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Research of Image Edge Detection Based on Active Contour Model

Author: XieShanShan
Tutor: MaSheXiang
School: Tianjin University of Technology
Course: Signal and Information Processing
Keywords: Edge detection Active contour model Concave region Force field analysis Gradient vector flow
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 48
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Abstract


Edge detection, aims to detect the dividing line between objects and backgrounds, is one of fundamental problems in computer vision and image pattern recognition. It plays an essential role in human life and engineering application that makes its research have meaningful practical values.Active contour model, a new image segmentation and target detection method, achieves the top-bottom image edge detection more rapidly and effectively in combining the top prior knowledge and bottom image information. It is a dynamic curve guided by the interaction of external and internal energy that press on toward the object under the principle of the energy minimization.It has introduced the purpose meaning, current situation and the principle main algorithm of edge detection in this paper, then focused on the concept and algorithm process of active contour model. There are two problems in active contour model, one is sensitive of initialization and another is difficulty in deeping into concave boundary regions. Series of improved models are able to deal with above issuesThere is a rapid edge detection method which combines the advantages of Distance Snake and GVF Snake. First, approximate the object contour preliminarily by Distance Snake and design criterion to determine the contour approximation. Then, using the GVF Snake to drive the contour into the concave regions while the contour approximation has been surpassed the given value. The results show that this new model not only has a large capture range and fast convergence, also could deep into concave regions.In this paper, it presents an improved algorithm based on the GVF Snake in order to achieve the multi-objective edge detection. Firstly, use the GVF Snake for carrying on the edge extraction of the whole object region, the results will contain false edges because of the stagnation point region. Secondly, analyze this false edge and establish a new initial contour automatically. Then, use the GVF Snake again to carry on new edges, and to determine the real edge and false edge. In the end, connect those real edges, which were extracted in above two steps, and remove the false edges to get the complete multi-objective edge. Experimental results show that the proposed algorithm can achieve the multi-objective edge detection.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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