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You searched for subject:(instance segmentation). Showing records 1 – 18 of 18 total matches.

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University of Ottawa

1. Kolhatkar, Dhanvin. Real-Time Instance and Semantic Segmentation Using Deep Learning .

Degree: 2020, University of Ottawa

 In this thesis, we explore the use of Convolutional Neural Networks for semantic and instance segmentation, with a focus on studying the application of existing… (more)

Subjects/Keywords: Instance segmentation; Semantic segmentation; Deep learning; Real-time; Mask prediction

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

APA (6th Edition):

Kolhatkar, D. (2020). Real-Time Instance and Semantic Segmentation Using Deep Learning . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/40616

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

Chicago Manual of Style (16th Edition):

Kolhatkar, Dhanvin. “Real-Time Instance and Semantic Segmentation Using Deep Learning .” 2020. Thesis, University of Ottawa. Accessed January 19, 2021. http://hdl.handle.net/10393/40616.

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

MLA Handbook (7th Edition):

Kolhatkar, Dhanvin. “Real-Time Instance and Semantic Segmentation Using Deep Learning .” 2020. Web. 19 Jan 2021.

Vancouver:

Kolhatkar D. Real-Time Instance and Semantic Segmentation Using Deep Learning . [Internet] [Thesis]. University of Ottawa; 2020. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/10393/40616.

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

Council of Science Editors:

Kolhatkar D. Real-Time Instance and Semantic Segmentation Using Deep Learning . [Thesis]. University of Ottawa; 2020. Available from: http://hdl.handle.net/10393/40616

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

2. Seguin, Guillaume. Analyse des personnes dans les films stéréoscopiques : Person analysis in stereoscopic movies.

Degree: Docteur es, Informatique, 2016, Paris Sciences et Lettres (ComUE)

Les humains sont au coeur de nombreux problèmes de vision par ordinateur, tels que les systèmes de surveillance ou les voitures sans pilote. Ils sont… (more)

Subjects/Keywords: Vision par ordinateur; Films 3D; Détection de personne; Estimation de pose; Segmentation vidéo; Segmentation multi-instance; Computer vision; 3D movies; Person detection; Pose estimation; Video segmentation; Instance-level segmentation; 004

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

APA (6th Edition):

Seguin, G. (2016). Analyse des personnes dans les films stéréoscopiques : Person analysis in stereoscopic movies. (Doctoral Dissertation). Paris Sciences et Lettres (ComUE). Retrieved from http://www.theses.fr/2016PSLEE021

Chicago Manual of Style (16th Edition):

Seguin, Guillaume. “Analyse des personnes dans les films stéréoscopiques : Person analysis in stereoscopic movies.” 2016. Doctoral Dissertation, Paris Sciences et Lettres (ComUE). Accessed January 19, 2021. http://www.theses.fr/2016PSLEE021.

MLA Handbook (7th Edition):

Seguin, Guillaume. “Analyse des personnes dans les films stéréoscopiques : Person analysis in stereoscopic movies.” 2016. Web. 19 Jan 2021.

Vancouver:

Seguin G. Analyse des personnes dans les films stéréoscopiques : Person analysis in stereoscopic movies. [Internet] [Doctoral dissertation]. Paris Sciences et Lettres (ComUE); 2016. [cited 2021 Jan 19]. Available from: http://www.theses.fr/2016PSLEE021.

Council of Science Editors:

Seguin G. Analyse des personnes dans les films stéréoscopiques : Person analysis in stereoscopic movies. [Doctoral Dissertation]. Paris Sciences et Lettres (ComUE); 2016. Available from: http://www.theses.fr/2016PSLEE021


University of Cambridge

3. Agapaki, Evangelia. Automated Object Segmentation in Existing Industrial Facilities.

Degree: PhD, 2020, University of Cambridge

 Shape segmentation from point cloud data is a core step of the digital twinning process for industrial facilities. However, this process is labour-intensive with 90%… (more)

Subjects/Keywords: Digital Twin; Industrial Factory; Point Cloud Data; Deep Learning; Class Segmentation; Instance Segmentation

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APA (6th Edition):

Agapaki, E. (2020). Automated Object Segmentation in Existing Industrial Facilities. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/305021

Chicago Manual of Style (16th Edition):

Agapaki, Evangelia. “Automated Object Segmentation in Existing Industrial Facilities.” 2020. Doctoral Dissertation, University of Cambridge. Accessed January 19, 2021. https://www.repository.cam.ac.uk/handle/1810/305021.

