Shape Matching and Object Recognition Using Shape Contexts

2,621 views

Published on

I gave this presentation from an IEEE transection on patternt analysis and machine intelligence, Vol. 24, No. 24, April 2002.

Published in: Technology, Business, Travel
1 Comment
1 Like
Statistics
Notes
No Downloads
Views
Total views
2,621
On SlideShare
0
From Embeds
0
Number of Embeds
18
Actions
Shares
0
Downloads
93
Comments
1
Likes
1
Embeds 0
No embeds

No notes for slide

Shape Matching and Object Recognition Using Shape Contexts

  1. 1. Seminar On CSE-4102<br />Shape Matching and Object Recognition<br />Using<br />Shape Contexts<br />1<br />Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh<br />
  2. 2. <ul><li>It is easy for human to make difference between two similar object.
  3. 3. It is difficult for machine to make difference between two similar object.</li></ul>2<br />Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh<br />
  4. 4. 3<br />Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh<br />
  5. 5. Shape Context:<br /><ul><li>It is Shape descriptor that play the role of shape matching.</li></ul>Log polar histogram<br />Sample(a)<br />Sample(b)<br />Correspond found using bipartite matching<br />4<br />Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh<br />
  6. 6. Bipartite graph matching:<br /><ul><li>If cij denotes the cost between two point the cost is determined by:</li></ul>Where,<br /> pi is a point on the first shape. (shape (a)).<br />pj is a point on the second shape.(shape(b)).<br /><ul><li>The concept of using dummy node. To minimize Total cost.
  7. 7. Total cost of matching:</li></ul>5<br />Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh<br />
  8. 8. Idle state:<br />Regularization :<br /><ul><li>We use affine model to choose a suitable family of transformation.
  9. 9. A standard choice of affine model:</li></ul>T(x)=Ax+o<br /><ul><li>We use TPS(Thin Plate Spline) model transformation.
  10. 10. If there is noise in specified values then the interpolation is relaxed by regularization.
  11. 11. Regularization parameter determine the amount of smoothing.</li></ul>6<br />Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh<br />
  12. 12. 7<br />Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh<br />
  13. 13. 8<br />Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh<br />
  14. 14. 9<br />Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh<br />
  15. 15. 10<br />Digit recognation:<br />Error is only 63 % using 20,000 training example.<br />Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh<br />
  16. 16. 11<br />3-D object detection:<br />Using 72 view per object.<br />Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh<br />
  17. 17. 12<br /><ul><li>A key characteristics of this approach is estimation of shape similarities and correspondence depends upon shape context.
  18. 18. In the experiment gray-scaled picture is used.
  19. 19. Some algorithm are modified while experimenting.</li></ul>Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh<br />
  20. 20. Thank you<br />13<br />Qudrat-E-Alahy Ratul, KUET, Khulna, Bangladesh<br />

×