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You searched for subject:( object detection). Showing records 1 – 30 of 665 total matches.

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1. Modolo, Davide. Advances in detecting object classes and their semantic parts.

Degree: PhD, 2017, University of Edinburgh

Object classes are central to computer vision and have been the focus of substantial research in the last fifteen years. This thesis addresses the tasks… (more)

Subjects/Keywords: 006.3; object detection; part detection

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APA (6th Edition):

Modolo, D. (2017). Advances in detecting object classes and their semantic parts. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/23472

Chicago Manual of Style (16th Edition):

Modolo, Davide. “Advances in detecting object classes and their semantic parts.” 2017. Doctoral Dissertation, University of Edinburgh. Accessed October 26, 2020. http://hdl.handle.net/1842/23472.

MLA Handbook (7th Edition):

Modolo, Davide. “Advances in detecting object classes and their semantic parts.” 2017. Web. 26 Oct 2020.

Vancouver:

Modolo D. Advances in detecting object classes and their semantic parts. [Internet] [Doctoral dissertation]. University of Edinburgh; 2017. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/1842/23472.

Council of Science Editors:

Modolo D. Advances in detecting object classes and their semantic parts. [Doctoral Dissertation]. University of Edinburgh; 2017. Available from: http://hdl.handle.net/1842/23472


University of Louisville

2. 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 (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 October 26, 2020. 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. 26 Oct 2020.

Vancouver:

Farag AA. Modeling small objects under uncertainties : novel algorithms and applications. [Internet] [Doctoral dissertation]. University of Louisville; 2012. [cited 2020 Oct 26]. 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 Victoria

3. Moria, Kawther. Computer vision-based detection of fire and violent actions performed by individuals in videos acquired with handheld devices.

Degree: Department of Computer Science, 2016, University of Victoria

 Advances in social networks and multimedia technologies greatly facilitate the recording and sharing of video data on violent social and/or political events via In- ternet.… (more)

Subjects/Keywords: Object detection; Fire detection; Object recognition; Violent scene detection; Crowd recognition.

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APA (6th Edition):

Moria, K. (2016). Computer vision-based detection of fire and violent actions performed by individuals in videos acquired with handheld devices. (Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/7423

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):

Moria, Kawther. “Computer vision-based detection of fire and violent actions performed by individuals in videos acquired with handheld devices.” 2016. Thesis, University of Victoria. Accessed October 26, 2020. http://hdl.handle.net/1828/7423.

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

MLA Handbook (7th Edition):

Moria, Kawther. “Computer vision-based detection of fire and violent actions performed by individuals in videos acquired with handheld devices.” 2016. Web. 26 Oct 2020.

Vancouver:

Moria K. Computer vision-based detection of fire and violent actions performed by individuals in videos acquired with handheld devices. [Internet] [Thesis]. University of Victoria; 2016. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/1828/7423.

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

Council of Science Editors:

Moria K. Computer vision-based detection of fire and violent actions performed by individuals in videos acquired with handheld devices. [Thesis]. University of Victoria; 2016. Available from: http://hdl.handle.net/1828/7423

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


University of Adelaide

4. Wang, Xinyu. High-performance Object Detection and Tracking Using Deep Learning.

Degree: 2019, University of Adelaide

 Human detection and tracking are two fundamental problems in computer vision, which have been cornerstones for many real-world applications such as video surveillance, intelligent transportation… (more)

Subjects/Keywords: Object detection; object tracking; deep learning

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

APA (6th Edition):

Wang, X. (2019). High-performance Object Detection and Tracking Using Deep Learning. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/124521

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, Xinyu. “High-performance Object Detection and Tracking Using Deep Learning.” 2019. Thesis, University of Adelaide. Accessed October 26, 2020. http://hdl.handle.net/2440/124521.

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

MLA Handbook (7th Edition):

Wang, Xinyu. “High-performance Object Detection and Tracking Using Deep Learning.” 2019. Web. 26 Oct 2020.

Vancouver:

Wang X. High-performance Object Detection and Tracking Using Deep Learning. [Internet] [Thesis]. University of Adelaide; 2019. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/2440/124521.

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

Council of Science Editors:

Wang X. High-performance Object Detection and Tracking Using Deep Learning. [Thesis]. University of Adelaide; 2019. Available from: http://hdl.handle.net/2440/124521

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


NSYSU

5. Lee, Allen. A VQ Coding Based Method for Object Detection.

Degree: Master, Mechanical and Electro-Mechanical Engineering, 2002, NSYSU

none Advisors/Committee Members: Yen,Chen-Wen (committee member), Cheng,Chi-Cheng (chair), Gou-Jen Wang (chair).

