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You searched for +publisher:"University of Southern California" +contributor:("Nevatia, Ramakant"). Showing records 1 – 26 of 26 total matches.

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University of Southern California

1. Ranasinghe, Nadeesha Oliver. Learning to detect and adapt to unpredicted changes.

Degree: PhD, Computer Science, 2012, University of Southern California

 To survive in the real world, a robot must be able to intelligently react to unpredicted and possibly simultaneous changes to its self (such as… (more)

Subjects/Keywords: learning; surprise; predictive modelling; developmental learning; robotics; artificial intelligence; structure learning; uninterpreted sensors; adapting to change; unpredicted changes; unpredictable changes; interference; autonomous navigation; autonomous surveillance

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APA (6th Edition):

Ranasinghe, N. O. (2012). Learning to detect and adapt to unpredicted changes. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/97440/rec/3794

Chicago Manual of Style (16th Edition):

Ranasinghe, Nadeesha Oliver. “Learning to detect and adapt to unpredicted changes.” 2012. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/97440/rec/3794.

MLA Handbook (7th Edition):

Ranasinghe, Nadeesha Oliver. “Learning to detect and adapt to unpredicted changes.” 2012. Web. 09 May 2021.

Vancouver:

Ranasinghe NO. Learning to detect and adapt to unpredicted changes. [Internet] [Doctoral dissertation]. University of Southern California; 2012. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/97440/rec/3794.

Council of Science Editors:

Ranasinghe NO. Learning to detect and adapt to unpredicted changes. [Doctoral Dissertation]. University of Southern California; 2012. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/97440/rec/3794


University of Southern California

2. Everist, Jacob Spencer. Robot mapping with proprioceptive spatial awareness in confined and sensor-challenged environments.

Degree: PhD, Computer Science, 2015, University of Southern California

 In many real-world environments such as flooded pipes or caves, exteroceptive sensors, such as vision, range or touch, often fail to give any useful information… (more)

Subjects/Keywords: proprioception; robotics; tactile sensing; touch sensing; mapping; snake robot; underground; pipes; sensors

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APA (6th Edition):

Everist, J. S. (2015). Robot mapping with proprioceptive spatial awareness in confined and sensor-challenged environments. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/566721/rec/5612

Chicago Manual of Style (16th Edition):

Everist, Jacob Spencer. “Robot mapping with proprioceptive spatial awareness in confined and sensor-challenged environments.” 2015. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/566721/rec/5612.

MLA Handbook (7th Edition):

Everist, Jacob Spencer. “Robot mapping with proprioceptive spatial awareness in confined and sensor-challenged environments.” 2015. Web. 09 May 2021.

Vancouver:

Everist JS. Robot mapping with proprioceptive spatial awareness in confined and sensor-challenged environments. [Internet] [Doctoral dissertation]. University of Southern California; 2015. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/566721/rec/5612.

Council of Science Editors:

Everist JS. Robot mapping with proprioceptive spatial awareness in confined and sensor-challenged environments. [Doctoral Dissertation]. University of Southern California; 2015. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/566721/rec/5612


University of Southern California

3. Prokaj, Jan. Exploitation of wide area motion imagery.

Degree: PhD, Computer Science, 2013, University of Southern California

 Current digital photography solutions now routinely allow the capture of tens of megapixels of data at 2 frames per second. At these resolutions, a geographic… (more)

Subjects/Keywords: computer vision; aerial imagery; aerial surveillance; unmanned aerial vehicle

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APA (6th Edition):

Prokaj, J. (2013). Exploitation of wide area motion imagery. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/307102/rec/2645

Chicago Manual of Style (16th Edition):

Prokaj, Jan. “Exploitation of wide area motion imagery.” 2013. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/307102/rec/2645.

MLA Handbook (7th Edition):

Prokaj, Jan. “Exploitation of wide area motion imagery.” 2013. Web. 09 May 2021.

Vancouver:

Prokaj J. Exploitation of wide area motion imagery. [Internet] [Doctoral dissertation]. University of Southern California; 2013. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/307102/rec/2645.

