1 / 23

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

erling
Télécharger la présentation

Edge detection

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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)

More Related