Subjects/Keywords: Object Detection

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

APA (6th Edition):

Lee, A. (2002). A VQ Coding Based Method for Object Detection. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716102-160939

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):

Lee, Allen. “A VQ Coding Based Method for Object Detection.” 2002. Thesis, NSYSU. Accessed October 26, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716102-160939.

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

MLA Handbook (7th Edition):

Lee, Allen. “A VQ Coding Based Method for Object Detection.” 2002. Web. 26 Oct 2020.

Vancouver:

Lee A. A VQ Coding Based Method for Object Detection. [Internet] [Thesis]. NSYSU; 2002. [cited 2020 Oct 26]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716102-160939.

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

Council of Science Editors:

Lee A. A VQ Coding Based Method for Object Detection. [Thesis]. NSYSU; 2002. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0716102-160939

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


Texas A&M University

6. Suresh, Karthik. Deep Nuisance Disentanglement for Robust Object Detection from Unmanned Aerial Vehicles.

Degree: MS, Computer Engineering, 2019, Texas A&M University

Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is becoming dramatically useful. Despite the great success of the generic object detection methods trained… (more)

Subjects/Keywords: Object Detection; Unmanned Aerial Vehicles

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APA (6th Edition):

Suresh, K. (2019). Deep Nuisance Disentanglement for Robust Object Detection from Unmanned Aerial Vehicles. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/185023

Chicago Manual of Style (16th Edition):

Suresh, Karthik. “Deep Nuisance Disentanglement for Robust Object Detection from Unmanned Aerial Vehicles.” 2019. Masters Thesis, Texas A&M University. Accessed October 26, 2020. http://hdl.handle.net/1969.1/185023.

MLA Handbook (7th Edition):

Suresh, Karthik. “Deep Nuisance Disentanglement for Robust Object Detection from Unmanned Aerial Vehicles.” 2019. Web. 26 Oct 2020.

Vancouver:

Suresh K. Deep Nuisance Disentanglement for Robust Object Detection from Unmanned Aerial Vehicles. [Internet] [Masters thesis]. Texas A&M University; 2019. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/1969.1/185023.

Council of Science Editors:

Suresh K. Deep Nuisance Disentanglement for Robust Object Detection from Unmanned Aerial Vehicles. [Masters Thesis]. Texas A&M University; 2019. Available from: http://hdl.handle.net/1969.1/185023


Universitat Autònoma de Barcelona

7. Pedersoli, Marco. Hierarchical multiresolution models for fast object detection.

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

 Day by day, the ability to automatically detect and recognize objects in unconstrained images is becoming more and more important. From security systems and robots,… (more)

Subjects/Keywords: Object detection; Multiresolution; Tecnologies; 004

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APA (6th Edition):

Pedersoli, M. (2012). Hierarchical multiresolution models for fast object detection. (Thesis). Universitat Autònoma de Barcelona. Retrieved from http://hdl.handle.net/10803/286175

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):

Pedersoli, Marco. “Hierarchical multiresolution models for fast object detection.” 2012. Thesis, Universitat Autònoma de Barcelona. Accessed October 26, 2020. http://hdl.handle.net/10803/286175.

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

MLA Handbook (7th Edition):

Pedersoli, Marco. “Hierarchical multiresolution models for fast object detection.” 2012. Web. 26 Oct 2020.

Vancouver:

Pedersoli M. Hierarchical multiresolution models for fast object detection. [Internet] [Thesis]. Universitat Autònoma de Barcelona; 2012. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/10803/286175.

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

Council of Science Editors:

Pedersoli M. Hierarchical multiresolution models for fast object detection. [Thesis]. Universitat Autònoma de Barcelona; 2012. Available from: http://hdl.handle.net/10803/286175

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


University of Bridgeport

8. Abualkibash, Munther Hamad. A Near Real-Time, Highly Scalable, Parallel and Distributed Adaptive Object Detection and Re-Training Framework Based on the Adaboost Algorithm .

Degree: 2015, University of Bridgeport

Object detection, such as face detection using supervised learning, often requires extensive training for the computer, which results in high execution times. If the trained… (more)

Subjects/Keywords: Computer science; Object detection

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APA (6th Edition):

Abualkibash, M. H. (2015). A Near Real-Time, Highly Scalable, Parallel and Distributed Adaptive Object Detection and Re-Training Framework Based on the Adaboost Algorithm . (Thesis). University of Bridgeport. Retrieved from https://scholarworks.bridgeport.edu/xmlui/handle/123456789/1213

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):

Abualkibash, Munther Hamad. “A Near Real-Time, Highly Scalable, Parallel and Distributed Adaptive Object Detection and Re-Training Framework Based on the Adaboost Algorithm .” 2015. Thesis, University of Bridgeport. Accessed October 26, 2020. https://scholarworks.bridgeport.edu/xmlui/handle/123456789/1213.

