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Efficient Color Boundary Detection with Color-opponent Mechanisms

Efficient Color Boundary Detection with Color-opponent Mechanisms. CVPR2013 Posters. Outline. Introduction Approach Experiments Conclusions. Introduction. Introduction.

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Efficient Color Boundary Detection with Color-opponent Mechanisms

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  1. Efficient Color Boundary Detection with Color-opponent Mechanisms CVPR2013 Posters

  2. Outline • Introduction • Approach • Experiments • Conclusions

  3. Introduction

  4. Introduction • Propose a new framework for boundary detection in complex natural scenes based on the color-opponent mechanisms of the visual system. Image source: http://en.wikipedia.org/wiki/Opponent_process

  5. Introduction • One of the key limitations ofopponent-based approaches is that they are blind to theluminance-defined boundaries. • In order to obtain the complete contours of objects, these methods had to spend extra computational cost to combine more cues to detect luminance boundaries [3]. [3] D. R. Martin, C. C. Fowlkes, and J. Malik, "Learning to detect natural image boundaries using local brightness, color, and texture cues," IEEE Trans. on PAMI, vol. 26, pp. 530-549, 2004.

  6. Introduction • Simulate the biological mechanisms of color information processing along the Retina-LGN-Cortex visual pathway Image source: http://en.wikipedia.org/wiki/Opponent_process

  7. Introduction Image source: [20] S. G. Solomon and P. Lennie, "The machinery of colour vision," Nature Reviews Neuroscience, vol. 8, pp. 276-286, 2007.

  8. Introduction • Color Mechanisms in the Visual System. • Properties : • 1. Trichromacy. • 2. Two opponent channels. • 3. Color opponency.

  9. Approach • Boundary Detection System : • 1.Cone Layer • 2.Ganglion/LGN Layer • 3.Cortex Layer

  10. A feedforward hierarchical system

  11. 1.Cone Layer • Type II cells in the ganglion/LGN layer is mainly for the perception of color region. • Four channels: red (R), green (G), blue (B) and yellow (Y) components, where Y = (R+G)/2. • Gaussian filters are used to simulate the receptive field of the cones in the retina. • Outputs:

  12. Approach • Boundary Detection System : • 1.Cone Layer • 2.Ganglion/LGN Layer • 3.Cortex Layer

  13. 2.Ganglion/LGN Layer Single-opponent cells in ganglion/LGN layer are important for separating color and achromatic information,which is clearly shown by Equation 1. w1 > 0 and w2 < 0 response : R-on/G-off cells w1 < 0 and w2 >0 response : R-off/G-on cells

  14. Approach • Boundary Detection System : • 1.Cone Layer • 2.Ganglion/LGN Layer • 3.Cortex Layer

  15. 3.Cortex Layer • In the cortex layer of V1, the receptive fields of most color- and color-luminance-sensitive neurons are both chromatically and spatially opponent.

  16. 3.Cortex Layer

  17. 3.Cortex Layer • The boundary responses at each orientation is given by (6)

  18. 3.Cortex Layer • The boundaries are detected in four channels (i.e., R+ wG, wR+ G, B+ wY and wB+Y ) with Equations 1-8.

  19. Experiments

  20. Experiments

  21. Experiments

  22. Experiments

  23. Experiments

  24. Experiments

  25. Experiments

  26. Conclusions • 1. Presented a novel biologically plausible computational model for contour detection of color images. • 2. Our model exhibits excellent capability of detecting both color and luminance boundaries synchronously in a time-saving manner.

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