MLA Handbook (7th Edition):

Agapaki, Evangelia. “Automated Object Segmentation in Existing Industrial Facilities.” 2020. Web. 19 Jan 2021.

Vancouver:

Agapaki E. Automated Object Segmentation in Existing Industrial Facilities. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Jan 19]. Available from: https://www.repository.cam.ac.uk/handle/1810/305021.

Council of Science Editors:

Agapaki E. Automated Object Segmentation in Existing Industrial Facilities. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://www.repository.cam.ac.uk/handle/1810/305021


Delft University of Technology

4. Wang, Ziqi (author). Depth-aware Instance Segmentation with a Discriminative Loss Function.

Degree: 2018, Delft University of Technology

This work explores the possibility of incorporating depth information into a deep neural network to improve accuracy of RGB instance segmentation. The baseline of this… (more)

Subjects/Keywords: Deep Learning; Computer Vision; instance segmentation; Intelligent Vehicles

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APA (6th Edition):

Wang, Z. (. (2018). Depth-aware Instance Segmentation with a Discriminative Loss Function. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:02bd3582-3304-4595-baa6-c6fcca755418

Chicago Manual of Style (16th Edition):

Wang, Ziqi (author). “Depth-aware Instance Segmentation with a Discriminative Loss Function.” 2018. Masters Thesis, Delft University of Technology. Accessed January 19, 2021. http://resolver.tudelft.nl/uuid:02bd3582-3304-4595-baa6-c6fcca755418.

MLA Handbook (7th Edition):

Wang, Ziqi (author). “Depth-aware Instance Segmentation with a Discriminative Loss Function.” 2018. Web. 19 Jan 2021.

Vancouver:

Wang Z(. Depth-aware Instance Segmentation with a Discriminative Loss Function. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 19]. Available from: http://resolver.tudelft.nl/uuid:02bd3582-3304-4595-baa6-c6fcca755418.

Council of Science Editors:

Wang Z(. Depth-aware Instance Segmentation with a Discriminative Loss Function. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:02bd3582-3304-4595-baa6-c6fcca755418


Carnegie Mellon University

5. Le, Ngan Thi Hoang. Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling.

Degree: 2018, Carnegie Mellon University

 Semantic labeling is becoming more and more popular among researchers in computer vision and machine learning. Many applications, such as autonomous driving, tracking, indoor navigation,… (more)

Subjects/Keywords: Gated Recurrent Unit; Level Set; Recurrent Neural Networks; Residual Network; Scene Labeling; Semantic Instance Segmentation

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APA (6th Edition):

Le, N. T. H. (2018). Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/1166

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

Chicago Manual of Style (16th Edition):

Le, Ngan Thi Hoang. “Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling.” 2018. Thesis, Carnegie Mellon University. Accessed January 19, 2021. http://repository.cmu.edu/dissertations/1166.

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

MLA Handbook (7th Edition):

Le, Ngan Thi Hoang. “Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling.” 2018. Web. 19 Jan 2021.

Vancouver:

Le NTH. Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling. [Internet] [Thesis]. Carnegie Mellon University; 2018. [cited 2021 Jan 19]. Available from: http://repository.cmu.edu/dissertations/1166.

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

Council of Science Editors:

Le NTH. Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling. [Thesis]. Carnegie Mellon University; 2018. Available from: http://repository.cmu.edu/dissertations/1166

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


Delft University of Technology

6. van Hilten, Arno (author). Segmenting and Detecting Carotid Plaque Components in MRI.

