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Markov Random Fields Based Image and...

Liu, Ming.

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Markov Random Fields Based Image and Video Processing.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Markov Random Fields Based Image and Video Processing./
作者:	Liu, Ming.
面頁冊數:	89 p.
附註:	Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: .
Contained By:	Dissertation Abstracts International72-04B.
標題:	Information Technology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3446029
ISBN:	9781124497952

Markov Random Fields Based Image and Video Processing.
Liu, Ming.

Markov Random Fields Based Image and Video Processing. - 89 p.

Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: .

Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2010.

Many problems in computer vision involve assigning each pixel a label, which represents some spatially varying quantity such as image intensity in image denoising or object index label in image segmentation. In general, such quantities in image processing tend to be spatially piecewise smooth, since they vary smoothly in the object surface and change dramatically at object boundaries, while in video processing, additional temporal smoothness is satisfied as the corresponding pixels in different frames should have similar labels. Markov random field (MRF) models provide a robust and unified framework for many image and video applications. The framework can be elegantly expressed as an MRF-based energy minimization problem, where two penalty terms are defined with different forms. Many approaches have been proposed to solve the MRF-based energy optimization problem, such as simulated annealing, iterated conditional modes, graph cuts, and belief propagation.

ISBN: 9781124497952Subjects--Topical Terms:

1030799
Information Technology.

Markov Random Fields Based Image and Video Processing.
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500 $a Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: .
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520 $a Many problems in computer vision involve assigning each pixel a label, which represents some spatially varying quantity such as image intensity in image denoising or object index label in image segmentation. In general, such quantities in image processing tend to be spatially piecewise smooth, since they vary smoothly in the object surface and change dramatically at object boundaries, while in video processing, additional temporal smoothness is satisfied as the corresponding pixels in different frames should have similar labels. Markov random field (MRF) models provide a robust and unified framework for many image and video applications. The framework can be elegantly expressed as an MRF-based energy minimization problem, where two penalty terms are defined with different forms. Many approaches have been proposed to solve the MRF-based energy optimization problem, such as simulated annealing, iterated conditional modes, graph cuts, and belief propagation.
520 $a In this dissertation, we propose three methods to solve the problems of interactive image segmentation, video completion, and image denoising, which are all formulated as MRF-based energy minimization problems. In our algorithms, different MRF-based energy functions with particular techniques according to the characteristics of different tasks are designed to well fit the problems. With the energy functions, different optimization schemes are proposed to find the optimal results in these applications. In interactive image segmentation, an iterative optimization based framework is proposed, where in each iteration an MRF-based energy function incorporating an estimated initial probabilistic map of the image is optimized with a relaxed global optimal solution. In video completion, a well-defined MRF energy function involving both spatial and temporal coherence relationship is constructed based on the local motions calculated in the first step of the algorithm. A hierarchical belief propagation optimization scheme is proposed to efficiently solve the problem. In image denoising, label relaxation based optimization on a Gaussian MRF energy is used to achieve the global optimal closed form solution.
520 $a Promising results obtained by the proposed algorithms, with both quantitative and qualitative comparisons to the state-of-the-art methods, demonstrate the effectiveness of our algorithms in these image and video processing applications.
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