Council of Science Editors:

Prokaj J. Exploitation of wide area motion imagery. [Doctoral Dissertation]. University of Southern California; 2013. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/307102/rec/2645


University of Southern California

4. Kang, Jeon-Hyung. Modeling social and cognitive aspects of user behavior in social media.

Degree: PhD, Computer Science, 2015, University of Southern California

 The spread of information in an online social network is a complex process that depends on the nature of information, the structure of the network,… (more)

Subjects/Keywords: probabilistic models; recommendation system; modeling information adoption; understanding information diffusion; user behavior; social media; social network; role of network structure; user effort

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APA (6th Edition):

Kang, J. (2015). Modeling social and cognitive aspects of user behavior in social media. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/603277/rec/4149

Chicago Manual of Style (16th Edition):

Kang, Jeon-Hyung. “Modeling social and cognitive aspects of user behavior in social media.” 2015. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/603277/rec/4149.

MLA Handbook (7th Edition):

Kang, Jeon-Hyung. “Modeling social and cognitive aspects of user behavior in social media.” 2015. Web. 09 May 2021.

Vancouver:

Kang J. Modeling social and cognitive aspects of user behavior in social media. [Internet] [Doctoral dissertation]. University of Southern California; 2015. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/603277/rec/4149.

Council of Science Editors:

Kang J. Modeling social and cognitive aspects of user behavior in social media. [Doctoral Dissertation]. University of Southern California; 2015. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/603277/rec/4149


University of Southern California

5. Kuo, Cheng-Hao. Multiple pedestrians tracking by discriminative models.

Degree: PhD, Electrical Engineering, 2011, University of Southern California

 We present our work on multiple pedestrians tracking in a single camera and across multiple non-overlapping cameras. We propose an approach for online learning of… (more)

Subjects/Keywords: adaboost; association-based tracking; detection-based tracking; discriminative models; multiple instance learning; mutli-target tracking

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APA (6th Edition):

Kuo, C. (2011). Multiple pedestrians tracking by discriminative models. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/671069/rec/4282

Chicago Manual of Style (16th Edition):

Kuo, Cheng-Hao. “Multiple pedestrians tracking by discriminative models.” 2011. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/671069/rec/4282.

MLA Handbook (7th Edition):

Kuo, Cheng-Hao. “Multiple pedestrians tracking by discriminative models.” 2011. Web. 09 May 2021.

Vancouver:

Kuo C. Multiple pedestrians tracking by discriminative models. [Internet] [Doctoral dissertation]. University of Southern California; 2011. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/671069/rec/4282.

Council of Science Editors:

Kuo C. Multiple pedestrians tracking by discriminative models. [Doctoral Dissertation]. University of Southern California; 2011. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/671069/rec/4282


University of Southern California

6. Noronha, Sanjay P. 3-D building detection and description from multiple intensity images using hierarchical grouping and matching of features.

Degree: PhD, Computer Science, 2013, University of Southern California

 A method for detection and description of rectangular buildings with flat and with gable roofs from two or more registered aerial intensity images is proposed.… (more)

Subjects/Keywords: computer vision; automated building detection

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APA (6th Edition):

Noronha, S. P. (2013). 3-D building detection and description from multiple intensity images using hierarchical grouping and matching of features. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/218764/rec/16

Chicago Manual of Style (16th Edition):

Noronha, Sanjay P. “3-D building detection and description from multiple intensity images using hierarchical grouping and matching of features.” 2013. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/218764/rec/16.

MLA Handbook (7th Edition):

Noronha, Sanjay P. “3-D building detection and description from multiple intensity images using hierarchical grouping and matching of features.” 2013. Web. 09 May 2021.

Vancouver:

Noronha SP. 3-D building detection and description from multiple intensity images using hierarchical grouping and matching of features. [Internet] [Doctoral dissertation]. University of Southern California; 2013. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/218764/rec/16.