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

MLA Handbook (7th Edition):

Abualkibash, Munther Hamad. “A Near Real-Time, Highly Scalable, Parallel and Distributed Adaptive Object Detection and Re-Training Framework Based on the Adaboost Algorithm .” 2015. Web. 26 Oct 2020.

Vancouver:

Abualkibash MH. A Near Real-Time, Highly Scalable, Parallel and Distributed Adaptive Object Detection and Re-Training Framework Based on the Adaboost Algorithm . [Internet] [Thesis]. University of Bridgeport; 2015. [cited 2020 Oct 26]. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/1213.

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

Council of Science Editors:

Abualkibash MH. A Near Real-Time, Highly Scalable, Parallel and Distributed Adaptive Object Detection and Re-Training Framework Based on the Adaboost Algorithm . [Thesis]. University of Bridgeport; 2015. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/1213

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


Halmstad University

9. Mühlfellner, Peter. Selection, Analysis and Implementationof Image-based Feature Extraction Approaches for a Heterogenous, Modular and FPGA-based Architecture for Camera-based Driver Assistance Systems.

Degree: Intelligent systems (IS-lab), 2011, Halmstad University

  We propose a scalable and fexible hardware architecture for the extraction of image features, used in conjunction with an attentional cascade classifier for appearance-based… (more)

Subjects/Keywords: Object Detection; Feature Extraction; FPGA

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APA (6th Edition):

Mühlfellner, P. (2011). Selection, Analysis and Implementationof Image-based Feature Extraction Approaches for a Heterogenous, Modular and FPGA-based Architecture for Camera-based Driver Assistance Systems. (Thesis). Halmstad University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-16377

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):

Mühlfellner, Peter. “Selection, Analysis and Implementationof Image-based Feature Extraction Approaches for a Heterogenous, Modular and FPGA-based Architecture for Camera-based Driver Assistance Systems.” 2011. Thesis, Halmstad University. Accessed October 26, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-16377.

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

MLA Handbook (7th Edition):

Mühlfellner, Peter. “Selection, Analysis and Implementationof Image-based Feature Extraction Approaches for a Heterogenous, Modular and FPGA-based Architecture for Camera-based Driver Assistance Systems.” 2011. Web. 26 Oct 2020.

Vancouver:

Mühlfellner P. Selection, Analysis and Implementationof Image-based Feature Extraction Approaches for a Heterogenous, Modular and FPGA-based Architecture for Camera-based Driver Assistance Systems. [Internet] [Thesis]. Halmstad University; 2011. [cited 2020 Oct 26]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-16377.

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

Council of Science Editors:

Mühlfellner P. Selection, Analysis and Implementationof Image-based Feature Extraction Approaches for a Heterogenous, Modular and FPGA-based Architecture for Camera-based Driver Assistance Systems. [Thesis]. Halmstad University; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-16377

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


Virginia Tech

10. Yellapantula, Sudha Ravali. Synthesizing Realistic Data for Vision Based Drone-to-Drone Detection.

Degree: MS, Computer Engineering, 2019, Virginia Tech

 In the recent years, technologies like Deep Learning and Machine Learning have seen many rapid developments. Among the many applications they have, object detection is… (more)

Subjects/Keywords: GANs; Deep Learning; Object Detection

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

APA (6th Edition):

Yellapantula, S. R. (2019). Synthesizing Realistic Data for Vision Based Drone-to-Drone Detection. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/91460

Chicago Manual of Style (16th Edition):

Yellapantula, Sudha Ravali. “Synthesizing Realistic Data for Vision Based Drone-to-Drone Detection.” 2019. Masters Thesis, Virginia Tech. Accessed October 26, 2020. http://hdl.handle.net/10919/91460.

MLA Handbook (7th Edition):

Yellapantula, Sudha Ravali. “Synthesizing Realistic Data for Vision Based Drone-to-Drone Detection.” 2019. Web. 26 Oct 2020.

Vancouver:

Yellapantula SR. Synthesizing Realistic Data for Vision Based Drone-to-Drone Detection. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/10919/91460.

Council of Science Editors:

Yellapantula SR. Synthesizing Realistic Data for Vision Based Drone-to-Drone Detection. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/91460


Virginia Tech

11. Nguyen, Chuong Hoang. Features identification and tracking for an autonomous ground vehicle.

Degree: MS, Mechanical Engineering, 2013, Virginia Tech

 This thesis attempts to develop features identification and tracking system for an autonomous ground vehicle by focusing on four fundamental tasks: Motion detection, object tracking,… (more)

Subjects/Keywords: Motion detection; object tracking; scene recognition; object detection

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APA (6th Edition):

Nguyen, C. H. (2013). Features identification and tracking for an autonomous ground vehicle. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/33127

Chicago Manual of Style (16th Edition):

Nguyen, Chuong Hoang. “Features identification and tracking for an autonomous ground vehicle.” 2013. Masters Thesis, Virginia Tech. Accessed October 26, 2020. http://hdl.handle.net/10919/33127.

