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Edge detection

Goal: Identify sudden changes (discontinuities) in an image This is where most information in an image is coded Example: line drawings. Edge detection. What causes an edge?. Depth discontinuity Surface orientation discontinuity Changes in surface properties

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Edge detection

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  1. Goal: Identify sudden changes (discontinuities) in an image This is where most information in an image is coded Example: line drawings Edge detection

  2. What causes an edge? • Depth discontinuity • Surface orientation discontinuity • Changes in surface properties • Light discontinuities (e.g. shadows)

  3. Scale Increased scale: • Eliminates noisy edges • Makes edges smoother and thicker • Removes fine details

  4. Suppression of non-maxima: • Choose the local maximum point along a perpendicular cross section of the edge.

  5. Example: Suppression of non-maxima courtesy of G. Loy Non-maxima suppressed Original image Gradient magnitude

  6. Differentiation with a Gaussian filter

  7. Example: Canny Edge Detection Using Matlab with default thresholds

  8. Corner Detector • Compute x- and y-derivatives with a Gaussian filter • Form the orientation tensor M for every pixel • Compute the product of eigen-values, i.e., the determinant of M • If both eigenvalues large (product is a local maximum), then it is a corner!

  9. Gradient directions

  10. Blow-up of gradient directions

  11. Corners can be detected where the product of the ellipse axes are local maxima

  12. Fast (bottom-up) - some methods scale

  13. Fast (bottom-up) - some methods scale

  14. Fast (bottom-up) - some don’t

  15. Task: Image Retrieval • Oxford Building Data (Philbin et al. CVPR’07) Query

  16. Task: Image Retrieval • Oxford Building Data (Philbin et al. CVPR’07) Query

  17. Task: Image Retrieval • Oxford Building Data (Philbin et al. CVPR’07) Good match Query

  18. Task: Image Retrieval • Oxford Building Data (Philbin et al. CVPR’07) Good match Matched? Query

  19. Task: Image Retrieval • Oxford Building Data (Philbin et al. CVPR’07) Good match Matched? Query

  20. Baseline System – Bags of Words • Interest point detection (position, scale, orientation) - Differences of Gaussian/Harris

  21. Baseline System – Bags of Words • Interest point detection - Differences of Gaussian/Harris • Feature extraction (feature vector e g R^128) - SIFT/SURF/DAISY

  22. Baseline System – Bags of Words • Interest point detection - Differences of Gaussian/Harris • Feature extraction - SIFT/SURF/DAISY • Generating vocabularies – quantization - hierarchical k-means (Nister, Stewenius CVPR’06) - approximate k-means (Philbin et al. CVPR’08)

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