Council of Science Editors:

Noronha SP. 3-D building detection and description from multiple intensity images using hierarchical grouping and matching of features. [Doctoral Dissertation]. University of Southern California; 2013. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/218764/rec/16


University of Southern California

7. Khan, Furqan Muhammad. Analyzing human activities in videos using component based models.

Degree: PhD, Computer Science, 2013, University of Southern California

 With cameras getting smaller, better and cheaper, the amount of videos produced these days has increased exponentially. Although not comprehensive by any means, the fact… (more)

Subjects/Keywords: computer vision; human activity analysis; machine learning; artificial intelligence; surveillance systems; graphical networks for action analysis

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APA (6th Edition):

Khan, F. M. (2013). Analyzing human activities in videos using component based models. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/300165/rec/823

Chicago Manual of Style (16th Edition):

Khan, Furqan Muhammad. “Analyzing human activities in videos using component based models.” 2013. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/300165/rec/823.

MLA Handbook (7th Edition):

Khan, Furqan Muhammad. “Analyzing human activities in videos using component based models.” 2013. Web. 09 May 2021.

Vancouver:

Khan FM. Analyzing human activities in videos using component based models. [Internet] [Doctoral dissertation]. University of Southern California; 2013. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/300165/rec/823.

Council of Science Editors:

Khan FM. Analyzing human activities in videos using component based models. [Doctoral Dissertation]. University of Southern California; 2013. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/300165/rec/823


University of Southern California

8. Yang, Bo. Multiple humnas tracking by learning appearance and motion patterns.

Degree: PhD, Computer Science, 2012, University of Southern California

 Tracking multiple humans in real scenes is an important problem in computer vision due to its importance for many applications, such as surveillance, robotics, and… (more)

Subjects/Keywords: multi-target tracking; appearance and motion patterns

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APA (6th Edition):

Yang, B. (2012). Multiple humnas tracking by learning appearance and motion patterns. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/77404/rec/4278

Chicago Manual of Style (16th Edition):

Yang, Bo. “Multiple humnas tracking by learning appearance and motion patterns.” 2012. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/77404/rec/4278.

MLA Handbook (7th Edition):

Yang, Bo. “Multiple humnas tracking by learning appearance and motion patterns.” 2012. Web. 09 May 2021.

Vancouver:

Yang B. Multiple humnas tracking by learning appearance and motion patterns. [Internet] [Doctoral dissertation]. University of Southern California; 2012. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/77404/rec/4278.

Council of Science Editors:

Yang B. Multiple humnas tracking by learning appearance and motion patterns. [Doctoral Dissertation]. University of Southern California; 2012. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/77404/rec/4278


University of Southern California

9. 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 (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/2189

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 May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/489008/rec/2189.

MLA Handbook (7th Edition):

Sharma, Pramod Kumar. “Effective incremental learning and detector adaptation methods for video object detection.” 2014. Web. 09 May 2021.

Vancouver:

Sharma PK. Effective incremental learning and detector adaptation methods for video object detection. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/489008/rec/2189.

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/2189


University of Southern California

10. Singh, Vivek Kumar. Monocular human pose tracking and action recognition in dynamic environments.

Degree: PhD, Computer Science, 2011, University of Southern California

 The objective of this work is to develop an efficient method to find human in videos captured from a single camera, and recognize the action… (more)

Subjects/Keywords: pictorial structures; branch and bound; conditional random fields; particle filtering

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APA (6th Edition):

Singh, V. K. (2011). Monocular human pose tracking and action recognition in dynamic environments. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/175429/rec/4216

Chicago Manual of Style (16th Edition):

Singh, Vivek Kumar. “Monocular human pose tracking and action recognition in dynamic environments.” 2011. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/175429/rec/4216.

MLA Handbook (7th Edition):

Singh, Vivek Kumar. “Monocular human pose tracking and action recognition in dynamic environments.” 2011. Web. 09 May 2021.

Vancouver:

Singh VK. Monocular human pose tracking and action recognition in dynamic environments. [Internet] [Doctoral dissertation]. University of Southern California; 2011. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/175429/rec/4216.