MLA Handbook (7th Edition):

Nguyen, Chuong Hoang. “Features identification and tracking for an autonomous ground vehicle.” 2013. Web. 26 Oct 2020.

Vancouver:

Nguyen CH. Features identification and tracking for an autonomous ground vehicle. [Internet] [Masters thesis]. Virginia Tech; 2013. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/10919/33127.

Council of Science Editors:

Nguyen CH. Features identification and tracking for an autonomous ground vehicle. [Masters Thesis]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/33127


Universiteit Utrecht

12. 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 October 26, 2020. 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. 26 Oct 2020.

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 2020 Oct 26]. 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


Linköping University

13. Nyström, Axel. Evaluation of Multiple Object Tracking in Surveillance Video.

Degree: Computer Vision, 2019, Linköping University

  Multiple object tracking is the process of assigning unique and consistent identities to objects throughout a video sequence. A popular approach to multiple object(more)

Subjects/Keywords: Multiple Object Tracking; Tracking-by-Detection; Object Detection; Object Tracking; Deep Learning; Signal Processing; Signalbehandling

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

APA (6th Edition):

Nyström, A. (2019). Evaluation of Multiple Object Tracking in Surveillance Video. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157666

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):

Nyström, Axel. “Evaluation of Multiple Object Tracking in Surveillance Video.” 2019. Thesis, Linköping University. Accessed October 26, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157666.

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

MLA Handbook (7th Edition):

Nyström, Axel. “Evaluation of Multiple Object Tracking in Surveillance Video.” 2019. Web. 26 Oct 2020.

Vancouver:

Nyström A. Evaluation of Multiple Object Tracking in Surveillance Video. [Internet] [Thesis]. Linköping University; 2019. [cited 2020 Oct 26]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157666.

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

Council of Science Editors:

Nyström A. Evaluation of Multiple Object Tracking in Surveillance Video. [Thesis]. Linköping University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157666

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


Australian National University

14. 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 October 26, 2020. 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. 26 Oct 2020.

Vancouver:

Zhang H. Learning to Generate and Refine Object Proposals . [Internet] [Thesis]. Australian National University; 2018. [cited 2020 Oct 26]. 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


NSYSU

15. Liu, Jia-Ying. Real-time Face Recognition System with Self-learning.

Degree: Master, Computer Science and Engineering, 2017, NSYSU

 Face recognition has matured over the recent years, it is widely used in access control system, immigration control system even lecture attendance system. Traditional face… (more)

Subjects/Keywords: Face recognition; Face detection; Skin color detection; Moving object detection; Object tracking

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

APA (6th Edition):

Liu, J. (2017). Real-time Face Recognition System with Self-learning. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0426117-093435

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):

Liu, Jia-Ying. “Real-time Face Recognition System with Self-learning.” 2017. Thesis, NSYSU. Accessed October 26, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0426117-093435.

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

MLA Handbook (7th Edition):

Liu, Jia-Ying. “Real-time Face Recognition System with Self-learning.” 2017. Web. 26 Oct 2020.

Vancouver:

Liu J. Real-time Face Recognition System with Self-learning. [Internet] [Thesis]. NSYSU; 2017. [cited 2020 Oct 26]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0426117-093435.

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

Council of Science Editors:

Liu J. Real-time Face Recognition System with Self-learning. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0426117-093435

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


Texas A&M University

16. Venugopal, Lakshmi. Real-Time Detection of Foreground in Video Surveillance Cameras Using CUDA.

Degree: MS, Electrical Engineering, 2018, Texas A&M University

 The rapid growth of video processing techniques has led to remarkable contributions in several applications such as compression, filtering, segmentation and object tracking. A fundamental… (more)

Subjects/Keywords: Moving object detection; GPU; CUDA; OpenCV

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

APA (6th Edition):

Venugopal, L. (2018). Real-Time Detection of Foreground in Video Surveillance Cameras Using CUDA. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/173600

Chicago Manual of Style (16th Edition):

Venugopal, Lakshmi. “Real-Time Detection of Foreground in Video Surveillance Cameras Using CUDA.” 2018. Masters Thesis, Texas A&M University. Accessed October 26, 2020. http://hdl.handle.net/1969.1/173600.

