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Artifact and Textured region Detection

Artifact and Textured region Detection. - Vishal Bangard. Outline. Need for artifact and textured region detection Aim of the project Techniques used in the imaging world Approaches used Results Conclusion. Why do artifact detection ?. A lot of transformations lead to artifacts

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Artifact and Textured region Detection

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  1. Artifact and Textured region Detection - Vishal Bangard

  2. Outline • Need for artifact and textured region detection • Aim of the project • Techniques used in the imaging world • Approaches used • Results • Conclusion

  3. Why do artifact detection ? • A lot of transformations lead to artifacts • Few of them lead to loss in texture • Main goal – repair/ replace the loss in texture using texture from adjacent regions • There are existing methods for replicating texture • Not many existing methods for detecting regions where there is texture loss

  4. Left image compressed at 94% The encircled areas show loss in texture due to compression Image: Barbara

  5. Aim of the project • Very subjective – depends a lot on prior experience and knowledge • Complete automated detection is very hard because of the subjective nature of the problem • Aim of this project: To locate regions near textured regions which may have been subject to texture loss

  6. Techniques that work well • The topics covered under this project are very subjective – hence the title of this slide is ‘techniques that work well’ as against ‘state of the art approaches’ • Detection of textured regions • Gabor filters • Difference of offset Gaussians

  7. Segmentation • Lots and lots of them (e.g. thresholding, clustering methods such as k-means and fuzzy c-means, connected components, region growing, etc.) • Again, very subjective

  8. Approaches taken • Analyze wavelet block decomposition for a change in the high frequency region • Low resolution • Higher miss probability The image Barbara compressed at 94%

  9. Solid black areas give regions with texture. Black lines are parts of edges Edge map of left image overlaid on the compressed image

  10. Use of Gabor coefficients as they are fairly reliable in detecting textured regions (found at six different orientations and four different scales) • Transformed image is adaptively thresholded to minimize the inter-class variance(Ref: N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, 1979.) • Magnitude and phase information is used collectively to locate regions of high texture

  11. Pictures speak a thousand words An edge map of the left image overlaid on the compressed image White regions give borders and textured areas

  12. Left image compressed at 90% using Elecard (version of JPEG 2000 coding software) Image: Baboon

  13. White regions give regions of high textures An edge map of the left image overlaid on the compressed image

  14. Conclusion • The system performs well in detecting textured regions (Statistics still need to be calculated) • Needs to be extended to color and non-square images • Can also be used for texture identification • Can be combined with existing methods to repair texture loss

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