Local level set segmentation method combined with narrow band

Yushi Li; Jun Zhou; Junlong Li; Chunsheng Liu

doi:10.1117/12.753376

15 November 2007 Local level set segmentation method combined with narrow band

Yushi Li, Jun Zhou, Junlong Li, Chunsheng Liu

Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 67862Q (2007) https://doi.org/10.1117/12.753376
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

The paper deduces a general form of energy function from the level set method based on Mumford-Shah model. It introduces the gradient of image features into the energy function, which could make the segmentation more precise and the algorithm converge faster. In order to detect local object in the image with clutter background, a local level set segmentation method using the proposed energy function is presented in this paper. The method combined with narrow band could obtain local optimal segmentation, which just needs prior location of the object. To tackle the problem that the calculation cost of level set is so expensive, the paper proposes an efficient algorithm for narrow band which implements very fast. The algorithm starts with a simple initial curve, and then it only updates the level set function in the narrow band. The local level set method is applied successfully to image segmentation with cluttered background, multi-object detection and moving object detection. The results of the experiments are presented in the end of paper.

Citation Download Citation

Yushi Li, Jun Zhou, Junlong Li, and Chunsheng Liu "Local level set segmentation method combined with narrow band", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67862Q (15 November 2007); https://doi.org/10.1117/12.753376

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KEYWORDS

Image segmentation

Image processing algorithms and systems

Image processing

Distributed interactive simulations

Detection and tracking algorithms

Computer vision technology

Image resolution

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