MLA Handbook (7th Edition):

Venugopal, Lakshmi. “Real-Time Detection of Foreground in Video Surveillance Cameras Using CUDA.” 2018. Web. 26 Oct 2020.

Vancouver:

Venugopal L. Real-Time Detection of Foreground in Video Surveillance Cameras Using CUDA. [Internet] [Masters thesis]. Texas A&M University; 2018. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/1969.1/173600.

Council of Science Editors:

Venugopal L. Real-Time Detection of Foreground in Video Surveillance Cameras Using CUDA. [Masters Thesis]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/173600


University of Waterloo

17. Mozifian, Melissa Farinaz. Real-time 3D Object Detection for Autonomous Driving.

Degree: 2018, University of Waterloo

 This thesis focuses on advancing the state-of-the-art 3D object detection and localization in autonomous driving. An autonomous vehicle requires operating within a very unpredictable and… (more)

Subjects/Keywords: Computer Vision; Object Detection; Deep Learning

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

APA (6th Edition):

Mozifian, M. F. (2018). Real-time 3D Object Detection for Autonomous Driving. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/13267

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):

Mozifian, Melissa Farinaz. “Real-time 3D Object Detection for Autonomous Driving.” 2018. Thesis, University of Waterloo. Accessed October 26, 2020. http://hdl.handle.net/10012/13267.

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

MLA Handbook (7th Edition):

Mozifian, Melissa Farinaz. “Real-time 3D Object Detection for Autonomous Driving.” 2018. Web. 26 Oct 2020.

Vancouver:

Mozifian MF. Real-time 3D Object Detection for Autonomous Driving. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/10012/13267.

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

Council of Science Editors:

Mozifian MF. Real-time 3D Object Detection for Autonomous Driving. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/13267

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


Victoria University of Wellington

18. Rahman, Ibrahim Mohammad Hussain. Visual attention strategies for target object detection.

Degree: 2018, Victoria University of Wellington

 The human visual attention system (HVA) encompasses a set of interconnected neurological modules that are responsible for analyzing visual stimuli by attending to those regions… (more)

Subjects/Keywords: Attention; Object detection; Saliency; Active vision

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

APA (6th Edition):

Rahman, I. M. H. (2018). Visual attention strategies for target object detection. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/6925

Chicago Manual of Style (16th Edition):

Rahman, Ibrahim Mohammad Hussain. “Visual attention strategies for target object detection.” 2018. Doctoral Dissertation, Victoria University of Wellington. Accessed October 26, 2020. http://hdl.handle.net/10063/6925.

MLA Handbook (7th Edition):

Rahman, Ibrahim Mohammad Hussain. “Visual attention strategies for target object detection.” 2018. Web. 26 Oct 2020.

Vancouver:

Rahman IMH. Visual attention strategies for target object detection. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2018. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/10063/6925.

Council of Science Editors:

Rahman IMH. Visual attention strategies for target object detection. [Doctoral Dissertation]. Victoria University of Wellington; 2018. Available from: http://hdl.handle.net/10063/6925


University of Toronto

19. Lo, Charles. A High-performance Architecture for Training Viola-Jones Object Detectors.

Degree: 2012, University of Toronto

The object detection framework developed by Viola and Jones has become very popular due to its high quality and detection speed. However, the complexity of… (more)

Subjects/Keywords: FPGA; Object Detection; Reconfigurable Architecture; 0544; 0800

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

APA (6th Edition):

Lo, C. (2012). A High-performance Architecture for Training Viola-Jones Object Detectors. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/33294

Chicago Manual of Style (16th Edition):

Lo, Charles. “A High-performance Architecture for Training Viola-Jones Object Detectors.” 2012. Masters Thesis, University of Toronto. Accessed October 26, 2020. http://hdl.handle.net/1807/33294.

MLA Handbook (7th Edition):

Lo, Charles. “A High-performance Architecture for Training Viola-Jones Object Detectors.” 2012. Web. 26 Oct 2020.

Vancouver:

Lo C. A High-performance Architecture for Training Viola-Jones Object Detectors. [Internet] [Masters thesis]. University of Toronto; 2012. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/1807/33294.

Council of Science Editors:

Lo C. A High-performance Architecture for Training Viola-Jones Object Detectors. [Masters Thesis]. University of Toronto; 2012. Available from: http://hdl.handle.net/1807/33294


University of Toronto

20. Yoon, Juny David. Model-free Setting-independent Detection of Dynamic Objects in 3D Lidar.

Degree: 2019, University of Toronto

This thesis presents a model-free, setting-independent method for online detection of dynamic objects in 3D lidar data. We focus on a common type of 3D… (more)

Subjects/Keywords: Lidar; Lidar Odometry; Object Detection; 0771

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APA (6th Edition):

Yoon, J. D. (2019). Model-free Setting-independent Detection of Dynamic Objects in 3D Lidar. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/94034

Chicago Manual of Style (16th Edition):

Yoon, Juny David. “Model-free Setting-independent Detection of Dynamic Objects in 3D Lidar.” 2019. Masters Thesis, University of Toronto. Accessed October 26, 2020. http://hdl.handle.net/1807/94034.

