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You searched for subject:( object instance segmentation). Showing records 1 – 30 of 7730 total matches.

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Australian National University

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

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 September 19, 2019. 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 Sep 2019.

Vancouver:

Zhang H. Learning to Generate and Refine Object Proposals . [Internet] [Thesis]. Australian National University; 2018. [cited 2019 Sep 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

2. 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 September 19, 2019. 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 Sep 2019.

Vancouver:

Hayder Z. Deep Structured Models for Large Scale Object Co-detection and Segmentation . [Internet] [Thesis]. Australian National University; 2017. [cited 2019 Sep 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

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

Degree: Docteur es, Mathématiques et informatique, 2016, Paris Saclay

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

APA (6th Edition):

Tsogkas, S. (2016). Mid-level representations for modeling objects : Représentations de niveau intermédiaire pour la modélisation d'objets. (Doctoral Dissertation). Paris Saclay. 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, Paris Saclay. Accessed September 19, 2019. 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 Sep 2019.

Vancouver:

Tsogkas S. Mid-level representations for modeling objects : Représentations de niveau intermédiaire pour la modélisation d'objets. [Internet] [Doctoral dissertation]. Paris Saclay; 2016. [cited 2019 Sep 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]. Paris Saclay; 2016. Available from: http://www.theses.fr/2016SACLC012


University of Illinois – Urbana-Champaign

4. 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 September 19, 2019. http://hdl.handle.net/2142/26317.

MLA Handbook (7th Edition):

Akbas, Emre. “Generation and analysis of segmentation trees for natural images.” 2011. Web. 19 Sep 2019.

Vancouver:

Akbas E. Generation and analysis of segmentation trees for natural images. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2019 Sep 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


Queens University

5. Lam, Joseph. Object Recognition by Registration of Repeatable 3D Interest Segments .

Degree: Electrical and Computer Engineering, 2015, Queens University

 3D object recognition using depth data remains a difficult problem in computer vision. In this thesis, an object recognition system based on registering repeatable 3D… (more)

Subjects/Keywords: 3D Segmentation; Object Recognition; Registration

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

APA (6th Edition):

Lam, J. (2015). Object Recognition by Registration of Repeatable 3D Interest Segments . (Thesis). Queens University. Retrieved from http://hdl.handle.net/1974/12728

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):

Lam, Joseph. “Object Recognition by Registration of Repeatable 3D Interest Segments .” 2015. Thesis, Queens University. Accessed September 19, 2019. http://hdl.handle.net/1974/12728.

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

MLA Handbook (7th Edition):

Lam, Joseph. “Object Recognition by Registration of Repeatable 3D Interest Segments .” 2015. Web. 19 Sep 2019.

Vancouver:

Lam J. Object Recognition by Registration of Repeatable 3D Interest Segments . [Internet] [Thesis]. Queens University; 2015. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/1974/12728.

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

Council of Science Editors:

Lam J. Object Recognition by Registration of Repeatable 3D Interest Segments . [Thesis]. Queens University; 2015. Available from: http://hdl.handle.net/1974/12728

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

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

Degree: Docteur es, Informatique, 2016, Paris Sciences et Lettres

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. 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. Accessed September 19, 2019. 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 Sep 2019.

Vancouver:

Seguin G. Analyse des personnes dans les films stéréoscopiques : Person analysis in stereoscopic movies. [Internet] [Doctoral dissertation]. Paris Sciences et Lettres; 2016. [cited 2019 Sep 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; 2016. Available from: http://www.theses.fr/2016PSLEE021


University of Louisville

7. Farag, Amal A. Modeling small objects under uncertainties : novel algorithms and applications.

Degree: PhD, 2012, University of Louisville

 Active Shape Models (ASM), Active Appearance Models (AAM) and Active Tensor Models (ATM) are common approaches to model elastic (deformable) objects. These models require an… (more)

Subjects/Keywords: Object modeling; Object categorization; Object segmentation; Lung nodules; Object detection

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

APA (6th Edition):

Farag, A. A. (2012). Modeling small objects under uncertainties : novel algorithms and applications. (Doctoral Dissertation). University of Louisville. Retrieved from 10.18297/etd/423 ; https://ir.library.louisville.edu/etd/423

Chicago Manual of Style (16th Edition):

Farag, Amal A. “Modeling small objects under uncertainties : novel algorithms and applications.” 2012. Doctoral Dissertation, University of Louisville. Accessed September 19, 2019. 10.18297/etd/423 ; https://ir.library.louisville.edu/etd/423.