Council of Science Editors:

Singh VK. Monocular human pose tracking and action recognition in dynamic environments. [Doctoral Dissertation]. University of Southern California; 2011. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/175429/rec/4216


University of Southern California

11. Banerjee, Prithviraj. Incorporating aggregate feature statistics in structured dynamical models for human activity recognition.

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

 Human action recognition in videos is a central problem of computer vision, with numerous applications in the fields of video surveillance, data mining and human… (more)

Subjects/Keywords: computer vision; machine learning; human activity recognition; activity detection; graphical models

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APA (6th Edition):

Banerjee, P. (2014). Incorporating aggregate feature statistics in structured dynamical models for human activity recognition. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/483796/rec/3444

Chicago Manual of Style (16th Edition):

Banerjee, Prithviraj. “Incorporating aggregate feature statistics in structured dynamical models for human activity recognition.” 2014. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/483796/rec/3444.

MLA Handbook (7th Edition):

Banerjee, Prithviraj. “Incorporating aggregate feature statistics in structured dynamical models for human activity recognition.” 2014. Web. 09 May 2021.

Vancouver:

Banerjee P. Incorporating aggregate feature statistics in structured dynamical models for human activity recognition. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/483796/rec/3444.

Council of Science Editors:

Banerjee P. Incorporating aggregate feature statistics in structured dynamical models for human activity recognition. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/483796/rec/3444


University of Southern California

12. Wu, Bo. Part based object detection, segmentation, and tracking by boosting simple shape feature based weak classifiers.

Degree: PhD, Computer Science, 2008, University of Southern California

 Detection, segmentation, and tracking of objects of a known class is a fundamental problem in computer vision. For this task, we need to first detect… (more)

Subjects/Keywords: object detection and tracking; AdaBoost

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APA (6th Edition):

Wu, B. (2008). Part based object detection, segmentation, and tracking by boosting simple shape feature based weak classifiers. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/75931/rec/4932

Chicago Manual of Style (16th Edition):

Wu, Bo. “Part based object detection, segmentation, and tracking by boosting simple shape feature based weak classifiers.” 2008. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/75931/rec/4932.

MLA Handbook (7th Edition):

Wu, Bo. “Part based object detection, segmentation, and tracking by boosting simple shape feature based weak classifiers.” 2008. Web. 09 May 2021.

Vancouver:

Wu B. Part based object detection, segmentation, and tracking by boosting simple shape feature based weak classifiers. [Internet] [Doctoral dissertation]. University of Southern California; 2008. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/75931/rec/4932.

Council of Science Editors:

Wu B. Part based object detection, segmentation, and tracking by boosting simple shape feature based weak classifiers. [Doctoral Dissertation]. University of Southern California; 2008. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/75931/rec/4932


University of Southern California

13. Pai, Cheng-Hua Jeff. Moving object detection on a runway prior to landing using an onboard infrared camera.

Degree: MS, Computer Science (Multimedia & Creative Technologies), 2007, University of Southern California

 Determining the status of a runway prior to landing is essential for any aircraft, whether manned or unmanned. In this thesis, we present a method… (more)

Subjects/Keywords: object detection onboard infrared camera

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APA (6th Edition):

Pai, C. J. (2007). Moving object detection on a runway prior to landing using an onboard infrared camera. (Masters Thesis). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/478587/rec/4248

Chicago Manual of Style (16th Edition):

Pai, Cheng-Hua Jeff. “Moving object detection on a runway prior to landing using an onboard infrared camera.” 2007. Masters Thesis, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/478587/rec/4248.

MLA Handbook (7th Edition):

Pai, Cheng-Hua Jeff. “Moving object detection on a runway prior to landing using an onboard infrared camera.” 2007. Web. 09 May 2021.

Vancouver:

Pai CJ. Moving object detection on a runway prior to landing using an onboard infrared camera. [Internet] [Masters thesis]. University of Southern California; 2007. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/478587/rec/4248.