Degree: 2018, Delft University of Technology

Cardiovascular diseases and stroke are currently the leading causes of death worldwide. Atherosclerotic plaque is a mostly asymptotic vascular disease, but rupture of an atherosclerotic… (more)

Subjects/Keywords: Machine Learning; Deep Learning; Multiple Instance Learning; Segmentation; Detection; Plaque Components; Carotid Artery; MRI

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APA (6th Edition):

van Hilten, A. (. (2018). Segmenting and Detecting Carotid Plaque Components in MRI. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9bce7f8a-8d69-4b48-98c4-fc4e6600b63d

Chicago Manual of Style (16th Edition):

van Hilten, Arno (author). “Segmenting and Detecting Carotid Plaque Components in MRI.” 2018. Masters Thesis, Delft University of Technology. Accessed January 19, 2021. http://resolver.tudelft.nl/uuid:9bce7f8a-8d69-4b48-98c4-fc4e6600b63d.

MLA Handbook (7th Edition):

van Hilten, Arno (author). “Segmenting and Detecting Carotid Plaque Components in MRI.” 2018. Web. 19 Jan 2021.

Vancouver:

van Hilten A(. Segmenting and Detecting Carotid Plaque Components in MRI. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 19]. Available from: http://resolver.tudelft.nl/uuid:9bce7f8a-8d69-4b48-98c4-fc4e6600b63d.

Council of Science Editors:

van Hilten A(. Segmenting and Detecting Carotid Plaque Components in MRI. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:9bce7f8a-8d69-4b48-98c4-fc4e6600b63d


Australian National University

7. Zhang, Haoyang. Learning to Generate and Refine Object Proposals .

Degree: 2018, Australian National University

 Visual object recognition is a fundamental and challenging problem in computer vision. To build a practical recognition system, one is first confronted with high computation… (more)

Subjects/Keywords: object proposal; object candidate; object detection; object instance segmentation; convolutional neural network (CNN)

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APA (6th Edition):

Zhang, H. (2018). Learning to Generate and Refine Object Proposals . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/143520

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

Chicago Manual of Style (16th Edition):

Zhang, Haoyang. “Learning to Generate and Refine Object Proposals .” 2018. Thesis, Australian National University. Accessed January 19, 2021. http://hdl.handle.net/1885/143520.

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

MLA Handbook (7th Edition):

Zhang, Haoyang. “Learning to Generate and Refine Object Proposals .” 2018. Web. 19 Jan 2021.

Vancouver:

Zhang H. Learning to Generate and Refine Object Proposals . [Internet] [Thesis]. Australian National University; 2018. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/1885/143520.

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

Council of Science Editors:

Zhang H. Learning to Generate and Refine Object Proposals . [Thesis]. Australian National University; 2018. Available from: http://hdl.handle.net/1885/143520

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


Australian National University

8. Hayder, Zeeshan. Deep Structured Models for Large Scale Object Co-detection and Segmentation .

Degree: 2017, Australian National University

 Structured decisions are often required for a large variety of image and scene understanding tasks in computer vision, with few of them being object detection,… (more)

Subjects/Keywords: Deep structured models; Context modeling; Object (co-)detection; Instance-level semantic segmentation

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

APA (6th Edition):

Hayder, Z. (2017). Deep Structured Models for Large Scale Object Co-detection and Segmentation . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/142816

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

Chicago Manual of Style (16th Edition):

Hayder, Zeeshan. “Deep Structured Models for Large Scale Object Co-detection and Segmentation .” 2017. Thesis, Australian National University. Accessed January 19, 2021. http://hdl.handle.net/1885/142816.

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

MLA Handbook (7th Edition):

Hayder, Zeeshan. “Deep Structured Models for Large Scale Object Co-detection and Segmentation .” 2017. Web. 19 Jan 2021.

Vancouver:

Hayder Z. Deep Structured Models for Large Scale Object Co-detection and Segmentation . [Internet] [Thesis]. Australian National University; 2017. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/1885/142816.

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

Council of Science Editors:

Hayder Z. Deep Structured Models for Large Scale Object Co-detection and Segmentation . [Thesis]. Australian National University; 2017. Available from: http://hdl.handle.net/1885/142816

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

9. Wang, Qiong. Salient object detection and segmentation in videos : Détection d'objets saillants et segmentation dans des vidéos.

