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Image Search Using Deformable Contours

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. A target is assumed to be a scaled, rotated version of the template

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Image Search Using Deformable Contours

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

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

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

  5. r1, a1, q1, l1 r2, a2, q2, l2 r3, a3, q3, l3 r4, a4, q4, l4 0...19 15,180,195,99 9,179,219,101 8,177,216,102 9,176,198,100 20...39 17,160,23,5 14,159,38,7 18,161,175,62 15,162,195,95 30…49 19,165,31,53 20,170,8,52 22,167,15,52 18,159,158,12 … … … … … 340...359 23,105,346,11 24,103,165,11 21,102,346,18 22,104,195,24 MGHT A line at the contour edge is extended in the g direction until it meets the other end of the contour R-Table

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

  7. MGHT Rotation Factor: xc = x + S r cos (a + b) yc = y + S r sin (a + b) Scaling Factor : New ref. Point :

  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 2.1 Meet an area of previous level, shed to that area 2.2 Not meet any area of previous level, make a new area , calculate a new peak

  9. Parameter : (Dx,Dy) or (u,v) Deformation : Contour Matching

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

  11. Coarse and Fine Matching • 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 Target Edge Result

  14. Experiment on Contour Matching Template Target Edge Result

  15. Experiment on Image Search Template Target Edge Map Result Hough Space

  16. Experiment on Image Search 1st Match Hypotheses Target Edge 2nd Match 3rd Match 4th Match

  17. Experiment on Image Search Template Target Edge Map Hough Space The Best Match

  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 heart shape bulb shape circle shape max : 100, min : 96 ave : 98 max : 100, min : 92 ave : 95 max : 96, min : 8 ave : 75

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

  22. Experimental Result Template Target Edge Hypotheses Threshold : 1.0-1.6 1.755835 2.165488 0.965049

  23. Experimental Result Template Hypotheses Edge Map Threshold : 1.2-1.6 5.074061 1.799267 1.114566

  24. Experimental Result Template Target Edge Hypotheses 0.868600 0.879799 3.799124 Threshold : 0.9-3.6

  25. Experimental Result Threshold : 1.5-3.2 Template Target Edge Hypotheses 1.293034 1.452130 3.364521 4.4185782

  26. Energy Threshold

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