MLA Handbook (7th Edition):

Yoon, Juny David. “Model-free Setting-independent Detection of Dynamic Objects in 3D Lidar.” 2019. Web. 26 Oct 2020.

Vancouver:

Yoon JD. Model-free Setting-independent Detection of Dynamic Objects in 3D Lidar. [Internet] [Masters thesis]. University of Toronto; 2019. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/1807/94034.

Council of Science Editors:

Yoon JD. Model-free Setting-independent Detection of Dynamic Objects in 3D Lidar. [Masters Thesis]. University of Toronto; 2019. Available from: http://hdl.handle.net/1807/94034


Victoria University of Wellington

21. Afzali Vahed Moghaddam, Shima. Evolutionary Computation for Feature Manipulation in Salient Object Detection.

Degree: 2020, Victoria University of Wellington

 The human visual system can efficiently cope with complex natural scenes containing various objects at different scales using the visual attention mechanism. Salient object detection(more)

Subjects/Keywords: Salient object detection; Evolutionary computation; Feature manipulation

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APA (6th Edition):

Afzali Vahed Moghaddam, S. (2020). Evolutionary Computation for Feature Manipulation in Salient Object Detection. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/8897

Chicago Manual of Style (16th Edition):

Afzali Vahed Moghaddam, Shima. “Evolutionary Computation for Feature Manipulation in Salient Object Detection.” 2020. Doctoral Dissertation, Victoria University of Wellington. Accessed October 26, 2020. http://hdl.handle.net/10063/8897.

MLA Handbook (7th Edition):

Afzali Vahed Moghaddam, Shima. “Evolutionary Computation for Feature Manipulation in Salient Object Detection.” 2020. Web. 26 Oct 2020.

Vancouver:

Afzali Vahed Moghaddam S. Evolutionary Computation for Feature Manipulation in Salient Object Detection. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2020. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/10063/8897.

Council of Science Editors:

Afzali Vahed Moghaddam S. Evolutionary Computation for Feature Manipulation in Salient Object Detection. [Doctoral Dissertation]. Victoria University of Wellington; 2020. Available from: http://hdl.handle.net/10063/8897


Colorado State University

22. Bolme, David Scott. Theory and applications of optimized correlation output filters.

Degree: PhD, Computer Science, 2011, Colorado State University

 Correlation filters are a standard way to solve many problems in signal processing, image processing, and computer vision. This research introduces two new filter training… (more)

Subjects/Keywords: computer vision; object detection; correlation filters

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APA (6th Edition):

Bolme, D. S. (2011). Theory and applications of optimized correlation output filters. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/47326

Chicago Manual of Style (16th Edition):

Bolme, David Scott. “Theory and applications of optimized correlation output filters.” 2011. Doctoral Dissertation, Colorado State University. Accessed October 26, 2020. http://hdl.handle.net/10217/47326.

MLA Handbook (7th Edition):

Bolme, David Scott. “Theory and applications of optimized correlation output filters.” 2011. Web. 26 Oct 2020.

Vancouver:

Bolme DS. Theory and applications of optimized correlation output filters. [Internet] [Doctoral dissertation]. Colorado State University; 2011. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/10217/47326.

Council of Science Editors:

Bolme DS. Theory and applications of optimized correlation output filters. [Doctoral Dissertation]. Colorado State University; 2011. Available from: http://hdl.handle.net/10217/47326


Iowa State University

23. Vaddi, Subrahmanyam. Efficient object detection model for real-time UAV applications.

Degree: 2019, Iowa State University

 Unmanned Aerial Vehicles (UAVs) especially drones, equipped with vision techniques have become very popular in recent years, with their extensive use in wide range of… (more)

Subjects/Keywords: Deep Learning; Object Detection; Computer Sciences

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

APA (6th Edition):

Vaddi, S. (2019). Efficient object detection model for real-time UAV applications. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/17592

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):

Vaddi, Subrahmanyam. “Efficient object detection model for real-time UAV applications.” 2019. Thesis, Iowa State University. Accessed October 26, 2020. https://lib.dr.iastate.edu/etd/17592.

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

MLA Handbook (7th Edition):

Vaddi, Subrahmanyam. “Efficient object detection model for real-time UAV applications.” 2019. Web. 26 Oct 2020.