MLA Handbook (7th Edition):

Farag, Amal A. “Modeling small objects under uncertainties : novel algorithms and applications.” 2012. Web. 19 Sep 2019.

Vancouver:

Farag AA. Modeling small objects under uncertainties : novel algorithms and applications. [Internet] [Doctoral dissertation]. University of Louisville; 2012. [cited 2019 Sep 19]. Available from: 10.18297/etd/423 ; https://ir.library.louisville.edu/etd/423.

Council of Science Editors:

Farag AA. Modeling small objects under uncertainties : novel algorithms and applications. [Doctoral Dissertation]. University of Louisville; 2012. Available from: 10.18297/etd/423 ; https://ir.library.louisville.edu/etd/423


University of New South Wales

8. Wang, Weihong. A Weakly Supervised Approach for Object Detection.

Degree: Computer Science & Engineering, 2016, University of New South Wales

Object detection in images and videos is an important topic in computer vision. In general, a large number of training samples are required to train… (more)

Subjects/Keywords: Boosting; Weakly supervised learning; Multiple instance learning; Object detection; Online learning

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

APA (6th Edition):

Wang, W. (2016). A Weakly Supervised Approach for Object Detection. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/56619 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41053/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Wang, Weihong. “A Weakly Supervised Approach for Object Detection.” 2016. Doctoral Dissertation, University of New South Wales. Accessed September 19, 2019. http://handle.unsw.edu.au/1959.4/56619 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41053/SOURCE02?view=true.

MLA Handbook (7th Edition):

Wang, Weihong. “A Weakly Supervised Approach for Object Detection.” 2016. Web. 19 Sep 2019.

Vancouver:

Wang W. A Weakly Supervised Approach for Object Detection. [Internet] [Doctoral dissertation]. University of New South Wales; 2016. [cited 2019 Sep 19]. Available from: http://handle.unsw.edu.au/1959.4/56619 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41053/SOURCE02?view=true.

Council of Science Editors:

Wang W. A Weakly Supervised Approach for Object Detection. [Doctoral Dissertation]. University of New South Wales; 2016. Available from: http://handle.unsw.edu.au/1959.4/56619 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41053/SOURCE02?view=true


UCLA

9. Taylor, Brian. Leveraging Occlusion Cues for Causal Video Object Segmentation.

Degree: Computer Science, 2016, UCLA

 This thesis describes a framework leveraging occlusions as a cue for detecting objects and accurately localizing their boundaries throughout the course of a video. Triggered… (more)

Subjects/Keywords: Computer science; depth layer segmentation; object tracking; occlusions; video object segmentation; video segmentation

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

APA (6th Edition):

Taylor, B. (2016). Leveraging Occlusion Cues for Causal Video Object Segmentation. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/3xz0z5k9

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):

Taylor, Brian. “Leveraging Occlusion Cues for Causal Video Object Segmentation.” 2016. Thesis, UCLA. Accessed September 19, 2019. http://www.escholarship.org/uc/item/3xz0z5k9.

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

MLA Handbook (7th Edition):

Taylor, Brian. “Leveraging Occlusion Cues for Causal Video Object Segmentation.” 2016. Web. 19 Sep 2019.

Vancouver:

Taylor B. Leveraging Occlusion Cues for Causal Video Object Segmentation. [Internet] [Thesis]. UCLA; 2016. [cited 2019 Sep 19]. Available from: http://www.escholarship.org/uc/item/3xz0z5k9.

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

Council of Science Editors:

Taylor B. Leveraging Occlusion Cues for Causal Video Object Segmentation. [Thesis]. UCLA; 2016. Available from: http://www.escholarship.org/uc/item/3xz0z5k9

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

10. Oliveira Pinheiro, Pedro Henrique. Large-Scale Image Segmentation with Convolutional Networks.

Degree: 2017, EPFL

Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial… (more)

Subjects/Keywords: object recognition; artificial neural networks; deep learning; semantic segmentation; object proposals; object detection; image segmentation.

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

APA (6th Edition):

Oliveira Pinheiro, P. H. (2017). Large-Scale Image Segmentation with Convolutional Networks. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/225546

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):

Oliveira Pinheiro, Pedro Henrique. “Large-Scale Image Segmentation with Convolutional Networks.” 2017. Thesis, EPFL. Accessed September 19, 2019. http://infoscience.epfl.ch/record/225546.

