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- W2023671622 abstract "Heritage structures are very important to mankind as they help in studying and understanding the growth of civilization and are sources of inspiration to modern craftsmen. However, many structures get degraded / vandalized due to various reasons like aging, material degradation, human interventions, etc. With the use of image inpainting techniques, one can digitally restore these structures. In almost all the available inpainting techniques, one has to manually select the target region. In this paper, we propose a novel approach to automatically identify cracks in the heritage structures. The method exploits the use of order-statistics filtering and the tensor voting to detect crack regions which are given as input to inpainting algorithms. The order-statistics filters enhance the contrast of the crack regions, which then act as input to a Canny edge detector. A 2D tensor voting is applied on the edge output for enhancing the curveness of the probable crack regions. Finally, image dilation is used for crack filling, generating the final target regions for image inpainting. We show the effectiveness of the method by conducting experiments on the images captured from the world Heritage site Hampi, Karnataka." @default.
- W2023671622 created "2016-06-24" @default.
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- W2023671622 date "2012-12-16" @default.
- W2023671622 modified "2023-09-24" @default.
- W2023671622 title "Automatic crack detection in heritage site images for image inpainting" @default.
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- W2023671622 doi "https://doi.org/10.1145/2425333.2425401" @default.
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