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Behind The Scenes of the ISS - Database: Modern Techniques of Shape

Behind The Scenes of the ISS - Database: Modern Techniques of Shape Recognition and Database Retrieval. Dr. Rolf Lakaemper Dept. of Computer and Information Sciences Temple University. Overview. Topics: The Shape Recognition Algorithm Implemented in ISS

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Behind The Scenes of the ISS - Database: Modern Techniques of Shape

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  1. Behind The Scenes of the ISS - Database: Modern Techniques of Shape Recognition and Database Retrieval Dr. Rolf Lakaemper Dept. of Computer and Information Sciences Temple University

  2. Overview • Topics: • The Shape Recognition Algorithm Implemented in ISS • Possible Applications in Different Areas of Computer Vision • Database Query by Vantage Objects - an Alternative to Classical Clustering ?

  3. The Task Comparisonof shape – features...

  4. The Task ... without semantical knowledge.

  5. Results first… • ISS = Intelligent Shape Selection • Image Database providing query by • Keyword • Texture • Shape • Shape is given by user-sketch, a mouse-drawn outline

  6. ISS - GUI

  7. The Sketchpad: Query by Shape

  8. The First Guess: Different Shape - Classes

  9. Selected shape defines query by shape – class

  10. Result

  11. Key Steps Retrieval by Vantage Objects Retrieval by Direct Shape Comparison

  12. The 2nd Step First: Shape Comparison ISS implements the ASR (Advanced Shape Recognition) Algorithm Developed by Hamburg University in cooperation with Siemens AG, Munich, for industrial applications in... ... robotics ... multimedia (MPEG – 7)

  13. Reticent Proudness… MPEG-7: ASR outperformes classical approaches ! Similarity test (70 basic shapes, 20 different deformations): ASR Hamburg Univ./Siemens AG 76.45 % Curvature Scale Space Mitsubishi ITE-VIL 75.44 % Multilayer Eigenvector Hyundai 70.33 % Zernicke Moments Hanyang University 70.22 % Wavelet Contour Heinrich Hertz Institute Berlin 67.67 % DAG Ordered Trees Mitsubishi/Princeton University 60.00 % (Capitulation :-) IBM --.-- %

  14. Requirements Robust automatic recognition of arbitrary shaped objects which is in accord with human visual perception Wide range of applications... ... recognition of complex and arbitrary patterns ... invariance to basic transformations ... results which are in accord with human perception ... applicable to three main tasks of recognition ... parameter-free operation Industrial requirements... ... robustness ... low processing time

  15. Requirements Next: Strategy Scaling (or resolution) Rotation Robust automatic recognition of arbitrary shaped objects which is in accord with human visual perception Rigid / non-rigid deformation Wide range of applications... ... recognition of complex and arbitrary patterns ... invariance to basic transformations ... results which are in accord with human perception ... applicable to three main tasks of recognition ... parameter-free operation Industrial requirements... ... robustness ... low processing time

  16. Requirements Simple Recognition (yes / no) ... robustness ... low processing time Robust automatic recognition of arbitrary shaped objects which is in accord with human visual perception Common Rating (best of ...) Wide range of applications... ... recognition of complex and arbitrary patterns Analytical Rating (best of, but...) ... results which are in accord with human perception ... invariance to basic transformations ... applicable to three main tasks of recognition ... parameter-free operation Industrial requirements... ... robustness ... low processing time

  17. Different Approaches Pattern Matching... ... Correlation Geometrical description... ... Hough – Transformation Feature – Vectors... ... (Zernicke - ) Moments Based on Visual Parts... ... Mokhtarian ... ASR

  18. Curvature Scale Space (Mokhtarian, Mitsubishi) Creation of reflection-point based feature-vector which implicitely contains part – information

  19. Visual Parts Motivated by psychological experiments (Hoffmann/Richards): split bounding-curve into convex / concave arcs