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

MLA Handbook (7th Edition):

Oliveira Pinheiro, Pedro Henrique. “Large-Scale Image Segmentation with Convolutional Networks.” 2017. Web. 19 Sep 2019.

Vancouver:

Oliveira Pinheiro PH. Large-Scale Image Segmentation with Convolutional Networks. [Internet] [Thesis]. EPFL; 2017. [cited 2019 Sep 19]. Available from: http://infoscience.epfl.ch/record/225546.

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

Council of Science Editors:

Oliveira Pinheiro PH. Large-Scale Image Segmentation with Convolutional Networks. [Thesis]. EPFL; 2017. Available from: http://infoscience.epfl.ch/record/225546

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


Universiteit Utrecht

11. Deiman, J. Automatic Tracking, Segmentation, Removal and Replay of Traffic in Panoramic 360-Degree Videos for Virtual Reality.

Degree: 2016, Universiteit Utrecht

 The Netherlands Aerospace Centre (NLR) has a virtual reality setup which allows users to experience an airplane fly-over with realistically simulated sound. They want to… (more)

Subjects/Keywords: object detection; object tracking; object segmentation; inpainting; virtual reality; panoramic video

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

APA (6th Edition):

Deiman, J. (2016). Automatic Tracking, Segmentation, Removal and Replay of Traffic in Panoramic 360-Degree Videos for Virtual Reality. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/334268

Chicago Manual of Style (16th Edition):

Deiman, J. “Automatic Tracking, Segmentation, Removal and Replay of Traffic in Panoramic 360-Degree Videos for Virtual Reality.” 2016. Masters Thesis, Universiteit Utrecht. Accessed September 19, 2019. http://dspace.library.uu.nl:8080/handle/1874/334268.

MLA Handbook (7th Edition):

Deiman, J. “Automatic Tracking, Segmentation, Removal and Replay of Traffic in Panoramic 360-Degree Videos for Virtual Reality.” 2016. Web. 19 Sep 2019.

Vancouver:

Deiman J. Automatic Tracking, Segmentation, Removal and Replay of Traffic in Panoramic 360-Degree Videos for Virtual Reality. [Internet] [Masters thesis]. Universiteit Utrecht; 2016. [cited 2019 Sep 19]. Available from: http://dspace.library.uu.nl:8080/handle/1874/334268.

Council of Science Editors:

Deiman J. Automatic Tracking, Segmentation, Removal and Replay of Traffic in Panoramic 360-Degree Videos for Virtual Reality. [Masters Thesis]. Universiteit Utrecht; 2016. Available from: http://dspace.library.uu.nl:8080/handle/1874/334268

12. Zou, Wenbin. Semantic-oriented Object Segmentation : Segmentation d'objet pour l'interprétation sémantique.

Degree: Docteur es, Traitement du signal et de l'image, 2014, Rennes, INSA

Cette thèse porte sur les problèmes de segmentation d’objets et la segmentation sémantique qui visent soit à séparer des objets du fond, soit à l’attribution… (more)

Subjects/Keywords: Segmentation d’objets; Segmentation sémantique; Détection de saillance; Object segmentation; Semantic segmentation; Saliency detection; 621.367

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

APA (6th Edition):

Zou, W. (2014). Semantic-oriented Object Segmentation : Segmentation d'objet pour l'interprétation sémantique. (Doctoral Dissertation). Rennes, INSA. Retrieved from http://www.theses.fr/2014ISAR0007

Chicago Manual of Style (16th Edition):

Zou, Wenbin. “Semantic-oriented Object Segmentation : Segmentation d'objet pour l'interprétation sémantique.” 2014. Doctoral Dissertation, Rennes, INSA. Accessed September 19, 2019. http://www.theses.fr/2014ISAR0007.

MLA Handbook (7th Edition):

Zou, Wenbin. “Semantic-oriented Object Segmentation : Segmentation d'objet pour l'interprétation sémantique.” 2014. Web. 19 Sep 2019.

Vancouver:

Zou W. Semantic-oriented Object Segmentation : Segmentation d'objet pour l'interprétation sémantique. [Internet] [Doctoral dissertation]. Rennes, INSA; 2014. [cited 2019 Sep 19]. Available from: http://www.theses.fr/2014ISAR0007.

