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CSE 803 Fall 20091 2D matching part 2 Review of alignment methods and errors in using them Introduction to more 2D matching methods.

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Presentation on theme: "CSE 803 Fall 20091 2D matching part 2 Review of alignment methods and errors in using them Introduction to more 2D matching methods."— Presentation transcript:

1 CSE 803 Fall D matching part 2 Review of alignment methods and errors in using them Introduction to more 2D matching methods

2 CSE 803 Fall Review of roadmap: algorithms to control matching

3 CSE 803 Fall Rigid transformation review

4 CSE 803 Fall Affine includes scaling and shear

5 CSE 803 Fall Problems with error Least squares fitting uses n >> 3 point pairs Significantly reduces error across field Will still be thrown off by outliers * can throw out pairs with high error and then refit * can set the “weight” of any pair to be inversely proportional to error squared

6 CSE 803 Fall Sources of error * Wrong matching in the pair of points yields outlier

7 CSE 803 Fall Point alignment error due to error in locations of Q1, Q2 Plastic slides can actually be overlaid for better viewing.

8 CSE 803 Fall Remove outlier and refit Plastic slides show concept better.

9 CSE 803 Fall Sometimes a halucination 6 points match, but the objects do not. Can verify using more model points.

10 CSE 803 Fall Local Feature Focus Method (Bolles)

11 CSE 803 Fall Local focus feature matching Local features tolerate occlusion by other objects (binpicking problem) Subgraph matching provides several features (distances, angles, connections, etc.) Method can be used to support different higher level strategies and alignment parameters

12 CSE 803 Fall Focus features matching attempts

13 CSE 803 Fall Pose clustering (generalized Hough transform) Use m minimal sets of matching features, each just enough to compute alignment Vector of alignment parameters is put as evidence into “parameter space” When all m units of evidence computed, examine parameter space for cluster[s]

14 CSE 803 Fall Pose clustering

15 CSE 803 Fall Line segment junctions for matching

16 CSE 803 Fall “Abstract vectors” subtending detected junctions Abstract vector with tail at T and tip at Y, or tail at L and tip at X MAPIMAGE

17 CSE 803 Fall Parameter space resulting from 10 vector matches Rotation, scale, translation computed as in single match alignment. Use the cluster center to estimate best alignment parameters.

18 CSE 803 Fall Detecting airplanes on airfield

19 CSE 803 Fall Airplane model of abstract vectors; detected image features

20 CSE 803 Fall Relational matching method

21 CSE 803 Fall Some relations between parts

22 CSE 803 Fall Recognition via consistent labeling

23 CSE 803 Fall Parts, labels, relations

24 CSE 803 Fall In a consistent labeling “image” parts relate as do “model” parts

25 CSE 803 Fall Distance relation often used

26 CSE 803 Fall What model labels apply to detected holes H1, H2, H3?

27 CSE 803 Fall Partial Interpretation Tree to find a distance consistent labeling The IT shows matching attempts that can be tried using a backtracking algorithm. If a relation fails the algorithm tries a different branch.

28 CSE 803 Fall Detailed IT algorithm Current matching pairs can be stored in the recursive stack. If a new pair is consistent with the previous pairs, continue forward; if not, then back up (and retract the recent pairing).

29 CSE 803 Fall

30 CSE 803 Fall Discrete relaxation labeling constrains possible labels A sometimes useful method that once drew much interest (see pubs by Rosenfeld, Zucker, Hummel, etc.) The Marr-Poggio stereo matching algorithm has the character of relaxation.

31 CSE 803 Fall Discrete relaxation labeling constrains possible labels

32 CSE 803 Fall Kleep matching via relaxation

33 CSE 803 Fall Removing a possible label for one part affects labels for related parts

34 CSE 803 Fall Relaxation labeling Can work truly in parallel Pairwise constraints are weaker than what the IT method can check, so sometimes the IT must follow the relaxation method There is “probabalistic relaxation” which changes probability of labels rather than just keeping or deleting them Relaxation was once thought to model human visual processes.


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