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Supervised manifold distance segmentation

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/10292

Supervised manifold distance segmentation

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Title: Supervised manifold distance segmentation
Author: Wang, Guanyu
Advisor(s): Kniss, Joe
Committee Member(s): Kniss, Joe
Sen, Pradeep
Williams, Lance
Department: University of New Mexico. Dept. of Computer Science
Subject(s): image segmentation
manifold data
extraction of surface
uncertainty
LC Subject(s): Image procesing--Digital techniques.
Rendering (Computer graphics).
Interactive computer graphics.
Information visualization.
Degree Level: Masters
Abstract: In this paper, I will propose a simple and robust method for image and volume data segmentation based on manifold distance metrics. In this approach, pixels in an image are not considered as points with color values arranged in a grid. In this way, a new data set is built by a transform function from one traditional 2D image or 3D volume to a manifold in higher dimension feature space. Multiple possible feature spaces like position, gradient and probabilistic measures are studied and experimented. Graph algorithm and probabilistic classification are involved. Both time and space complexity of this algorithm is O(N). With appropriate choice of feature vector, this method could produce similar qualitative and quantitative results to other algorithms like Level Sets and Random Walks. Analysis of sensitivity to parameters is presented. Comparison between segmentation results and ground-truth images is also provided to validate of the robustness of this method.
Graduation Date: December 2009
URI: http://hdl.handle.net/1928/10292

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