Degree: Docteur es, Signal, Image, Vision, 2019, Rennes, INSA

Cette thèse est centrée sur le problème de la détection d'objets saillants et de leur segmentation dans une vidéo en vue de détecter les objets… (more)

Subjects/Keywords: Vidéo; Détection d'objet saillant; Segmentation d'instance d'objet; Apprentissage en profondeur; Video; Salient object detection; Object instance segmentation; Deep-learning; 006.7

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APA (6th Edition):

Wang, Q. (2019). Salient object detection and segmentation in videos : Détection d'objets saillants et segmentation dans des vidéos. (Doctoral Dissertation). Rennes, INSA. Retrieved from http://www.theses.fr/2019ISAR0003

Chicago Manual of Style (16th Edition):

Wang, Qiong. “Salient object detection and segmentation in videos : Détection d'objets saillants et segmentation dans des vidéos.” 2019. Doctoral Dissertation, Rennes, INSA. Accessed January 19, 2021. http://www.theses.fr/2019ISAR0003.

MLA Handbook (7th Edition):

Wang, Qiong. “Salient object detection and segmentation in videos : Détection d'objets saillants et segmentation dans des vidéos.” 2019. Web. 19 Jan 2021.

Vancouver:

Wang Q. Salient object detection and segmentation in videos : Détection d'objets saillants et segmentation dans des vidéos. [Internet] [Doctoral dissertation]. Rennes, INSA; 2019. [cited 2021 Jan 19]. Available from: http://www.theses.fr/2019ISAR0003.

Council of Science Editors:

Wang Q. Salient object detection and segmentation in videos : Détection d'objets saillants et segmentation dans des vidéos. [Doctoral Dissertation]. Rennes, INSA; 2019. Available from: http://www.theses.fr/2019ISAR0003

10. Luc, Pauline. Apprentissage autosupervisé de modèles prédictifs de segmentation à partir de vidéos : Self-supervised learning of predictive segmentation models from video.

Degree: Docteur es, Mathématiques et informatique, 2019, Université Grenoble Alpes (ComUE)

Les modèles prédictifs ont le potentiel de permettre le transfert des succès récents en apprentissage par renforcement à de nombreuses tâches du monde réel, en… (more)

Subjects/Keywords: Apprentissage profond; Segmentation sémantique; Segmentation d’instance; Modèles génératifs; Apprentissage prédictif; Compréhension vidéo; Deep learning; Semantic segmentation; Instance segmentation; Generative modeling; Predictive learning; Video understanding; 004; 510

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APA (6th Edition):

Luc, P. (2019). Apprentissage autosupervisé de modèles prédictifs de segmentation à partir de vidéos : Self-supervised learning of predictive segmentation models from video. (Doctoral Dissertation). Université Grenoble Alpes (ComUE). Retrieved from http://www.theses.fr/2019GREAM024

Chicago Manual of Style (16th Edition):

Luc, Pauline. “Apprentissage autosupervisé de modèles prédictifs de segmentation à partir de vidéos : Self-supervised learning of predictive segmentation models from video.” 2019. Doctoral Dissertation, Université Grenoble Alpes (ComUE). Accessed January 19, 2021. http://www.theses.fr/2019GREAM024.

MLA Handbook (7th Edition):

Luc, Pauline. “Apprentissage autosupervisé de modèles prédictifs de segmentation à partir de vidéos : Self-supervised learning of predictive segmentation models from video.” 2019. Web. 19 Jan 2021.

Vancouver:

Luc P. Apprentissage autosupervisé de modèles prédictifs de segmentation à partir de vidéos : Self-supervised learning of predictive segmentation models from video. [Internet] [Doctoral dissertation]. Université Grenoble Alpes (ComUE); 2019. [cited 2021 Jan 19]. Available from: http://www.theses.fr/2019GREAM024.

Council of Science Editors:

Luc P. Apprentissage autosupervisé de modèles prédictifs de segmentation à partir de vidéos : Self-supervised learning of predictive segmentation models from video. [Doctoral Dissertation]. Université Grenoble Alpes (ComUE); 2019. Available from: http://www.theses.fr/2019GREAM024


Georgia Tech

11. Hsu, Yen-Chang. Learning from pairwise similarity for visual categorization.

Degree: PhD, Electrical and Computer Engineering, 2020, Georgia Tech

 Learning high-capacity machine learning models for perception, especially for high-dimensional inputs such as in computer vision, requires a large amount of human-annotated data. Many efforts… (more)

Subjects/Keywords: Transfer learning; Pairwise similarity; Clustering; Deep learning; Neural networks; Classification; Out-of-distribution detection; Instance segmentation; Lane detection

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APA (6th Edition):

Hsu, Y. (2020). Learning from pairwise similarity for visual categorization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62814

Chicago Manual of Style (16th Edition):

Hsu, Yen-Chang. “Learning from pairwise similarity for visual categorization.” 2020. Doctoral Dissertation, Georgia Tech. Accessed January 19, 2021. http://hdl.handle.net/1853/62814.

