1. Image segmentation based on edge and corner detectors Joachim Stahl 04/21/2005

2. Problem The results of edge based image segmentation are affected by the performance of the underlying edge detector. In particular, edge detectors are weak at corner points. Solution: Introduce the results of a corner detector to make up for this weakness.

3. Image Segmentation Goal: to separate an image into foreground and background Foreground represents an object of interest. Different approaches, pixel-based and edge-based.

4. Edge Detection Returns a binary image indicating pixels in the original image where an abrupt changes in pixel intensity occur. Most famous method, Canny edge detector. One major drawback: it works poorly at corners.

5. Corner Detection Like edge detection, deals with points of high intensity changes, but also with abrupt changes in the direction of the edge track. Most famous method, Harris corner detector.

6. Basic Idea In order to create connected boundaries, edge grouping methods create fragments to fill the gaps. Let the construction of these gap-filling fragments be affected by the presence of a corner point.

7. Expected improvement By having this extra corner information incorporated, it is expected that a method can improve its detection in these special cases:

8. Considering Track Length Another important consideration, the length of the edge track to which the fragment belongs. Not considering this length could lead to false corners due to inaccurate edge approximation introduced by noise in the image.

9. Sample application Implement method around Ratio Contour. RC assigns a cost to each fragment based on proximity and continuity. Let the presence of a corner affect the curvature of nearby fragments. We can reformulate the cost function of RC to reflect this:

10. Result

11. Conclusions Incorporating corner detection to edge grouping can improve results. However, improvement is only noticeable in a small percentage of cases. Still a work in progress. It is a motivation to also work on edge detection improvement.

12. Thank you! Questions?