SVD Based Automatic Detection of Target Regions for Image Inpainting

Padalkar, Milind G.; Zaveri, Mukesh A.; Joshi, Manjunath V.

doi:10.1007/978-3-642-37484-5_6

Cited by 7 publications

(9 citation statements)

References 12 publications

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“…The results of our crack detection method are at par with and in some cases better than those obtained using the techniques in [30] and [43]. Yet, our method is significantly faster, more accurate and the inpainted results are convincing.…”

Section: Resultsmentioning

confidence: 54%

“…20a is of size 400 × 300. We also show a comparison of the results obtained by our crack detection method with those obtained using the techniques proposed by Amano [1], Turakhia et al [43] and Padalkar et al [30]. It may be noted that the results for the technique in [1] are the best possible, obtained after fine-tuning the parameters.…”

Section: Resultsmentioning

confidence: 73%

“…The performance of the proposed method in comparison with the techniques suggested by Amano [1], Turakhia et al [43] and Padalkar et al [30] in terms of Recall, Precision and execution time, for input images in Figs. 15,16,17,18,19 and 20 is given in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…The technique proposed in [30] compares overlapping adjacent patches for similarity. The patches are compared in the singular value decomposition (SVD) domain and use an image-dependent threshold to identify cracked regions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The detected regions in b-e and the manual selection by volunteers in f are shown in red colour. Results of inpainting the regions detected in b-e are shown in g-j, respectively (a) Input (b)Amano [1] (c) Turakhia et al[43] (d)Padalkar et al [30]. (e) Proposed method (f) Selection by volunteers (g) Inpainting of (b) (h) Inpainting of (c) (i) Inpainting of (d) (j) Inpainting of (e)Fig.…”

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confidence: 99%

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Auto-inpainting heritage scenes: a complete framework for detecting and infilling cracks in images and videos with quantitative assessment

Padalkar

Joshi

2015

Machine Vision and Applications

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The need for preservation of cultural heritage has necessitated the research on digitally repairing the photographs of damaged monuments. In this paper, we first propose a technique for automatically detecting the cracked regions in photographs of monuments. Unlike the usual practice of manually selecting the mask for inpainting, the detected regions are supplied to an inpainting algorithm. Thus, the process of digitally repairing the cracked regions that physical objects have, using inpainting, is completely automated. The detection of cracked regions is based on comparison of patches, for which we use a measure derived from the edit distance, which is a popular string metric used in the area of text mining. Further, we extend this method to perform inpainting of video frames by making use of the scaleinvariant feature transform and homography. We consider the camera to move while capturing video of the heritage site, as such videos are typically captured by novices, hobbyists and tourists. Finally, we also propose a video quality measure to quantify the temporal consistency of the inpainted video. Experiments have been carried out on videos captured from the heritage site at Hampi, India.

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Section: Resultsmentioning

confidence: 54%