MLA Handbook (7th Edition):

Hsu, Yen-Chang. “Learning from pairwise similarity for visual categorization.” 2020. Web. 19 Jan 2021.

Vancouver:

Hsu Y. Learning from pairwise similarity for visual categorization. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/1853/62814.

Council of Science Editors:

Hsu Y. Learning from pairwise similarity for visual categorization. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62814

12. Cheng, Hsien Ting. Unsupervised video segmentation and its application to activity recognition.

Degree: PhD, 1200, 2015, University of Illinois – Urbana-Champaign

 We addressed the fundamental problem of computer vision: segmentation and recognition, in the space-time domain. With the knowledge that generic image segmentation introduces unstable regions… (more)

Subjects/Keywords: segmentation; Video segmentation; Unsupervised clustering; Activity recognition; Multiple instance learning

…variations than encountered in more complex categories. Based on segmentation tree implementation… …8]. Figure 1.2: Illustration steps of a segmentation tree building algorithm. (… …g) Results of multiscale segmentation. (e) Segmentation result for… …photometric scale = 5. All regions are included. (f) Segmentation for = 65. Two regions… …fragment in between the two merging regions is less than 65. (g) Segmentation for = 80… 

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APA (6th Edition):

Cheng, H. T. (2015). Unsupervised video segmentation and its application to activity recognition. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/72891

Chicago Manual of Style (16th Edition):

Cheng, Hsien Ting. “Unsupervised video segmentation and its application to activity recognition.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed January 19, 2021. http://hdl.handle.net/2142/72891.

MLA Handbook (7th Edition):

Cheng, Hsien Ting. “Unsupervised video segmentation and its application to activity recognition.” 2015. Web. 19 Jan 2021.

Vancouver:

Cheng HT. Unsupervised video segmentation and its application to activity recognition. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/2142/72891.

Council of Science Editors:

Cheng HT. Unsupervised video segmentation and its application to activity recognition. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/72891


Linköping University

13. Fritz, Karin. Instance Segmentation of Buildings in Satellite Images.

Degree: Computer Vision, 2020, Linköping University

  When creating a photo realistic 3D model of the world using satellite imagery, image classification is an important part of the process. In this… (more)

Subjects/Keywords: cnn; convolutional neural networks; instance segmentation; semantic segmentation; Signal Processing; Signalbehandling; Computer Vision and Robotics (Autonomous Systems); Datorseende och robotik (autonoma system)

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APA (6th Edition):

Fritz, K. (2020). Instance Segmentation of Buildings in Satellite Images. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-164537

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

Chicago Manual of Style (16th Edition):

Fritz, Karin. “Instance Segmentation of Buildings in Satellite Images.” 2020. Thesis, Linköping University. Accessed January 19, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-164537.

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

MLA Handbook (7th Edition):

Fritz, Karin. “Instance Segmentation of Buildings in Satellite Images.” 2020. Web. 19 Jan 2021.

Vancouver:

Fritz K. Instance Segmentation of Buildings in Satellite Images. [Internet] [Thesis]. Linköping University; 2020. [cited 2021 Jan 19]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-164537.

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

Council of Science Editors:

Fritz K. Instance Segmentation of Buildings in Satellite Images. [Thesis]. Linköping University; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-164537

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

14. Tsogkas, Stavros. Mid-level representations for modeling objects : Représentations de niveau intermédiaire pour la modélisation d'objets.

