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An Implementation

Sanun Srisuk

42973003

of EdgeFlow

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Outline

Problem Statement Theory & Algorithm Results Conclusion

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Problem Statement

Segmentation using small scale Segmentation using large scale

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Theory & Algorithm

is a pixel in an image.

is an edge energy at location s along the orientation theta.

is the probability of finding an image boundary in the direction theta from s.

is the probability of finding an image boundary in the direction theta+pi from s.

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Gaussian Function

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3-D GD mask

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GD mask in different theta

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3-D DOOG mask

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DOOG mask in different theta

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Intensity Edges

The edge energy E(s, theta) at scale sigma is defined to be the magnitude of the gradient of the smoothed image , which is obtained by smoothing the original image I(x,y) with a Gaussian kernel

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Intensity Edges

P(s, theta) is the probability of finding an image boundary in the direction theta from s.

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Conventional Edge Detection & EdgeFlow

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Edge Flow Vector

where is a complex number with its magnitude representing the resulting edge energy and angle representing the flow direction.

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Results

red green blueintensity

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Intensity & Texture EdgeFlow

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Total EdgeFlow

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Segmentation Results

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Segmentation Results

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EdgeFlow

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EdgeFlow

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EdgeFlow Results

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EdgeFlow Results

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EdgeFlow Results

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EdgeFlow Results

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Segmentation results in different theta

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Segmentation results in different theta

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Conclusion

EdgeFlow using a predictive coding model to identify and integrate the direction of change in image attributes such as color, texture, and phase discontinuities, at each image location.

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The End.