Council of Science Editors:

Zou W. Semantic-oriented Object Segmentation : Segmentation d'objet pour l'interprétation sémantique. [Doctoral Dissertation]. Rennes, INSA; 2014. Available from: http://www.theses.fr/2014ISAR0007


Carnegie Mellon University

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

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 September 19, 2019. 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 Sep 2019.

Vancouver:

Le NTH. Contextual Recurrent Level Set Networks and Recurrent Residual Networks for Semantic Labeling. [Internet] [Thesis]. Carnegie Mellon University; 2018. [cited 2019 Sep 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


University of Southern California

14. Sharma, Pramod Kumar. Effective incremental learning and detector adaptation methods for video object detection.

Degree: PhD, Computer Science, 2014, University of Southern California

Object detection is a challenging problem in Computer Vision. With increasing use of social media, smart phones and modern digital cameras thousands of videos are… (more)

Subjects/Keywords: object detection; human detection; adaptation; incremental learning; multiple instance learning; unsupervised; online; video; surveillance

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

APA (6th Edition):

Sharma, P. K. (2014). Effective incremental learning and detector adaptation methods for video object detection. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/489008/rec/2186

Chicago Manual of Style (16th Edition):

Sharma, Pramod Kumar. “Effective incremental learning and detector adaptation methods for video object detection.” 2014. Doctoral Dissertation, University of Southern California. Accessed September 19, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/489008/rec/2186.

MLA Handbook (7th Edition):

Sharma, Pramod Kumar. “Effective incremental learning and detector adaptation methods for video object detection.” 2014. Web. 19 Sep 2019.

Vancouver:

Sharma PK. Effective incremental learning and detector adaptation methods for video object detection. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2019 Sep 19]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/489008/rec/2186.

Council of Science Editors:

Sharma PK. Effective incremental learning and detector adaptation methods for video object detection. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/489008/rec/2186


University of California – Merced

15. Safar, Simon. Learning shape priors with neural networks.

Degree: Electrical Engineering and Computer Science, 2014, University of California – Merced

 We propose two methods for object segmentation by combining learned shape priors with local features. The first, Max-Margin Boltzmann Machines, learns shapes in an unsupervised… (more)

Subjects/Keywords: Computer science; image; learning; object; segmentation

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

APA (6th Edition):

Safar, S. (2014). Learning shape priors with neural networks. (Thesis). University of California – Merced. Retrieved from http://www.escholarship.org/uc/item/709186x7

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):

Safar, Simon. “Learning shape priors with neural networks.” 2014. Thesis, University of California – Merced. Accessed September 19, 2019. http://www.escholarship.org/uc/item/709186x7.

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

MLA Handbook (7th Edition):

Safar, Simon. “Learning shape priors with neural networks.” 2014. Web. 19 Sep 2019.

Vancouver:

Safar S. Learning shape priors with neural networks. [Internet] [Thesis]. University of California – Merced; 2014. [cited 2019 Sep 19]. Available from: http://www.escholarship.org/uc/item/709186x7.

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

Council of Science Editors:

Safar S. Learning shape priors with neural networks. [Thesis]. University of California – Merced; 2014. Available from: http://www.escholarship.org/uc/item/709186x7

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


Universitat Autònoma de Barcelona

16. Gonfaus, Josep M. Towards Deep Image Understanding: From pixels to semantics.

Degree: Departament de Ciències de la Computació, 2012, Universitat Autònoma de Barcelona

 Understand the content of the images is one of the great challenges of computer vision. Being able to recognize which are the objects in the… (more)

Subjects/Keywords: Object recognition; Localization; Segmentation; Tecnologies; 004

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APA (6th Edition):

Gonfaus, J. M. (2012). Towards Deep Image Understanding: From pixels to semantics. (Thesis). Universitat Autònoma de Barcelona. Retrieved from http://hdl.handle.net/10803/117584

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):

Gonfaus, Josep M. “Towards Deep Image Understanding: From pixels to semantics.” 2012. Thesis, Universitat Autònoma de Barcelona. Accessed September 19, 2019. http://hdl.handle.net/10803/117584.

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

MLA Handbook (7th Edition):

Gonfaus, Josep M. “Towards Deep Image Understanding: From pixels to semantics.” 2012. Web. 19 Sep 2019.

Vancouver:

Gonfaus JM. Towards Deep Image Understanding: From pixels to semantics. [Internet] [Thesis]. Universitat Autònoma de Barcelona; 2012. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/10803/117584.

