Video Object Extraction via MRF-Based Contour Tracking

Chung, Chih-Yuan; Chen, Homer H.

doi:10.1109/tcsvt.2009.2026823

Cited by 24 publications

(2 citation statements)

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“…There are lots of researches for 3D object recognition in which most of them are to model 3D objects using multiple 2D views. For example, in [3,4], a method is proposed for combining multiple images of a 3D object into a single model representation; however, this approach requires that the single target object should occupy the majority of each of the training view images, which makes it meaningless in practice; the other most primary approaches are to get and describe the object's stable surface features from 2D view images, such as color feature [5], texture feature, shape feature [6], and contour feature [7].…”

Section: Related Researchesmentioning

confidence: 99%

An Innovative SIFT-Based Method for Rigid Video Object Recognition

Zhang

Xiong

2014

Mathematical Problems in Engineering

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This paper presents an innovative SIFT-based method for rigid video object recognition (hereafter called RVO-SIFT). Just like what happens in the vision system of human being, this method makes the object recognition and feature updating process organically unify together, using both trajectory and feature matching, and thereby it can learn new features not only in the training stage but also in the recognition stage, which can improve greatly the completeness of the video object’s features automatically and, in turn, increases the ratio of correct recognition drastically. The experimental results on real video sequences demonstrate its surprising robustness and efficiency.

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Section: Related Researchesmentioning

confidence: 99%

An Innovative SIFT-Based Method for Rigid Video Object Recognition

Zhang

Xiong

2014

Mathematical Problems in Engineering

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“…The initialization of the object tracking also has a significant impact on tracking results, in some contour-based tracking methods [5][6], the initializations are manually configured by human. One contour-based tracking initialization method [9] requires users to specify a basic model description for tracking task using reference image in setup step, when the tracking system begins, it will search the target in sequence that is relative to the reference image to initialize the tracking algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Optical Flow-Based PIG Method for Active Contour Tracking Initialization

Wang

et al. 2013

AMR

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In this paper, we propose a new optical flow-based Poisson inverse gradient (OFPIG) initialization method for active contour tracking. This method can automatically initialize the contour of moving target for consequent tracking. First, an optical flow based motion detection method is adopted to remove background information, and then a Poisson inverse gradient (PIG) initialization is applied to locate the target region. Finally, parametric active contour is used to evolve the correct target contour depending on the initialization precision. Experimental results have demonstrated its effectiveness and robustness.

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