Degree: Docteur es, Mathématiques et informatique, 2016, Université Paris-Saclay (ComUE)

Dans cette thèse, nous proposons l'utilisation de représentations de niveau intermédiaire, et en particulier i) d'axes médians, ii) de parties d'objets, et iii) des caractéristiques… (more)

Subjects/Keywords: Axes médians; Parties d' objets; Réseaux de neurones convolutionnels; Modèles de parties déformables; Segmentation semantique; Multiple instance learning; Medial axis; Object parts; Convolutional Neural Networks; Deformable part models; Semantic segmentation; Multiple instance learning

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APA (6th Edition):

Tsogkas, S. (2016). Mid-level representations for modeling objects : Représentations de niveau intermédiaire pour la modélisation d'objets. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2016SACLC012

Chicago Manual of Style (16th Edition):

Tsogkas, Stavros. “Mid-level representations for modeling objects : Représentations de niveau intermédiaire pour la modélisation d'objets.” 2016. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed January 19, 2021. http://www.theses.fr/2016SACLC012.

MLA Handbook (7th Edition):

Tsogkas, Stavros. “Mid-level representations for modeling objects : Représentations de niveau intermédiaire pour la modélisation d'objets.” 2016. Web. 19 Jan 2021.

Vancouver:

Tsogkas S. Mid-level representations for modeling objects : Représentations de niveau intermédiaire pour la modélisation d'objets. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2016. [cited 2021 Jan 19]. Available from: http://www.theses.fr/2016SACLC012.

Council of Science Editors:

Tsogkas S. Mid-level representations for modeling objects : Représentations de niveau intermédiaire pour la modélisation d'objets. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2016. Available from: http://www.theses.fr/2016SACLC012

15. Grard, Matthieu. Generic instance segmentation for object-oriented bin-picking : Segmentation en instances génériques pour le dévracage orienté objet.

Degree: Docteur es, Informatique, 2019, Lyon

 Le dévracage robotisé est une tâche industrielle en forte croissance visant à automatiser le déchargement par unité d’une pile d’instances d'objet en vrac pour faciliter… (more)

Subjects/Keywords: Vision par ordinateur; Dévracage robotisé; Apprentissage profond; Segmentation en instances; Détection des occultations; Réseaux entièrement convolutionnels; Données d'apprentissage synthétiques; Computer vision; Robotic bin-picking; Deep learning; Instance segmentation; Occlusion detection; Fully convolutional networks; Synthetic training data

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

APA (6th Edition):

Grard, M. (2019). Generic instance segmentation for object-oriented bin-picking : Segmentation en instances génériques pour le dévracage orienté objet. (Doctoral Dissertation). Lyon. Retrieved from http://www.theses.fr/2019LYSEC015

Chicago Manual of Style (16th Edition):

Grard, Matthieu. “Generic instance segmentation for object-oriented bin-picking : Segmentation en instances génériques pour le dévracage orienté objet.” 2019. Doctoral Dissertation, Lyon. Accessed January 19, 2021. http://www.theses.fr/2019LYSEC015.

MLA Handbook (7th Edition):

Grard, Matthieu. “Generic instance segmentation for object-oriented bin-picking : Segmentation en instances génériques pour le dévracage orienté objet.” 2019. Web. 19 Jan 2021.

Vancouver:

Grard M. Generic instance segmentation for object-oriented bin-picking : Segmentation en instances génériques pour le dévracage orienté objet. [Internet] [Doctoral dissertation]. Lyon; 2019. [cited 2021 Jan 19]. Available from: http://www.theses.fr/2019LYSEC015.

Council of Science Editors:

Grard M. Generic instance segmentation for object-oriented bin-picking : Segmentation en instances génériques pour le dévracage orienté objet. [Doctoral Dissertation]. Lyon; 2019. Available from: http://www.theses.fr/2019LYSEC015


University of Illinois – Urbana-Champaign

16. Akbas, Emre. Generation and analysis of segmentation trees for natural images.

Degree: PhD, 1200, 2011, University of Illinois – Urbana-Champaign

 This dissertation is about extracting as well as making use of the structure and hierarchy present in images. We develop a new low-level, multiscale, hierarchical… (more)

Subjects/Keywords: computer vision; image processing; image segmentation; machine learning; segmentation benchmark; natural image statistics; image classification; scene classification; binocular fusion; region matching; multiple instance learning (mil); mis-boost; pascal voc; Pattern Analysis Statistical Modeling and Computational Learning (PASCAL); Visual Object Classes (VOC)

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

APA (6th Edition):

Akbas, E. (2011). Generation and analysis of segmentation trees for natural images. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/26317

Chicago Manual of Style (16th Edition):

Akbas, Emre. “Generation and analysis of segmentation trees for natural images.” 2011. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed January 19, 2021. http://hdl.handle.net/2142/26317.

