1. Hue-Grayscale Collaborating Edge Detection & Edge Color Distribution Space Jiqiang Song March 6 th, 2002

2. Introduction Definition of “Edge” in an image –Shape transition of intensity and/or color Meaning of edge –Outline of objects –Image structure –An important feature for image segmentation & object detection

3. Part 1: Hue-Edge Collaborating (HGC) edge detection Existing edge detection methods –Binary image –Grayscale image –Color image

4. Binary edge detector A foreground pixel ‘P’ (P=1) is an edge point if its convolution result does not equal zero. HGC Edge Detector — P2P2 P1P1 P3P3 P8P8 P P4P4 P7P7 P5P5 P6P6 0 0 4 0 0  0 P2P2 P1P1 P3P3 P8P8 P P4P4 P7P7 P5P5 P6P6 8  0  8-connected edges  4-connected edges

5. Grayscale edge detector Gradient operators –Sobel, Prewitt, Roberts Second derivative operators –Zero-crossing, LoG Others –Canny, SUSAN HGC Edge Detector —

6. Color edge detector Multi-dimensional gradient methods Output fusion methods HGC Edge Detector — R G B Multi-dimensional gradient calculation Thresholding Color edges R G B 1D Edge detection Output fusion Color edges 1D Edge detection

7. Why to design a HGC edge detector? Grayscale edge detector  >90% of real edges, fast. Color edge detector  more edges, slow. Our application: video processing –Thousands of images in a 10 minutes long video (when sampling 3~4 images/second) –Color edge detector often over-detects edges. HGC Edge Detector —

8. Introduction of color models RGB –R (red); G (green); B (blue) Grayscale –Luminance, achromatic, 1 dimension HSI – a perceptual color model –H (hue); S (saturation); I (intensity) Others: YUV, HIQ, CIE(Lab),… HGC Edge Detector —

9. Grayscale vs. HSI RGB  Grayscale g = 0.299  R + 0.587  G + 0.114  B; (0  g  1) RGB  HSI HGC Edge Detector —

10. Grayscale vs. HSI (continued) 1.The change of hue cannot be detected in grayscale space. 2.The noticeable change of intensity or saturation can be detected in grayscale space. HGC Edge Detector —

11. HGC edge detector Step 1: Generate Hue Edge Map (HEM) & Grayscale Edge Map (GEM) Step 2: Overdetected edge minimization Step 3: Output fusion HGC Edge Detector —

12. Hue Edge Map & Grayscale Edge map Convert a sampled RGB video image into a hue map & a grayscale map. Use Sobel operator to detect edge strength (gradient) in two maps. Use a fuzzy threshold to generate edge maps. HGC Edge Detector —

13. Overdetected hue edge minimization ASSUME: a valuable edge point must have a certain connected length. Extract hue edge points that are not grayscale edge points. Use a run-length transform (RLT) to calculate the maximum connected length of an edge point in any direction. Remove edge points that are not of desired connected length. HGC Edge Detector —

14. Output fusion Merge HEM & GEM into a final Color Edge Map (CEM). HGC Edge Detector —

15. Performance comparison Compared methods –A grayscale edge detector (Sobel) –HGC edge detector –A YUV color edge detector Compared aspects –Speed –Edge completeness Testing data: real-life video images HGC Edge Detector —

16. Speed comparison HGC edge detector saves average 20% of processing time compared to the YUV color edge detector. HGC Edge Detector —

17. Comparison of edge completeness HGC Edge Detector —

18. Comparison of edge completeness (continued) HGC Edge Detector —

19. Part 2: Edge Color Distribution Space Why introducing a Edge Color Distribution Space (ECDS) ? –2D edge space is crowded. –Color is an important information to segment different objects. Object discussed here is uniform-color object or textured object, not high-level object. The discussed image is of width W, of height H, and of 256-level grayscale.

20. Directional color operator Get the directional average color of a point Edge point (x, y, g): 0  x  W, 0  y  H, 0  g  255 ECDS —

21. X-Y-G space  ECDS Quantization –ECDS –(x,y,g)  (mx,my,gl) Distance-weighted accumulation ECDS —

22. Characteristics of ECDS Spatial relation of an object in the image is kept. Objects of different colors are separated. The edge of uniform-color object is continuous. The edge of textured object is clustering. ECDS —

23. ECDS: a synthetic image ECDS —

24. ECDS: a video image ECDS —

25. End. Thank you!