Council of Science Editors:

Pai CJ. Moving object detection on a runway prior to landing using an onboard infrared camera. [Masters Thesis]. University of Southern California; 2007. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/478587/rec/4248


University of Southern California

14. Khatri, Vikash. Intelligent video surveillance using soft biometrics.

Degree: MS, Electrical Engineering, 2010, University of Southern California

 The increasing use of surveillance cameras has generated a need of intelligent surveillance systems, which can identify the events of interest from the long video… (more)

Subjects/Keywords: soft biometric features; surveillance systems; camera calibration; outdoor environment

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APA (6th Edition):

Khatri, V. (2010). Intelligent video surveillance using soft biometrics. (Masters Thesis). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/366167/rec/3566

Chicago Manual of Style (16th Edition):

Khatri, Vikash. “Intelligent video surveillance using soft biometrics.” 2010. Masters Thesis, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/366167/rec/3566.

MLA Handbook (7th Edition):

Khatri, Vikash. “Intelligent video surveillance using soft biometrics.” 2010. Web. 09 May 2021.

Vancouver:

Khatri V. Intelligent video surveillance using soft biometrics. [Internet] [Masters thesis]. University of Southern California; 2010. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/366167/rec/3566.

Council of Science Editors:

Khatri V. Intelligent video surveillance using soft biometrics. [Masters Thesis]. University of Southern California; 2010. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/366167/rec/3566


University of Southern California

15. Jahangiri, Mehrdad. WOLAP: wavelet-based on-line analytical processing.

Degree: PhD, Computer Science, 2008, University of Southern California

 Wavelet Transform has emerged as an elegant tool for online analytical queries. Most of the methods using wavelets, however, share the disadvantage of providing only… (more)

Subjects/Keywords: range aggregate query; OLAP; wavelet transform; scientific data analysis

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APA (6th Edition):

Jahangiri, M. (2008). WOLAP: wavelet-based on-line analytical processing. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/198703/rec/7959

Chicago Manual of Style (16th Edition):

Jahangiri, Mehrdad. “WOLAP: wavelet-based on-line analytical processing.” 2008. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/198703/rec/7959.

MLA Handbook (7th Edition):

Jahangiri, Mehrdad. “WOLAP: wavelet-based on-line analytical processing.” 2008. Web. 09 May 2021.

Vancouver:

Jahangiri M. WOLAP: wavelet-based on-line analytical processing. [Internet] [Doctoral dissertation]. University of Southern California; 2008. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/198703/rec/7959.

Council of Science Editors:

Jahangiri M. WOLAP: wavelet-based on-line analytical processing. [Doctoral Dissertation]. University of Southern California; 2008. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/198703/rec/7959


University of Southern California

16. Yu, Qian. Spatio-temporal probabilistic inference for persistent object detection and tracking.

Degree: PhD, Computer Science, 2009, University of Southern California

 Tracking is a critical component of video analysis, as it provides the description of spatiotemporal relationships between observations and moving objects required by activity recognition… (more)

Subjects/Keywords: multiple target tracking; spatio-temporal inference

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APA (6th Edition):

Yu, Q. (2009). Spatio-temporal probabilistic inference for persistent object detection and tracking. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/148282/rec/5991

Chicago Manual of Style (16th Edition):

Yu, Qian. “Spatio-temporal probabilistic inference for persistent object detection and tracking.” 2009. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/148282/rec/5991.

MLA Handbook (7th Edition):

Yu, Qian. “Spatio-temporal probabilistic inference for persistent object detection and tracking.” 2009. Web. 09 May 2021.

Vancouver:

Yu Q. Spatio-temporal probabilistic inference for persistent object detection and tracking. [Internet] [Doctoral dissertation]. University of Southern California; 2009. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/148282/rec/5991.

Council of Science Editors:

Yu Q. Spatio-temporal probabilistic inference for persistent object detection and tracking. [Doctoral Dissertation]. University of Southern California; 2009. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/148282/rec/5991


University of Southern California

17. Lv, Fengjun. Model based view-invariant human action recognition and segmentation.

Degree: PhD, Computer Science, 2007, University of Southern California

 Recognizing basic human actions such as walking, sitting down and waving hands from a single video is an important task for many applications in video… (more)

Subjects/Keywords: action recognition; human motion analysis

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APA (6th Edition):

Lv, F. (2007). Model based view-invariant human action recognition and segmentation. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/480328/rec/4113

Chicago Manual of Style (16th Edition):

Lv, Fengjun. “Model based view-invariant human action recognition and segmentation.” 2007. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/480328/rec/4113.

