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A robust image segmentation framework based on total variation spectral transform
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AI
DOI：10.1016/j.patrec.2021.12.001.png)
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Abstract
En 中文
Image segmentation is a critical process that influences subsequent tasks in computer vision. Despite years of research, popular segmentation methods usually cause over-segmentation or under-segmentation when segmenting images with noises. To address the issue, this paper proposes an image segmentation framework with the help of total variation (TV) spectral transform technique. Firstly, we impose the TV spectral transform on an image with noises to get basic elements that preserve shape structure. Sec-ondly, the object structure is roughly separated from the background by a separation surface, which is fitted using the maximal response time of each pixel. Thirdly, the roughly generated object structure is refined with a guided filter whose guidance image is calculated through a band-pass filter and inverse transform in the TV spectral domain. Finally, binary segmentation and morphological operations are con-ducted on the refined object structure to obtain the segmentation result. Experiment results indicate that our method can achieve high segmentation accuracy and be robust to noise compared with other classical approaches. (c) 2021 Published by Elsevier B.V.
Keywords:
Image segmentation
Total variation
Spectral transform
Separation surface
Guided filter
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