1 An Implementation Sanun Srisuk of EdgeFlow

2 Outline Problem Statement Theory & Algorithm Results Conclusion

3 Problem Statement Segmentation using small scaleSegmentation using large scale

4 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.

5 Gaussian Function

6 3-D GD mask

7 GD mask in different theta

8 3-D DOOG mask

9 DOOG mask in different theta

10 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

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

12 Conventional Edge Detection & EdgeFlow

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

14 Results red greenblue intensity

15 Intensity & Texture EdgeFlow

16 Total EdgeFlow

17 Segmentation Results

18 Segmentation Results

19 EdgeFlow

20 EdgeFlow

21 EdgeFlow Results

22 EdgeFlow Results

23 EdgeFlow Results

24 EdgeFlow Results

25 Segmentation results in different theta

26 Segmentation results in different theta

27 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.

28 The End.