MLA Handbook (7th Edition):

Lv, Fengjun. “Model based view-invariant human action recognition and segmentation.” 2007. Web. 09 May 2021.

Vancouver:

Lv F. Model based view-invariant human action recognition and segmentation. [Internet] [Doctoral dissertation]. University of Southern California; 2007. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/480328/rec/4113.

Council of Science Editors:

Lv F. Model based view-invariant human action recognition and segmentation. [Doctoral Dissertation]. University of Southern California; 2007. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/480328/rec/4113


University of Southern California

18. Chu, Chi-Wei. Body pose estimation and gesture recognition for human-computer interaction system.

Degree: PhD, Computer Science, 2008, University of Southern California

 In this thesis we present an approached for a visual communication application for a dark, theater-like interactive virtual simulation training environment. Our system visually estimates… (more)

Subjects/Keywords: computer vision; ICP; HCI; body tracking

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APA (6th Edition):

Chu, C. (2008). Body pose estimation and gesture recognition for human-computer interaction system. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/107307/rec/1150

Chicago Manual of Style (16th Edition):

Chu, Chi-Wei. “Body pose estimation and gesture recognition for human-computer interaction system.” 2008. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/107307/rec/1150.

MLA Handbook (7th Edition):

Chu, Chi-Wei. “Body pose estimation and gesture recognition for human-computer interaction system.” 2008. Web. 09 May 2021.

Vancouver:

Chu C. Body pose estimation and gesture recognition for human-computer interaction system. [Internet] [Doctoral dissertation]. University of Southern California; 2008. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/107307/rec/1150.

Council of Science Editors:

Chu C. Body pose estimation and gesture recognition for human-computer interaction system. [Doctoral Dissertation]. University of Southern California; 2008. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/107307/rec/1150


University of Southern California

19. Liao, Wei-Kai. Facial gesture analysis in an interactive environment.

Degree: PhD, Computer Science, 2008, University of Southern California

 This research focuses on tracking, modeling, quantifying and analyzing facial motions for gesture understanding. Facial gesture analysis is an important problem in computer vision since… (more)

Subjects/Keywords: face; facial gestures; expression recognition; head pose estimation; 3D face tracking; tensor voting; manifold learning; graphical model; face alignment

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APA (6th Edition):

Liao, W. (2008). Facial gesture analysis in an interactive environment. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/106684/rec/2707

Chicago Manual of Style (16th Edition):

Liao, Wei-Kai. “Facial gesture analysis in an interactive environment.” 2008. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/106684/rec/2707.

MLA Handbook (7th Edition):

Liao, Wei-Kai. “Facial gesture analysis in an interactive environment.” 2008. Web. 09 May 2021.

Vancouver:

Liao W. Facial gesture analysis in an interactive environment. [Internet] [Doctoral dissertation]. University of Southern California; 2008. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/106684/rec/2707.

Council of Science Editors:

Liao W. Facial gesture analysis in an interactive environment. [Doctoral Dissertation]. University of Southern California; 2008. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/106684/rec/2707


University of Southern California

20. Natarajan, Pradeep. Robust representation and recognition of actions in video.

Degree: PhD, Computer Science, 2009, University of Southern California

 Recognizing actions from video and other sensory data is important for a number of applications such as surveillance and human-computer interaction. While the potential applications… (more)

Subjects/Keywords: computer vision; action recognition; hidden Markov models; conditional random fields

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APA (6th Edition):

Natarajan, P. (2009). Robust representation and recognition of actions in video. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/562446/rec/5622

Chicago Manual of Style (16th Edition):

Natarajan, Pradeep. “Robust representation and recognition of actions in video.” 2009. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/562446/rec/5622.