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

Council of Science Editors:

Gonfaus JM. Towards Deep Image Understanding: From pixels to semantics. [Thesis]. Universitat Autònoma de Barcelona; 2012. Available from: http://hdl.handle.net/10803/117584

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


University of Adelaide

17. Cao, Yuanzhouhan. Deep learning based RGB-D vision tasks.

Degree: 2018, University of Adelaide

 Depth is an important source of information in computer vision. However, depth is usually discarded in most vision tasks. In this thesis, we study the… (more)

Subjects/Keywords: depth estimation; object detection; semantic segmentation

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

APA (6th Edition):

Cao, Y. (2018). Deep learning based RGB-D vision tasks. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/112866

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):

Cao, Yuanzhouhan. “Deep learning based RGB-D vision tasks.” 2018. Thesis, University of Adelaide. Accessed September 19, 2019. http://hdl.handle.net/2440/112866.

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

MLA Handbook (7th Edition):

Cao, Yuanzhouhan. “Deep learning based RGB-D vision tasks.” 2018. Web. 19 Sep 2019.

Vancouver:

Cao Y. Deep learning based RGB-D vision tasks. [Internet] [Thesis]. University of Adelaide; 2018. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/2440/112866.

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

Council of Science Editors:

Cao Y. Deep learning based RGB-D vision tasks. [Thesis]. University of Adelaide; 2018. Available from: http://hdl.handle.net/2440/112866

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


King Abdullah University of Science and Technology

18. Lao, Dong. Minimum Delay Moving Object Detection.

Degree: 2017, King Abdullah University of Science and Technology

 This thesis presents a general framework and method for detection of an object in a video based on apparent motion. The object moves, at some… (more)

Subjects/Keywords: object detection; motion segmentation; optimal delay

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APA (6th Edition):

Lao, D. (2017). Minimum Delay Moving Object Detection. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/623619

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):

Lao, Dong. “Minimum Delay Moving Object Detection.” 2017. Thesis, King Abdullah University of Science and Technology. Accessed September 19, 2019. http://hdl.handle.net/10754/623619.

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

MLA Handbook (7th Edition):

Lao, Dong. “Minimum Delay Moving Object Detection.” 2017. Web. 19 Sep 2019.

Vancouver:

Lao D. Minimum Delay Moving Object Detection. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2017. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/10754/623619.

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

Council of Science Editors:

Lao D. Minimum Delay Moving Object Detection. [Thesis]. King Abdullah University of Science and Technology; 2017. Available from: http://hdl.handle.net/10754/623619

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


University of Texas – Austin

19. Jain, Suyog Dutt. Human machine collaboration for foreground segmentation in images and videos.

Degree: PhD, Computer Science, 2018, University of Texas – Austin

 Foreground segmentation is defined as the problem of generating pixel level foreground masks for all the objects in a given image or video. Accurate foreground… (more)

Subjects/Keywords: Computer vision; Crowdsourcing; Human machine collaboration; Image and video segmentation; Image segmentation; Video segmentation; Foreground segmentation; Object segmentation

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APA (6th Edition):

Jain, S. D. (2018). Human machine collaboration for foreground segmentation in images and videos. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63453

Chicago Manual of Style (16th Edition):

Jain, Suyog Dutt. “Human machine collaboration for foreground segmentation in images and videos.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed September 19, 2019. http://hdl.handle.net/2152/63453.

MLA Handbook (7th Edition):

Jain, Suyog Dutt. “Human machine collaboration for foreground segmentation in images and videos.” 2018. Web. 19 Sep 2019.

Vancouver:

Jain SD. Human machine collaboration for foreground segmentation in images and videos. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/2152/63453.

Council of Science Editors:

Jain SD. Human machine collaboration for foreground segmentation in images and videos. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/63453


Temple University

20. Ma, Tianyang. Graph-based Inference with Constraints for Object Detection and Segmentation.

Degree: PhD, 2013, Temple University

Computer and Information Science

For many fundamental problems of computer vision, adopting a graph-based framework can be straight-forward and very effective. In this thesis, I… (more)

Subjects/Keywords: Computer science;

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APA (6th Edition):

Ma, T. (2013). Graph-based Inference with Constraints for Object Detection and Segmentation. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,231622

Chicago Manual of Style (16th Edition):

Ma, Tianyang. “Graph-based Inference with Constraints for Object Detection and Segmentation.” 2013. Doctoral Dissertation, Temple University. Accessed September 19, 2019. http://digital.library.temple.edu/u?/p245801coll10,231622.

