Be an Optimist Prime in the world of Computer Vision and AI
· THE APPLICATION OF COMPUTER VISION, MACHINE AND DEEP LEARNING ALGORITHMS UTILIZING MATLAB Andrea Linda Murphy, University of New Hampshire, Durham Date of Award Spring Project Type Thesis Program or Major Information Technology Degree Name Master of Science First Advisor Mihaela Sabin Second Advisor Karen Jin Third Human Motion Analysis using Computer Vision and Deep Learning Kingston University Faculty of Science, Engineering and Computing Analysis of human motion is implicitly or explicitly required in many areas such as healthcare, sports, video surveillance, body-based user interfaces and computer games and animation. Read more Supervisor: Prof D Makris The memory of the doctoral thesis (2 copies in paper and one electronic version) with this form for the cover. Summaries of the thesis (R form) in English and Catalan or Spanish in Word and in paper, duly signed. Proposal of reviewers (PR form), in word, including links to the cv or cv brief in pdf of the reviewers
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Chronocam is a rapidly growing startup developing event-based technology, with more than 15 PhDs working on problems like tracking, detection, classification, SLAM, etc. Event-based computer vision has the potential to solve many long-standing problems in traditional computer vision, and this is a super exciting time as this potential is becoming more and more tangible However, traditional approaches generally represent action and perception separately as computer vision modules that recognize objects and as planners that execute actions based on labels and poses. I propose here a more integrated approach where action and perception are combined in a memory model, in which a sequence of actions can be planned based on The PhD thesis exposes students to cutting-edge and unsolved research problems in the field of Computer Vision, where they are required to propose new solutions and significantly contribute towards the body of knowledge. Students pursue an independent research study, under the guidance of a supervisory panel, for a period of 3 - 4 years
Algebraic Methods in Computer Vision
This thesis presents end-to-end deep learning architectures for a number of core computer vision problems; scene understanding, camera pose estimation, stereo vision and video semantic segmentation. Our models outperform traditional approaches and advance state-of-the-art on a number of challenging computer vision benchmarks. However, these end-to-end The memory of the doctoral thesis (2 copies in paper and one electronic version) with this form for the cover. Summaries of the thesis (R form) in English and Catalan or Spanish in Word and in paper, duly signed. Proposal of reviewers (PR form), in word, including links to the cv or cv brief in pdf of the reviewers · THE APPLICATION OF COMPUTER VISION, MACHINE AND DEEP LEARNING ALGORITHMS UTILIZING MATLAB Andrea Linda Murphy, University of New Hampshire, Durham Date of Award Spring Project Type Thesis Program or Major Information Technology Degree Name Master of Science First Advisor Mihaela Sabin Second Advisor Karen Jin Third
PhD Thesis
· THE APPLICATION OF COMPUTER VISION, MACHINE AND DEEP LEARNING ALGORITHMS UTILIZING MATLAB Andrea Linda Murphy, University of New Hampshire, Durham Date of Award Spring Project Type Thesis Program or Major Information Technology Degree Name Master of Science First Advisor Mihaela Sabin Second Advisor Karen Jin Third In this thesis we propose an automatic generator of such efficient specific solvers based on the modified Groebner basis method. We demonstrate the usefulness of our approach by providing new, efficient and numerical stable solutions to several important relative pose problems, most of them previously unsolved The memory of the doctoral thesis (2 copies in paper and one electronic version) with this form for the cover. Summaries of the thesis (R form) in English and Catalan or Spanish in Word and in paper, duly signed. Proposal of reviewers (PR form), in word, including links to the cv or cv brief in pdf of the reviewers
The abstract
· THE APPLICATION OF COMPUTER VISION, MACHINE AND DEEP LEARNING ALGORITHMS UTILIZING MATLAB Andrea Linda Murphy, University of New Hampshire, Durham Date of Award Spring Project Type Thesis Program or Major Information Technology Degree Name Master of Science First Advisor Mihaela Sabin Second Advisor Karen Jin Third In this thesis we propose an automatic generator of such efficient specific solvers based on the modified Groebner basis method. We demonstrate the usefulness of our approach by providing new, efficient and numerical stable solutions to several important relative pose problems, most of them previously unsolved The PhD thesis exposes students to cutting-edge and unsolved research problems in the field of Computer Vision, where they are required to propose new solutions and significantly contribute towards the body of knowledge. Students pursue an independent research study, under the guidance of a supervisory panel, for a period of 3 - 4 years
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