báo cáo hóa học:" Review Article On Parsing Visual Sequences with the Hidden Markov Model"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Review Article On Parsing Visual Sequences with the Hidden Markov Model | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2009 Article ID 924287 13 pages doi 2009 924287 Review Article On Parsing Visual Sequences with the Hidden Markov Model Naomi Harte Daire Lennon and Anil Kokaram School of Engineering Trinity College Dublin Dublin 2 Ireland Correspondence should be addressed to Naomi Harte nharte@ Received 18 August 2008 Revised 21 January 2009 Accepted 17 June 2009 Recommended by Riccardo Leonardi Hidden Markov Models have been employed in many vision applications to model and identify events of interest. Their use is common in applications where HMMs are used to classify previously divided segments of video as one of a set of events being modelled. HMMs can also simultaneously segment and classify events within a continuous video without the need for a separate first step to identify the start and end of the events. This is significantly less common. This paper is an exploration of the development of HMM frameworks for such complete event recognition. A review of how HMMs have been applied to both event classification and recognition is presented. The discussion evolves in parallel with an example of a real application in psychology for illustration. The complete videos depict sessions where candidates perform a number of different exercises under the instruction of a psychologist. The goal is to isolate portions of video containing just one of these exercises. The exercise involves rotating the head of a kneeling subject to the left back to centre to the right to the centre and repeating a number of times. By designing a HMM system to automatically isolate portions of video containing this exercise issues such as the strategy of choice of event to be modelled feature design and selection as well as training and testing are reviewed. Thus this paper shows how HMMs can be more extensively applied in the domain of event recognition in video. Copyright 2009 Naomi Harte et al. This is an

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