1. Image Search Using Deformable Contours By : Preeyakorn Tipwai Advisor : Suthep Madarasmi, Ph.D Computer Vision Laboratoy, Computer Engineering Department King Mongkut’s University of Technology Thonburi

2. Problem A target is assumed to be a scaled, rotated version of the template with edges distorted

3. Methodology Inspiration Jain et al [1], “Object Matching Using Deformable Templates” Our Methodogy Finding Hypotheses : MGHT Peak Clustering : Watershed Method Contour Matching : Smooth Membrane Fitting

4. Preprocessing Given a sketched template Find tangent direction Given a target image Calculte edge map : Canny Edge Detection Find tangent direction

5. MGHT A line at the contour edge is extended in the  direction until it meets the other end of the contour  r 1,  1,  1, l 1 r 2,  2,  2, l 2 r 3,  3,  3, l 3 r 4,  4,  4, l 4 0...1915,180,195,999,179,219,1018,177,216,1029,176,198,100 20...3917,160,23,514,159,38,718,161,175,6215,162,195,95 30…4919,165,31,5320,170,8,5222,167,15,5218,159,158,12 …………… 340...3 59 23,105,346,1124,103,165,1121,102,346,1822,104,195,24 R-Table

6. MGHT  Invariant rotation and scale of   = 30  -200  = -170  = 190  = 300  -110  = 190 

7. MGHT Rotation Factor: Scaling Factor : New ref. Point : x c = x + S r cos (  y c = y + S r sin ( 

8. Watershed for Peak Clustering 1.Shed, by labeling, at the first level, calculate peaks of each label 2.Increase to higher level, shed again 2.1Meet an area of previous level, shed to that area 2.2Not meet any area of previous level, make a new area, calculate a new peak

9. Deformation : Contour Matching Parameter :  x  y  or (u,v)

10. Grid Matching : Data and Smoothness Constraints Inter-grid Matching: Consistency between adjacent grids Coarse and Fine Matching

11. Inter-grid Matching: Example

12. Matching Algorithm Update (u,v) : Gibbs Sampler with simulated annealing to minimize energy function Template Target Edge

13. Experiment on Contour Matching Template TargetEdge Result

14. Template TargetEdge Result

15. Experiment on Image Search TemplateTarget Edge MapResult Hough Space

16. Experiment on Image Search Target Edge Hypotheses 1st Match 2nd Match 3rd Match4th Match

17. Experiment on Image Search TemplateTarget Edge MapThe Best MatchHough Space

18. Experiment on Image Querying Database Search for Circle shape Search for bulb shape

19. Conclusion A deformation model Contour Matching A method for image search Future work: large image database, efficient method for minimizing energy, coarse-and- fine approach to computer vison modules

20. Similarity Retrieval Effectiveness circle shape heart shape bulb shape max : 100, min : 96 ave : 98 max : 100, min : 92 ave : 95 max : 96, min : 8 ave : 75

21. Experimental Result 3.986274 0.929011 2.705226 TemplateTarget Edge Hypotheses Threshold : 1.0 - 2.6

22. Experimental Result 2.1654880.965049 1.755835 Threshold : 1.0-1.6 Template Target EdgeHypotheses

23. Experimental Result 1.7992671.114566 5.074061 Threshold : 1.2-1.6 Template Edge Map Hypotheses

24. Experimental Result TemplateTarget Edge Hypotheses0.8686000.8797993.799124 Threshold : 0.9-3.6

25. Experimental Result Template Target EdgeHypotheses 1.293034 1.4521303.364521 4.4185782 Threshold : 1.5-3.2

26. Energy Threshold