MLA Handbook (7th Edition):

Natarajan, Pradeep. “Robust representation and recognition of actions in video.” 2009. Web. 09 May 2021.

Vancouver:

Natarajan P. Robust representation and recognition of actions in video. [Internet] [Doctoral dissertation]. University of Southern California; 2009. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/562446/rec/5622.

Council of Science Editors:

Natarajan P. Robust representation and recognition of actions in video. [Doctoral Dissertation]. University of Southern California; 2009. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/562446/rec/5622


University of Southern California

21. Hu, Jinhui. Integrating complementary information for photorealistic representation of large-scale environments.

Degree: PhD, Computer Science, 2007, University of Southern California

 A wealth of datasets from different sensors exists for environment representation. The key observations of this thesis are that the different datasets are complementary and… (more)

Subjects/Keywords: building modeling; LiDAR; aerial images; ground images

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

APA (6th Edition):

Hu, J. (2007). Integrating complementary information for photorealistic representation of large-scale environments. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/326495/rec/3537

Chicago Manual of Style (16th Edition):

Hu, Jinhui. “Integrating complementary information for photorealistic representation of large-scale environments.” 2007. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/326495/rec/3537.

MLA Handbook (7th Edition):

Hu, Jinhui. “Integrating complementary information for photorealistic representation of large-scale environments.” 2007. Web. 09 May 2021.

Vancouver:

Hu J. Integrating complementary information for photorealistic representation of large-scale environments. [Internet] [Doctoral dissertation]. University of Southern California; 2007. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/326495/rec/3537.

Council of Science Editors:

Hu J. Integrating complementary information for photorealistic representation of large-scale environments. [Doctoral Dissertation]. University of Southern California; 2007. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/326495/rec/3537


University of Southern California

22. Sebe, Ismail Oner. Interactive rapid part-based 3d modeling from a single image and its applications.

Degree: PhD, Electrical Engineering, 2008, University of Southern California

 Commercially available 3D modeling software are often designed for professional artists and engineers. In this thesis, we present a novel image-based modeling framework to rapidly… (more)

Subjects/Keywords: image-based modeling; part-based modeling; single image modeling; part-based detection and learning

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APA (6th Edition):

Sebe, I. O. (2008). Interactive rapid part-based 3d modeling from a single image and its applications. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/591265/rec/3577

Chicago Manual of Style (16th Edition):

Sebe, Ismail Oner. “Interactive rapid part-based 3d modeling from a single image and its applications.” 2008. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/591265/rec/3577.

MLA Handbook (7th Edition):

Sebe, Ismail Oner. “Interactive rapid part-based 3d modeling from a single image and its applications.” 2008. Web. 09 May 2021.

Vancouver:

Sebe IO. Interactive rapid part-based 3d modeling from a single image and its applications. [Internet] [Doctoral dissertation]. University of Southern California; 2008. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/591265/rec/3577.

Council of Science Editors:

Sebe IO. Interactive rapid part-based 3d modeling from a single image and its applications. [Doctoral Dissertation]. University of Southern California; 2008. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/591265/rec/3577


University of Southern California

23. Song, Xuefeng. Multiple vehicle segmentation and tracking in complex environments.

Degree: PhD, Computer Science, University of Southern California

 Our goal is to detect and to track multiple moving vehicles observed from static surveillance cameras, which are usually placed on poles or buildings. Methods… (more)

Subjects/Keywords: computer vision; pattern recognition; vehicle tracking

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APA (6th Edition):

Song, X. (n.d.). Multiple vehicle segmentation and tracking in complex environments. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/322986/rec/4284

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

Chicago Manual of Style (16th Edition):

Song, Xuefeng. “Multiple vehicle segmentation and tracking in complex environments.” Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/322986/rec/4284.

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

MLA Handbook (7th Edition):

Song, Xuefeng. “Multiple vehicle segmentation and tracking in complex environments.” Web. 09 May 2021.

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

Vancouver:

Song X. Multiple vehicle segmentation and tracking in complex environments. [Internet] [Doctoral dissertation]. University of Southern California; [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/322986/rec/4284.