Vancouver:

Vaddi S. Efficient object detection model for real-time UAV applications. [Internet] [Thesis]. Iowa State University; 2019. [cited 2020 Oct 26]. Available from: https://lib.dr.iastate.edu/etd/17592.

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

Council of Science Editors:

Vaddi S. Efficient object detection model for real-time UAV applications. [Thesis]. Iowa State University; 2019. Available from: https://lib.dr.iastate.edu/etd/17592

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


University of Technology, Sydney

24. Kale, Anup Vasant. Object detection in dynamic environmental conditions using evolutionary multimodal approach.

Degree: 2018, University of Technology, Sydney

 Environmental dynamism and uncertainty can play a critical role in many problems involving camera-based detection of real-life objects. Uncertainty is witnessed due to the presence… (more)

Subjects/Keywords: Object detection; Multimodal approach; Multimodal image

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APA (6th Edition):

Kale, A. V. (2018). Object detection in dynamic environmental conditions using evolutionary multimodal approach. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/129352

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):

Kale, Anup Vasant. “Object detection in dynamic environmental conditions using evolutionary multimodal approach.” 2018. Thesis, University of Technology, Sydney. Accessed October 26, 2020. http://hdl.handle.net/10453/129352.

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

MLA Handbook (7th Edition):

Kale, Anup Vasant. “Object detection in dynamic environmental conditions using evolutionary multimodal approach.” 2018. Web. 26 Oct 2020.

Vancouver:

Kale AV. Object detection in dynamic environmental conditions using evolutionary multimodal approach. [Internet] [Thesis]. University of Technology, Sydney; 2018. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/10453/129352.

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

Council of Science Editors:

Kale AV. Object detection in dynamic environmental conditions using evolutionary multimodal approach. [Thesis]. University of Technology, Sydney; 2018. Available from: http://hdl.handle.net/10453/129352

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


Delft University of Technology

25. GAO, Xinyu (author). Sensor Data Fusion of Lidar and Camera for Road User Detection.

Degree: 2018, Delft University of Technology

Object detection is one of the most important research topics in autonomous vehicles. The detection systems of autonomous vehicles nowadays are mostly image-based ones which… (more)

Subjects/Keywords: 3D object detection; Lidar; Camera; sensor fusion

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APA (6th Edition):

GAO, X. (. (2018). Sensor Data Fusion of Lidar and Camera for Road User Detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a

Chicago Manual of Style (16th Edition):

GAO, Xinyu (author). “Sensor Data Fusion of Lidar and Camera for Road User Detection.” 2018. Masters Thesis, Delft University of Technology. Accessed October 26, 2020. http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a.

MLA Handbook (7th Edition):

GAO, Xinyu (author). “Sensor Data Fusion of Lidar and Camera for Road User Detection.” 2018. Web. 26 Oct 2020.

Vancouver:

GAO X(. Sensor Data Fusion of Lidar and Camera for Road User Detection. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Oct 26]. Available from: http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a.

Council of Science Editors:

GAO X(. Sensor Data Fusion of Lidar and Camera for Road User Detection. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a


King Abdullah University of Science and Technology

26. 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 October 26, 2020. 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. 26 Oct 2020.

Vancouver:

Lao D. Minimum Delay Moving Object Detection. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2017. [cited 2020 Oct 26]. 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


NSYSU

27. Muchtar, Kahlil. Background Modeling with Applications in Video Surveillance Systems.

Degree: PhD, Electrical Engineering, 2017, NSYSU

 Video Surveillance has been a very active research topic in recent years. This is because the growing needs in many applications, such as object recognition,… (more)

Subjects/Keywords: background modeling; Markov Random Field; abandoned object detection; mixture of Gaussians; Moving object detection

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APA (6th Edition):

Muchtar, K. (2017). Background Modeling with Applications in Video Surveillance Systems. (Doctoral Dissertation). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0405117-193911

Chicago Manual of Style (16th Edition):

Muchtar, Kahlil. “Background Modeling with Applications in Video Surveillance Systems.” 2017. Doctoral Dissertation, NSYSU. Accessed October 26, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0405117-193911.

MLA Handbook (7th Edition):

Muchtar, Kahlil. “Background Modeling with Applications in Video Surveillance Systems.” 2017. Web. 26 Oct 2020.

Vancouver:

Muchtar K. Background Modeling with Applications in Video Surveillance Systems. [Internet] [Doctoral dissertation]. NSYSU; 2017. [cited 2020 Oct 26]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0405117-193911.