  20. ASR: Strategy ASR: Strategy Source: 2D - Image Object - Segmentation Contour Extraction Evolution Contour – Segmentation Arc – Matching

  21. Scale Space Ordered set of representations on different information levels

  22. Curve Evolution Target: reduce data by elimination of irrelevant features, preserve relevant features ... noise reduction ... shape simplification:

  23. Curve Evolution: Tangent Space next: TS-properties Transformation from image-space to tangent-space bild s.22

  24. Tangent Space: Properties next: Step-Compensation In tangent space... ... the height of a step shows the turn-angle ... monotonic increasing intervals represent convex arcs ... height-shifting corresponds to rotation ... the resulting curve can be interpreted as 1 – dimensional signal => idea: filter signal in tangent space (demo: 'fishapplet')

  25. Curve Evolution: Step Compensation New nonlinear filter: merging of 2 steps with area – difference F given by: (a-b)pq p + q F = q a g b F F p

  26. Curve Evolution: Step Compensation Interpretation in image – space: ... Polygon – linearization ... removal of visual irrelevant vertices q p removed vertex

  27. Curve Evolution: Step Compensation next: Iterative SC Interpretation in image – space: ... Polygon – linearization ... removal of visual irrelevant vertices

  28. Curve Evolution: Iterative Step Compensation Keep it simple: repeated step compensation ! Remark: there are of course some traps ...

  29. Curve Evolution: Properties The evolution... ... reduces the shape-complexity ... is robust to noise ... is invariant to translation, scaling and rotation ... preserves the position of important vertices ... extracts line segments ... is in accord with visual perception ... offers noise-reduction and shape abstraction ... is parameter free ... is translatable to higher dimensions

  30. Curve Evolution: Properties back Robustness (demo: noiseApplet)

  31. Curve Evolution: Properties back Preservation of position, no blurring !

  32. Curve Evolution: Properties back Strong relation to digital lines and segments

  33. Curve Evolution: Properties back Noise reduction as well as shape abstraction

  34. Curve Evolution: Properties back Parameter free

  35. Curve Evolution: Properties back Extendable to higher dimensions

  36. Curve Evolution: Properties Extendable to higher dimensions

  37. Curve Evolution: Properties Extendable to higher dimensions

  38. Curve Evolution: Properties Extendable to higher dimensions

  39. Shape Comparison: Measure Tangent space offers an intuitive measure:

  40. Shape Comparison: Measure Drawback: not adaptive to unequally distributed noise Solution: partition bounding curve

  41. Shape Comparison: Contour Segmentation Solution: partition bounding curve

  42. Shape Comparison: Contour Segmentation Motivated by psychological experiments (Hoffmann/Richards): split bounding-curve into convex / concave arcs

  43. Shape Comparison: Correspondence next: Corr. -example Optimal arc-correspondence: find one to many (many to one) correspondence, that minimizes the arc-measure !

  44. Graph of Correspondence next: Corr. - Results arc a0 a3 a2 a0 a1 a2 a3 a1 b0 b0 b1 b2 b3 b3 b2 correspondence b1 Graph: ... edge represents correspondence ... node represents matched arcs

  45. Shape Comparison: Correspondence next: Corr. - Results Example: a0 a1 a2 a3 a0 a3 a2 a1 b0 b1 b2 b3 b0 b3 b2 b1

  46. Shape Comparison: Correspondence next: Corr. - Results Result: Optimal correspondence is given by cheapest way

  47. Correspondence: Results

  48. (Movie Deer.avi)

  49. Correspondence: Results Correspondence and arc-measure allow... ... the identification of visual parts as well as ... the identification of the entire object ... a robust recognition of defective parts ... a shape matching which is in accord with human perception

  50. ASR: Applications in Computer Vision • Robotics: Shape Screening • (Movie: Robot2.avi) • Straightforward Training Phase • Recognition of Rough Differences • Recognition of Differences in Detail • Recognition of Parts

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