JOURNAL ARTICLE

Object contour tracking using graph cuts based active contours

Nan XuNarendra Ahuja

Year: 2003 Journal:   Proceedings - International Conference on Image Processing Vol: 1 Pages: III-277   Publisher: Institute of Electrical and Electronics Engineers

Abstract

In this paper, we present an object contour tracking approach using graph cuts based active contours (GCBAC). Our proposed algorithm does not need any a priori global shape model, which makes it useful for tracking objects with deformable shapes and appearances. GCBAC are not sensitive to initial conditions and always converge to the optimal contour within the dilated neighborhood of itself. Given an initial boundary near the object in the first frame, GCBAC can iteratively converge to an optimal object boundary. In each frame thereafter, the resulting contour in the previous frame is taken as initialization and the algorithm consists of two steps. In the first step, GCBAC are applied to the difference between this frame and its previous one. The resulting contour is taken as initialization of the second step, which applies GCBAC to current frame directly. To evaluate the tracking performance, we apply the algorithm to several real world video sequences. Experimental results are provided.

Keywords:
Initialization Computer vision Artificial intelligence Tracking (education) Frame (networking) Computer science Object (grammar) Boundary (topology) Video tracking Graph Active contour model A priori and a posteriori Cut Algorithm Mathematics Image segmentation Segmentation Theoretical computer science

Metrics

35
Cited By
1.83
FWCI (Field Weighted Citation Impact)
12
Refs
0.86
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Medical Image Segmentation Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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