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Here today. Gone Tomorrow

Here today. Gone Tomorrow. Aaron McClennon-Sowchuk , Michail Greshischev. Objectives. remove an object from a set of images by using information (pixels) from other images in the set. The images must be of the same scene but can vary in

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Here today. Gone Tomorrow

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  1. Here today. Gone Tomorrow Aaron McClennon-Sowchuk, MichailGreshischev

  2. Objectives • remove an object from a set of images by using information (pixels) from other images in the set. • The images mustbe of the same scene but can vary in time of taken and/or perspective of scene. The allowed variance in time means objects may change location from one image to the next. Applications: stock photography, video surveillance, etc.

  3. Steps • Read Images • Project images in same perspective • Align the images • Identify differences • Infill objects

  4. Reading Images • How are images represented? • Matrices (M x N x P) • M is the width of the image • N is the height of the image • P is 1 or 3 depend on quality of image 1: binary (strictly or white) or gray-scale images 3: coloured images (3 components of colour: R,G,B) • What tools are capable of processing images? • Many to choose from but MatLab is ideal for matrices. • Hence the name Mat(rix) Lab(oratory)

  5. Identifying differences • Possible Methods: • Direct subtraction • Structural Similarity Index (SSIM) • Complex Waveform SSIM

  6. Identifying differences • Direct subtraction • Too good to be true! (way too much noise)

  7. Identifying differences • Structural Similarity Index (SSIM) • Number 0-1 indicating how “similar” two pixels are. • 1 indicates perfect match, 0 indicates no similarities at all • Number calculated based on: • Luminance, function of the mean intensity for gray-scale image • Contrast, function of std.dev of intensity for gray-scale image

  8. Identifying differences • Once again, way too much noise. • SSIM map: 0  black pixel 1 white pixel

  9. Infilling the objects • Concerns: • Identify regions to copy • Calculate a bounding box (smallest area surrounding entire blob) • How to distinguish noise from actual objects? • Area - those blobs with area below threshold are ignored • location - those blobs along an edge of image are ignored. • Copying method • Direct – images from same perspectives • Manipulated pixels – images from different perspectives.

  10. Infilling the objects • Original bounding box results: Matlab returns Left position Top position Width and Height of each box

  11. Infilling the objects • Result with small blobs and blobs along edges ignored: • Left: 119 • Top: 52 • Width: 122 • Height: 264

  12. Infilling the objects • Once regions identified, how can pixels be copied? • Same perspective – direct copy is possible.

  13. Infilling the objects • Result of direct copying

  14. Infilling the objects • Different perspectives • Goal: remove black trophy from left image

  15. Infilling the objects • Direct copying produces horrendous results! Rectified image Result

  16. Work to come... • Copying techniques • Need better method for infilling objects between images in different perspectives. Perhaps use same alignment matrix. • Anti-Aliasing • Method to smooth the edges around pixels copied from one image to another • example looks alright but could improve other test cases • User friendly interface • Current state: a dozen different MatLab scripts. • In the perfect world, we’d have a nice interface to let user load images and clearly displa

  17. Conclusions

  18. References • Z. Wang and A. C. Bovik, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Processing, vol. 13, pp. 600 – 612, Apr. 2004. www.ece.uwaterloo.ca/~z70wang/publications/ssim.html

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