HueGrayscale Collaborating Edge Detection Edge Color Distribution Space

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

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

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

HGC Edge Detector — Binary edge detector • A foreground pixel ‘P’ (P=1) is an edge point if its convolution result does not equal zero. P 2 P 3 P 4 0 -1 0 P 1 P P 5 • -1 4 -1 0 P 8 P 7 P 6 0 -1 0 8 -connected edges P 2 P 3 P 4 -1 -1 -1 P P 5 • -1 8 -1 0 P 8 P 7 P 6 -1 -1 -1 4 -connected edges

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

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

HGC Edge Detector — 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 — 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 — Grayscale vs. HSI • RGB Grayscale g = 0. 299 R + 0. 587 G + 0. 114 B; (0 g 1) • RGB HSI

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

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

HGC Edge Detector — Comparison of edge completeness

HGC Edge Detector — Comparison of edge completeness (continued)

Part 2: Edge Color Distribution Space • Why introducing a Edge Color Distribution Space (ECDS) ? – 2 D 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.

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

ECDS — 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 — ECDS: a synthetic image

ECDS — ECDS: a video image

End. Thank you!