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

Council of Science Editors:

Song X. Multiple vehicle segmentation and tracking in complex environments. [Doctoral Dissertation]. University of Southern California; Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/322986/rec/4284

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


University of Southern California

24. Yuan, Chang. Motion segmentation and dense reconstruction of scenes containing moving objects observed by a moving camera.

Degree: PhD, Computer Science, 2007, University of Southern California

 We investigate two fundamental issues in Computer Vision: 2D motion segmentation and 3D dense shape reconstruction of a dynamic scene observed from a moving camera.… (more)

Subjects/Keywords: motion segmentation; 3d reconstruction

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APA (6th Edition):

Yuan, C. (2007). Motion segmentation and dense reconstruction of scenes containing moving objects observed by a moving camera. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/494232/rec/4231

Chicago Manual of Style (16th Edition):

Yuan, Chang. “Motion segmentation and dense reconstruction of scenes containing moving objects observed by a moving camera.” 2007. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/494232/rec/4231.

MLA Handbook (7th Edition):

Yuan, Chang. “Motion segmentation and dense reconstruction of scenes containing moving objects observed by a moving camera.” 2007. Web. 09 May 2021.

Vancouver:

Yuan C. Motion segmentation and dense reconstruction of scenes containing moving objects observed by a moving camera. [Internet] [Doctoral dissertation]. University of Southern California; 2007. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/494232/rec/4231.

Council of Science Editors:

Yuan C. Motion segmentation and dense reconstruction of scenes containing moving objects observed by a moving camera. [Doctoral Dissertation]. University of Southern California; 2007. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/494232/rec/4231


University of Southern California

25. Wang, Lu. Line segment matching and its applications in 3D urban modeling.

Degree: PhD, Computer Science, 2010, University of Southern California

 Man-made environments are full of line segments, and a complex curve can be approximated with multiple straight-line segments. Therefore, line segment matching is an important… (more)

Subjects/Keywords: image matching; image registration; 3D modeling; urban modeling; wide-baseline

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APA (6th Edition):

Wang, L. (2010). Line segment matching and its applications in 3D urban modeling. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/287762/rec/3832

Chicago Manual of Style (16th Edition):

Wang, Lu. “Line segment matching and its applications in 3D urban modeling.” 2010. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/287762/rec/3832.

MLA Handbook (7th Edition):

Wang, Lu. “Line segment matching and its applications in 3D urban modeling.” 2010. Web. 09 May 2021.

Vancouver:

Wang L. Line segment matching and its applications in 3D urban modeling. [Internet] [Doctoral dissertation]. University of Southern California; 2010. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/287762/rec/3832.

Council of Science Editors:

Wang L. Line segment matching and its applications in 3D urban modeling. [Doctoral Dissertation]. University of Southern California; 2010. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/287762/rec/3832


University of Southern California

26. Siagian, Christian. Biologically inspired mobile robot vision localization.

Degree: PhD, Computer Science (Robotics & Automation), 2009, University of Southern California

 The problem of localization is central to endowing mobile machines with intelligence. Vision is a promising research path because of its versatility and robustness in… (more)

Subjects/Keywords: vision localization; robot localization; saliency; gist; biologically-inspired vision

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APA (6th Edition):

Siagian, C. (2009). Biologically inspired mobile robot vision localization. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/184573/rec/1121

Chicago Manual of Style (16th Edition):

Siagian, Christian. “Biologically inspired mobile robot vision localization.” 2009. Doctoral Dissertation, University of Southern California. Accessed May 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/184573/rec/1121.

MLA Handbook (7th Edition):

Siagian, Christian. “Biologically inspired mobile robot vision localization.” 2009. Web. 09 May 2021.

Vancouver:

Siagian C. Biologically inspired mobile robot vision localization. [Internet] [Doctoral dissertation]. University of Southern California; 2009. [cited 2021 May 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/184573/rec/1121.

Council of Science Editors:

Siagian C. Biologically inspired mobile robot vision localization. [Doctoral Dissertation]. University of Southern California; 2009. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/184573/rec/1121

.