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Object Removal in Multi-View Photos

Object Removal in Multi-View Photos. 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 time of taken and/or perspective of scene.

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Object Removal in Multi-View Photos

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  1. Object Removal in Multi-View Photos 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 black/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. Object Removal in Multi-View Photos Image Rectification

  6. Image Rectification • Transformation process used to project two-or-more images onto a common image plane. • Corrects image distortion by transforming the image into a standard coordinate system. 1 Figure 1: Example rectification of source images (1) to common image plane (2). 1

  7. Image Rectification To perform a transform... • Cameras are calibrated and provide internal parameters resulting in an essential matrix representingrelationship between the cameras. • We don’t have access to camera’s internal parameters. • What if single camera was used? • The more general case (without camera calibration) is represented by the fundamental matrix. 2

  8. Fundamental Matrix • Algebraic representation of epipolar geometry. • 3×3 matrix which relates corresponding points in stereo images. • 7 degrees of freedom, therefore at least 7 correspondences are required to compute the fundamental matrix. 3

  9. Corresponding Points • Figure out which parts of an image correspond to which parts of another image. • But what is a ‘part’ of an image? • ‘part’ of an image is a Spatial Feature. • Spatial Feature Detection is the process of identifying spatial features in images.

  10. Spatial Feature Detection - Edges • Canny, Prewitt, Sobel, Difference of Gaussians... Figure 2: Example application of Canny Edge Detection 4

  11. Spatial Feature Detection - Corners • Harris, FAST, SUSAN Figure 2: Example application of Harris Corner Detection 5

  12. Feature Description • Simply identifying a feature point is not in itself useful. • consider how one would attempt to match detected feature points between multiple images. • Scale-invariant feature transform (SIFT) offers robust feature description. 6 • Invariant to scale • Invariant to orientation • partially invariant to illumination changes

  13. SIFT • Uses Difference of Gaussians along with multiple smoothing and resampling filters to detect key points (Feature Points with descriptor data) • Key point specifies 2D location, scale, and orientation.

  14. SIFT Figure 3: Sample image for SIFT application. 7

  15. SIFT – Feature Points Figure 4: Detected feature points via SIFT. 7

  16. SIFT – Key Point Figure 5: A SIFT key point in detail. 7

  17. SIFT - Matching • Matches key points by identifying nearest neighbour with the minimum Euclidean distance. • Ensures robustness via... • Cluster identification by Hough transform voting. • Model verification by linear least squares.

  18. SIFT - Matching Figure 5: Example of matched SIFT key points. Note its tolerance to image scale and rotation.

  19. SIFT – Suitable for Multi-View? • SIFT fails to accurately match key points between images which vary significantly in perspective. Figure 7 & 8: Comparison of SIFT accuracy with varying perspective angles. Left image is 45 degrees with 152 matches. Right image is 75 degrees with 11 matches. 8

  20. SIFT – Suitable for Multi-View? • SIFT fails to accurately match key points between images which undergo non-scalable affine transformation or projection. Figure 9: SIFT fails to identify any key point matches between rotated images on a cylinder. 8

  21. ASIFT • Affine-SIFT (ASIFT) is a new framework for fully affine invariant image comparison. • Uses existing SIFT key point descriptors, but matching algorithm has improved.

  22. ASIFT – Improvements over SIFT • Simulated images are compared by a rotation, translation and zoom-invariant algorithm. • (SIFT normalizes translation and rotation and simulates zoom.)

  23. ASIFT – Improvements over SIFT Figure 10: ASIFT (left) identifies 165 matches compared to SIFT’s (right) 11 matches on surface rotated 75 degrees. 8

  24. ASIFT – Improvements over SIFT Figure 10: ASIFT identifies 381 matches between rotated surfaces. 8

  25. Image Rectification • Quick Review... • Given multiple images of the same scene from different perspectives... • We have identified & matched feature points using ASIFT. • We now have sufficient matching points to calculate the fundamental matrix.

  26. Calculating Fundamental Matrix • Random Sample Consensus (RANSAC) is used to eliminate outliers from matched points. • Select 7 points at random. • Use them to compute a Fundamental Matrix between the image pair. • Project every point in the dataset onto the conjugate image pair using the Fundamental Matrix. • If at least 7 points were projected closer to their actual locations than their allowable errors, stop. • Use those 7 points to calculate final Fundamental Matrix.

  27. Example Image Rectification • Input Images

  28. Example Image Rectification • ASIFT Matches

  29. Example Image Rectification • RANSAC selection

  30. Example Image Rectification • Resulting Rectification

  31. Identifying Image Differences • Possible Methods: • Direct subtraction • Structural Similarity Index (SSIM) • Complex Waveform SSIM

  32. Identifying Image Differences • Direct subtraction • Too good to be true! (way too much noise)

  33. 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

  34. Object Removal in Multi-View Photos Complex Waveform SSIM

  35. Complex Waveform SSIM • SSIM vs Complex Waveform SSIM(CWSSIM)

  36. CWSSIM - Implementation • Steerable Pyramid constructed for each image • (Steerable Pyramid is a linear multi-scale, multi-orientation image decomposition) • SSIM value calculated for each band, from high to low frequency. • SSIM values for each band are scaled and summed.

  37. CWSSIM - Implementation • Analyzing bands with multi-scale, multi-orientation image decomposition instead of direct pixel comparison provides tolerance for Spatial Shifts. • By reducing the contribution SSIM indexes belonging to high frequency bands we can reduce noise. • …but we lose recognition of changes in that frequency.

  38. CWSSIM - Example • Input Images

  39. CWSSIM - Example • Application of CWSSIM with equal frequency weights.

  40. CWSSIM - Example • Input Images

  41. CWSSIM - Example • Application of CWSSIM with decreased low frequency weight.

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

  43. 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.

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

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

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

  47. Infilling the objects • Result of direct copying

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

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

  50. 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

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