MLA Handbook (7th Edition):

Akbas, Emre. “Generation and analysis of segmentation trees for natural images.” 2011. Web. 19 Jan 2021.

Vancouver:

Akbas E. Generation and analysis of segmentation trees for natural images. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/2142/26317.

Council of Science Editors:

Akbas E. Generation and analysis of segmentation trees for natural images. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/26317


Penn State University

17. Zhao, Qi. Mixture Model Learning with Instance-level Constraints.

Degree: 2008, Penn State University

 Machine learning traditionally includes two categories of methods: supervised learning and unsupervised learning. In recent years, one paradigm, semi-supervised learning, has attracted much more interest… (more)

Subjects/Keywords: semi-supervised learning with instance-level const; mixture modeling with constraints; constrained clustering; image segmentation with background information

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

APA (6th Edition):

Zhao, Q. (2008). Mixture Model Learning with Instance-level Constraints. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/6605

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

Chicago Manual of Style (16th Edition):

Zhao, Qi. “Mixture Model Learning with Instance-level Constraints.” 2008. Thesis, Penn State University. Accessed January 19, 2021. https://submit-etda.libraries.psu.edu/catalog/6605.

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

MLA Handbook (7th Edition):

Zhao, Qi. “Mixture Model Learning with Instance-level Constraints.” 2008. Web. 19 Jan 2021.

Vancouver:

Zhao Q. Mixture Model Learning with Instance-level Constraints. [Internet] [Thesis]. Penn State University; 2008. [cited 2021 Jan 19]. Available from: https://submit-etda.libraries.psu.edu/catalog/6605.

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

Council of Science Editors:

Zhao Q. Mixture Model Learning with Instance-level Constraints. [Thesis]. Penn State University; 2008. Available from: https://submit-etda.libraries.psu.edu/catalog/6605

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

18. Zhuo, Wei. 2D+3D Indoor Scene Understanding from a Single Monocular Image .

Degree: 2018, Australian National University

 Scene understanding, as a broad field encompassing many subtopics, has gained great interest in recent years. Among these subtopics, indoor scene understanding, having its own… (more)

Subjects/Keywords: Scene Understanding; Monocular Image Processing; Depth Estimation; 3D Box Proposal; Semantic Labeling; Instance Segmentation; Support Relationship Inference

instance segmentation, semantic labeling and support relationship inference from a monocular… …we tackled its three subtasks of instance segmentation, semantic labeling, and the support… …scene; c) Based on depth estimation, we jointly reason about instance segmentation… …59 5.1 Our scene parsing framework for instance segmentation, semantic labeling and… …Instance Segmentation Our CRF Model for Intergrated Scene Parsing Semantic… 

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

APA (6th Edition):

Zhuo, W. (2018). 2D+3D Indoor Scene Understanding from a Single Monocular Image . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/144616

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

Chicago Manual of Style (16th Edition):

Zhuo, Wei. “2D+3D Indoor Scene Understanding from a Single Monocular Image .” 2018. Thesis, Australian National University. Accessed January 19, 2021. http://hdl.handle.net/1885/144616.

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

MLA Handbook (7th Edition):

Zhuo, Wei. “2D+3D Indoor Scene Understanding from a Single Monocular Image .” 2018. Web. 19 Jan 2021.

Vancouver:

Zhuo W. 2D+3D Indoor Scene Understanding from a Single Monocular Image . [Internet] [Thesis]. Australian National University; 2018. [cited 2021 Jan 19]. Available from: http://hdl.handle.net/1885/144616.

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

Council of Science Editors:

Zhuo W. 2D+3D Indoor Scene Understanding from a Single Monocular Image . [Thesis]. Australian National University; 2018. Available from: http://hdl.handle.net/1885/144616

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

.