MLA Handbook (7th Edition):

Ma, Tianyang. “Graph-based Inference with Constraints for Object Detection and Segmentation.” 2013. Web. 19 Sep 2019.

Vancouver:

Ma T. Graph-based Inference with Constraints for Object Detection and Segmentation. [Internet] [Doctoral dissertation]. Temple University; 2013. [cited 2019 Sep 19]. Available from: http://digital.library.temple.edu/u?/p245801coll10,231622.

Council of Science Editors:

Ma T. Graph-based Inference with Constraints for Object Detection and Segmentation. [Doctoral Dissertation]. Temple University; 2013. Available from: http://digital.library.temple.edu/u?/p245801coll10,231622


University of Alberta

21. Saini, Amritpal S. Real time spatio temporal segmentation of RGBD cloud and applications.

Degree: MS, Department of Computing Science, 2015, University of Alberta

 There is considerable research work going on segmentation of RGB-D clouds due its applications in tasks like scene understanding, robotics etc. The availability of inexpensive… (more)

Subjects/Keywords: Segmentation; Object discovery; Object detection; GPU; Point cloud; RGBD; Micorsoft Kinect

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

APA (6th Edition):

Saini, A. S. (2015). Real time spatio temporal segmentation of RGBD cloud and applications. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/k3569693x

Chicago Manual of Style (16th Edition):

Saini, Amritpal S. “Real time spatio temporal segmentation of RGBD cloud and applications.” 2015. Masters Thesis, University of Alberta. Accessed September 19, 2019. https://era.library.ualberta.ca/files/k3569693x.

MLA Handbook (7th Edition):

Saini, Amritpal S. “Real time spatio temporal segmentation of RGBD cloud and applications.” 2015. Web. 19 Sep 2019.

Vancouver:

Saini AS. Real time spatio temporal segmentation of RGBD cloud and applications. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2019 Sep 19]. Available from: https://era.library.ualberta.ca/files/k3569693x.

Council of Science Editors:

Saini AS. Real time spatio temporal segmentation of RGBD cloud and applications. [Masters Thesis]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/k3569693x


University of Manitoba

22. Naha, Shujon. Zero-shot Learning for Visual Recognition Problems.

Degree: Computer Science, 2015, University of Manitoba

 In this thesis we discuss different aspects of zero-shot learning and propose solutions for three challenging visual recognition problems: 1) unknown object recognition from images… (more)

Subjects/Keywords: Zero-shot Learning; Computer Vision; Object Recognition; Action Recognition; Object Segmentation

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APA (6th Edition):

Naha, S. (2015). Zero-shot Learning for Visual Recognition Problems. (Masters Thesis). University of Manitoba. Retrieved from http://hdl.handle.net/1993/31806

Chicago Manual of Style (16th Edition):

Naha, Shujon. “Zero-shot Learning for Visual Recognition Problems.” 2015. Masters Thesis, University of Manitoba. Accessed September 19, 2019. http://hdl.handle.net/1993/31806.

MLA Handbook (7th Edition):

Naha, Shujon. “Zero-shot Learning for Visual Recognition Problems.” 2015. Web. 19 Sep 2019.

Vancouver:

Naha S. Zero-shot Learning for Visual Recognition Problems. [Internet] [Masters thesis]. University of Manitoba; 2015. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/1993/31806.

Council of Science Editors:

Naha S. Zero-shot Learning for Visual Recognition Problems. [Masters Thesis]. University of Manitoba; 2015. Available from: http://hdl.handle.net/1993/31806


Australian National University

23. Wang, Tao. Context-driven Object Detection and Segmentation with Auxiliary Information .

Degree: 2016, Australian National University

 One fundamental problem in computer vision and robotics is to localize objects of interest in an image. The task can either be formulated as an… (more)

Subjects/Keywords: object detection; glass object segmentation; semi-supervised boosting

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APA (6th Edition):

Wang, T. (2016). Context-driven Object Detection and Segmentation with Auxiliary Information . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/110355

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):

Wang, Tao. “Context-driven Object Detection and Segmentation with Auxiliary Information .” 2016. Thesis, Australian National University. Accessed September 19, 2019. http://hdl.handle.net/1885/110355.

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

MLA Handbook (7th Edition):

Wang, Tao. “Context-driven Object Detection and Segmentation with Auxiliary Information .” 2016. Web. 19 Sep 2019.

