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variational-bayesian-inference
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Image Categorization Using Deep Learning
, M.Sc. Thesis Sharif University of Technology ; Jafari Siavoshani, Mahdi (Supervisor) ; Rabiee, Hamid Reza (Co-Advisor)
Abstract
The representation of data influences the explanation factors of data variations. Thus,the success of learner algorithms depends on the data representation. Our main contribution in this thesis is learning of high level and abstract representation using deep structure. One of the fundamental examples of representation learning is the AutoEncoders. The auto-encoder is a rigid framework that doesn’t consider explanation factors in terms of statistical concepts. So, the auto-encoders can be re-interpreted by seeing the decoder as the statistical model of interest. The role of encoder is a mechanism for inference in the model described by the decoder. Our purpose is to design such model with...
Dynamic Texture Segmentation in Video Sequences
, Ph.D. Dissertation Sharif University of Technology ; Manzuri Shalmani, Mohammad Taghi (Supervisor)
Abstract
Video segmentation means grouping of pixels of the video sequences into spatio-temporal regions which exhibit coherence in both appearance and motion. Due to complexity and spatio-temporal variations, dynamic texture segmentation is a one of the most challenging task in video processing. The problem of dynamic texture segmentation has received considerable attention due to the explosive growth of its applications in video analysis and surveillance systems. In this thesis, two novel approaches have been proposed. The first proposed method is based on generative Dynamic texture models (DTMs) which represent videos as a linear dynamical system. Since DTMs cannot be used for complex videos which...