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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

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Hue-Grayscale Collaborating Edge Detection & Edge Color Distribution Space


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hue grayscale collaborating edge detection edge color distribution space

Hue-Grayscale Collaborating Edge Detection & Edge Color Distribution Space

Jiqiang Song

March 6th, 2002

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

0

P2

-1

P2

-1

-1

P3

P3

P4

-1

0

P4

-1

P1

-1

P1

P

P

4

8

P5

-1

P5

-1

 0

 0

0

P8

P8

-1

P7

-1

-1

P7

0

P6

P6

-1

HGC Edge Detector—

Binary edge detector
  • A foreground pixel ‘P’ (P=1) is an edge point if its convolution result does not equal zero.

 8-connected edges

 4-connected edges

grayscale edge detector

HGC Edge Detector—

Grayscale edge detector
  • Gradient operators
    • Sobel, Prewitt, Roberts
  • Second derivative operators
    • Zero-crossing, LoG
  • Others
    • Canny, SUSAN
color edge detector

R

Multi-dimensional gradient calculation

Color edges

G

Thresholding

B

R

1D Edge detection

Color edges

G

1D Edge detection

Output fusion

B

1D Edge detection

HGC Edge Detector—

Color edge detector
  • Multi-dimensional gradient methods
  • Output fusion methods
why to design a hgc edge detector

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.
introduction of color models

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),…
grayscale vs hsi

HGC Edge Detector—

Grayscale vs. HSI
  • RGB  Grayscale

g = 0.299R + 0.587G + 0.114B; (0 g 1)

  • RGB  HSI
grayscale vs hsi continued

HGC Edge Detector—

Grayscale vs. HSI (continued)
  • The change of hue cannot be detected in grayscale space.
  • The noticeable change of intensity or saturation can be detected in grayscale space.
hgc edge detector

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

hue edge map grayscale edge map

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.
overdetected hue edge minimization

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.
output fusion

HGC Edge Detector—

Output fusion
  • Merge HEM & GEM into a final Color Edge Map (CEM).
performance comparison

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
speed comparison

HGC Edge Detector—

Speed comparison
  • HGC edge detector saves average 20% of processing time compared to the YUV color edge detector.
part 2 edge color distribution space
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.
directional color operator

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
x y g space ecds

ECDS —

X-Y-G space  ECDS
  • Quantization
    • ECDS
    • (x,y,g)(mx,my,gl)
  • Distance-weighted accumulation
characteristics of ecds

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.