1. Edge Segmentation in Computer Images CSE350/450-011 4 Sep 03

2. Administration Homework 1 is posted: Due 16 Sep 03 Questions?

3. Class Objectives Understand image representation in a computer Understand what edges correspond to in a computer image Understand how edges can be extracted from computer image

4. Tracking Image Features Artificial –Color –Fiducials Natural –Color/Grayscale level –Edges –Corners –Textures

5. Omnidirectional Vision System Tracking Image Features

6. Computer Image Representation Images can be viewed as discrete functions indicating the light intensity of a scene They are represented in computers as 2-d arrays over a discrete set of values (e.g. 0-255)

7. Sample Application Indoor Obstacle Avoidance

8. The Edge Detection Process INPUT IMAGE Noise Smoothing Edge Enhancement Edge Detection EDGE IMAGE

9. What are edges? Edges correspond to abrupt changes in image intensity How can we estimate the rate of change in a function?

10. Taking the discrete derivative abs()

11. Thresholding the Gradient Image threshold(40)

12. The effects of Thresholding Threshold = 50 Threshold 100 Threshold 20

13. The effects of Filtering Noise Threshold 20 Gaussian SmoothingUnsmoothed Edges Threshold 50

14. Dominant Edge Detectors Sobel Masks Canny –Estimate edge direction E s –Estimate edge strength E d –Remove surplus edge pixels in the E d direction

15. What we learned Computer images correspond to discrete 2-D functions of light intensity Edges correspond to abrupt changes in image intensity Edges can be detected by –Smoothing out image noise –Estimating the gradient of the image at every point to generate a “gradient” image –Thresholding the gradient image at an appropriate level