Vancouver:

Wang T. Context-driven Object Detection and Segmentation with Auxiliary Information . [Internet] [Thesis]. Australian National University; 2016. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/1885/110355.

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

Council of Science Editors:

Wang T. Context-driven Object Detection and Segmentation with Auxiliary Information . [Thesis]. Australian National University; 2016. Available from: http://hdl.handle.net/1885/110355

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


EPFL

24. Achanta, Radhakrishna. Finding Objects of Interest in Images using Saliency and Superpixels.

Degree: 2011, EPFL

 The ability to automatically find objects of interest in images is useful in the areas of compression, indexing and retrieval, re-targeting, and so on. There… (more)

Subjects/Keywords: saliency; segmentation; object detection; superpixels; medical image segmentation; saillance; segmentation; détection d'objets; superpixels; segmentation d'images médicales

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APA (6th Edition):

Achanta, R. (2011). Finding Objects of Interest in Images using Saliency and Superpixels. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/153491

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):

Achanta, Radhakrishna. “Finding Objects of Interest in Images using Saliency and Superpixels.” 2011. Thesis, EPFL. Accessed September 19, 2019. http://infoscience.epfl.ch/record/153491.

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

MLA Handbook (7th Edition):

Achanta, Radhakrishna. “Finding Objects of Interest in Images using Saliency and Superpixels.” 2011. Web. 19 Sep 2019.

Vancouver:

Achanta R. Finding Objects of Interest in Images using Saliency and Superpixels. [Internet] [Thesis]. EPFL; 2011. [cited 2019 Sep 19]. Available from: http://infoscience.epfl.ch/record/153491.

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

Council of Science Editors:

Achanta R. Finding Objects of Interest in Images using Saliency and Superpixels. [Thesis]. EPFL; 2011. Available from: http://infoscience.epfl.ch/record/153491

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


Australian National University

25. 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

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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 September 19, 2019. 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 Sep 2019.

Vancouver:

Zhuo W. 2D+3D Indoor Scene Understanding from a Single Monocular Image . [Internet] [Thesis]. Australian National University; 2018. [cited 2019 Sep 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


California State University – Sacramento

26. Suryavanshi, Rajani Mohan. Ranking system object instance graph.

Degree: 2019, California State University – Sacramento

 Defending today???s enterprise network has become more and more challenging considering the increasing amount of cyber-attacks. It is critical to understand how an attack happens… (more)

Subjects/Keywords: Asset rank; Page rank; Network security; Enterprise security; System object instance graph; Zero-day attack path

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APA (6th Edition):

Suryavanshi, R. M. (2019). Ranking system object instance graph. (Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/207979

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):

Suryavanshi, Rajani Mohan. “Ranking system object instance graph.” 2019. Thesis, California State University – Sacramento. Accessed September 19, 2019. http://hdl.handle.net/10211.3/207979.

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

MLA Handbook (7th Edition):

Suryavanshi, Rajani Mohan. “Ranking system object instance graph.” 2019. Web. 19 Sep 2019.

Vancouver:

Suryavanshi RM. Ranking system object instance graph. [Internet] [Thesis]. California State University – Sacramento; 2019. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/10211.3/207979.

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

Council of Science Editors:

Suryavanshi RM. Ranking system object instance graph. [Thesis]. California State University – Sacramento; 2019. Available from: http://hdl.handle.net/10211.3/207979

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


Penn State University

27. Yin, Zhaozheng. Persistent Object Tracking by Figure-Ground Segmentation.

Degree: PhD, Computer Science and Engineering, 2009, Penn State University

 To persistently track objects through changes in appearance and environment, a tracker's object appearance model must be adapted over time. However, adaptation must be done… (more)

Subjects/Keywords: motion segmentation; figure-ground segmentation; feature selection and fusion; tracking failure recovery; object tracking

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

APA (6th Edition):

Yin, Z. (2009). Persistent Object Tracking by Figure-Ground Segmentation. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/9876

Chicago Manual of Style (16th Edition):

Yin, Zhaozheng. “Persistent Object Tracking by Figure-Ground Segmentation.” 2009. Doctoral Dissertation, Penn State University. Accessed September 19, 2019. https://etda.libraries.psu.edu/catalog/9876.

MLA Handbook (7th Edition):

Yin, Zhaozheng. “Persistent Object Tracking by Figure-Ground Segmentation.” 2009. Web. 19 Sep 2019.

