Dr. Ting Cao Profile

Dr. Ting Cao

at Chang'an Univ

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Proceedings Article | 15 November 2017 Paper

Crack image segmentation based on improved DBC method

Ting Cao, Nan Yang, Fengping Wang, Ting Gao, Weixing Wang

Proceedings Volume 10605, 106052B (2017) https://doi.org/10.1117/12.2292900

KEYWORDS: Image segmentation

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With the development of computer vision technology, crack detection based on digital image segmentation method arouses global attentions among researchers and transportation ministries. Since the crack always exhibits the random shape and complex texture, it is still a challenge to accomplish reliable crack detection results. Therefore, a novel crack image segmentation method based on fractal DBC (differential box counting) is introduced in this paper. The proposed method can estimate every pixel fractal feature based on neighborhood information which can consider the contribution from all possible direction in the related block. The block moves just one pixel every time so that it could cover all the pixels in the crack image. Unlike the classic DBC method which only describes fractal feature for the related region, this novel method can effectively achieve crack image segmentation according to the fractal feature of every pixel. The experiment proves the proposed method can achieve satisfactory results in crack detection.

Proceedings Article | 15 November 2017 Paper

Change detection from remote sensing images based on fractional integral and improved FLICM

Fengping Wang, Weixing Wang, Ting Cao, Weiwei Chen

Proceedings Volume 10605, 1060522 (2017) https://doi.org/10.1117/12.2292656

KEYWORDS: Remote sensing

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This paper presents a new change detection method based on fractional integral and improved FLICM clustering. Firstly, the log-ratio operator is applied to obtain the difference image from two registered and corrected remote sensing images; and then, the fractional integral operator is introduced to de-nosing and preserve the edge and texture information of the difference image; Finally, the improved FLICM is carried out to get the change areas, which fully considering the pixel neighborhood information and the spatial distance information of the objective function. Experimental results show that the proposed algorithm has strong ability to suppress noise, and can obtain good detection results.

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