Council of Science Editors:

Muchtar K. Background Modeling with Applications in Video Surveillance Systems. [Doctoral Dissertation]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0405117-193911


Mississippi State University

28. Thomas, Mark Dewayne. POLICE CAR VISIBILITY: THE RELATIONSHIP BETWEEN DETECTION, CATEGORIZATION AND VISUAL SALIENCY.

Degree: PhD, Psychology, 2012, Mississippi State University

  Perceptual categorization involves integrating bottom-up sensory information with top-down knowledge which is based on prior experience. Bottom-up information comes from the external world and… (more)

Subjects/Keywords: police car; categorization; detection; visual saliency; perceptual categorization; object detection; object categorization

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APA (6th Edition):

Thomas, M. D. (2012). POLICE CAR VISIBILITY: THE RELATIONSHIP BETWEEN DETECTION, CATEGORIZATION AND VISUAL SALIENCY. (Doctoral Dissertation). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-04042012-112903/ ;

Chicago Manual of Style (16th Edition):

Thomas, Mark Dewayne. “POLICE CAR VISIBILITY: THE RELATIONSHIP BETWEEN DETECTION, CATEGORIZATION AND VISUAL SALIENCY.” 2012. Doctoral Dissertation, Mississippi State University. Accessed October 26, 2020. http://sun.library.msstate.edu/ETD-db/theses/available/etd-04042012-112903/ ;.

MLA Handbook (7th Edition):

Thomas, Mark Dewayne. “POLICE CAR VISIBILITY: THE RELATIONSHIP BETWEEN DETECTION, CATEGORIZATION AND VISUAL SALIENCY.” 2012. Web. 26 Oct 2020.

Vancouver:

Thomas MD. POLICE CAR VISIBILITY: THE RELATIONSHIP BETWEEN DETECTION, CATEGORIZATION AND VISUAL SALIENCY. [Internet] [Doctoral dissertation]. Mississippi State University; 2012. [cited 2020 Oct 26]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-04042012-112903/ ;.

Council of Science Editors:

Thomas MD. POLICE CAR VISIBILITY: THE RELATIONSHIP BETWEEN DETECTION, CATEGORIZATION AND VISUAL SALIENCY. [Doctoral Dissertation]. Mississippi State University; 2012. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-04042012-112903/ ;


Linköping University

29. Tydén, Amanda. Edge Machine Learning for Animal Detection, Classification, and Tracking.

Degree: Automatic Control, 2020, Linköping University

  A research field currently advancing is the use of machine learning on camera trap data, yet few explore deep learning for camera traps to… (more)

Subjects/Keywords: computer vision; object detection; object tracking; edge machine learning; animal detection; Control Engineering; Reglerteknik

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APA (6th Edition):

Tydén, A. (2020). Edge Machine Learning for Animal Detection, Classification, and Tracking. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166572

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):

Tydén, Amanda. “Edge Machine Learning for Animal Detection, Classification, and Tracking.” 2020. Thesis, Linköping University. Accessed October 26, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166572.

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

MLA Handbook (7th Edition):

Tydén, Amanda. “Edge Machine Learning for Animal Detection, Classification, and Tracking.” 2020. Web. 26 Oct 2020.

Vancouver:

Tydén A. Edge Machine Learning for Animal Detection, Classification, and Tracking. [Internet] [Thesis]. Linköping University; 2020. [cited 2020 Oct 26]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166572.

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

Council of Science Editors:

Tydén A. Edge Machine Learning for Animal Detection, Classification, and Tracking. [Thesis]. Linköping University; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166572

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


University of Edinburgh

30. Gonzalez-Garcia, Abel. Image context for object detection, object context for part detection.

Degree: PhD, 2018, University of Edinburgh

 Objects and parts are crucial elements for achieving automatic image understanding. The goal of the object detection task is to recognize and localize all the… (more)

Subjects/Keywords: object detection; automatic image; object class detection; window classifiers; convolutional neural networks

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APA (6th Edition):

Gonzalez-Garcia, A. (2018). Image context for object detection, object context for part detection. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/28842

Chicago Manual of Style (16th Edition):

Gonzalez-Garcia, Abel. “Image context for object detection, object context for part detection.” 2018. Doctoral Dissertation, University of Edinburgh. Accessed October 26, 2020. http://hdl.handle.net/1842/28842.

MLA Handbook (7th Edition):

Gonzalez-Garcia, Abel. “Image context for object detection, object context for part detection.” 2018. Web. 26 Oct 2020.

Vancouver:

Gonzalez-Garcia A. Image context for object detection, object context for part detection. [Internet] [Doctoral dissertation]. University of Edinburgh; 2018. [cited 2020 Oct 26]. Available from: http://hdl.handle.net/1842/28842.

Council of Science Editors:

Gonzalez-Garcia A. Image context for object detection, object context for part detection. [Doctoral Dissertation]. University of Edinburgh; 2018. Available from: http://hdl.handle.net/1842/28842

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