Vancouver:

Yin Z. Persistent Object Tracking by Figure-Ground Segmentation. [Internet] [Doctoral dissertation]. Penn State University; 2009. [cited 2019 Sep 19]. Available from: https://etda.libraries.psu.edu/catalog/9876.

Council of Science Editors:

Yin Z. Persistent Object Tracking by Figure-Ground Segmentation. [Doctoral Dissertation]. Penn State University; 2009. Available from: https://etda.libraries.psu.edu/catalog/9876


The Ohio State University

28. Sharma, Vinay. Simultaneous object detection and segmentation using top-down and bottom-up processing.

Degree: PhD, Computer and Information Science, 2008, The Ohio State University

 This thesis addresses the fundamental tasks of detecting objects in images, recovering their location, and determining their silhouette shape. We focus on object detection techniques… (more)

Subjects/Keywords: Computer Science; Object detection; Object segmentation; Simultaneous detection and segmentation; Thermal imagery; IR imagery; EO-IR fusion

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APA (6th Edition):

Sharma, V. (2008). Simultaneous object detection and segmentation using top-down and bottom-up processing. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1196372113

Chicago Manual of Style (16th Edition):

Sharma, Vinay. “Simultaneous object detection and segmentation using top-down and bottom-up processing.” 2008. Doctoral Dissertation, The Ohio State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1196372113.

MLA Handbook (7th Edition):

Sharma, Vinay. “Simultaneous object detection and segmentation using top-down and bottom-up processing.” 2008. Web. 19 Sep 2019.

Vancouver:

Sharma V. Simultaneous object detection and segmentation using top-down and bottom-up processing. [Internet] [Doctoral dissertation]. The Ohio State University; 2008. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1196372113.

Council of Science Editors:

Sharma V. Simultaneous object detection and segmentation using top-down and bottom-up processing. [Doctoral Dissertation]. The Ohio State University; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1196372113

29. Petit, Antoine. Robust visual detection and tracking of complex objects : applications to space autonomous rendez-vous and proximity operations : Détection et suivi visuels robustes d'objets complexes : applications au rendezvous spatial autonome.

Degree: Docteur es, Traitement du signal et télécommunications, 2013, Rennes 1

Dans cette thèse nous étudions le fait de localiser complètement un objet connu par vision artificielle, en utilisant une caméra monoculaire, ce qui constitue un… (more)

Subjects/Keywords: Suivi visuel; Détection d’objets; Segmentation; Robotique spatiale; Visual tracking; Object detection; Moving object segmentation; Space robotics

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APA (6th Edition):

Petit, A. (2013). Robust visual detection and tracking of complex objects : applications to space autonomous rendez-vous and proximity operations : Détection et suivi visuels robustes d'objets complexes : applications au rendezvous spatial autonome. (Doctoral Dissertation). Rennes 1. Retrieved from http://www.theses.fr/2013REN1S190

Chicago Manual of Style (16th Edition):

Petit, Antoine. “Robust visual detection and tracking of complex objects : applications to space autonomous rendez-vous and proximity operations : Détection et suivi visuels robustes d'objets complexes : applications au rendezvous spatial autonome.” 2013. Doctoral Dissertation, Rennes 1. Accessed September 19, 2019. http://www.theses.fr/2013REN1S190.

MLA Handbook (7th Edition):

Petit, Antoine. “Robust visual detection and tracking of complex objects : applications to space autonomous rendez-vous and proximity operations : Détection et suivi visuels robustes d'objets complexes : applications au rendezvous spatial autonome.” 2013. Web. 19 Sep 2019.

Vancouver:

Petit A. Robust visual detection and tracking of complex objects : applications to space autonomous rendez-vous and proximity operations : Détection et suivi visuels robustes d'objets complexes : applications au rendezvous spatial autonome. [Internet] [Doctoral dissertation]. Rennes 1; 2013. [cited 2019 Sep 19]. Available from: http://www.theses.fr/2013REN1S190.

Council of Science Editors:

Petit A. Robust visual detection and tracking of complex objects : applications to space autonomous rendez-vous and proximity operations : Détection et suivi visuels robustes d'objets complexes : applications au rendezvous spatial autonome. [Doctoral Dissertation]. Rennes 1; 2013. Available from: http://www.theses.fr/2013REN1S190

30. 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

…Since humans tend to perform object-level segmentation, producing ground truth for the… …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… 

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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 September 19, 2019. 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 Sep 2019.

Vancouver:

Cheng HT. Unsupervised video segmentation and its application to activity recognition. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2019 Sep 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

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