Modelling of patterns between operational data, diagnostic trouble codes and workshop history using big data and machine learning. [Thesis]. Uppsala University; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-279823

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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Open Access Theses and Dissertations