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Computer and Robot Vision II

Computer and Robot Vision II. Chapter 18 Object Models And Matching. Presented by: 傅楸善 & 張博思 0911 246 313 r94922093@ntu.edu.tw 指導教授 : 傅楸善 博士. 18.1 Introduction. object recognition: one of most important aspects of computer vision. 18.2 Two-Dimensional Object Representation.

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Computer and Robot Vision II

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  1. Computer and Robot Vision II Chapter 18 Object Models And Matching Presented by: 傅楸善 & 張博思 0911 246 313 r94922093@ntu.edu.tw 指導教授: 傅楸善 博士 Digital Camera and Computer Vision Laboratory Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan, R.O.C.

  2. 18.1 Introduction • object recognition: one of most important aspects of computer vision DC & CV Lab. CSIE NTU

  3. 18.2 Two-Dimensional Object Representation • 2D shape analysis useful in machine vision application: • medical image analysis • aerial image analysis • manufacturing DC & CV Lab. CSIE NTU

  4. 18.2 Two-Dimensional Object Representation • 2D shape representation classes: • global features • local features • boundary description • skeleton • 2D parts DC & CV Lab. CSIE NTU

  5. 18.2.1 Global Feature Representation • 2D object: can be thought of as binary image • value 1: pixels of object • value 0: pixels outside object • 2D shape features: area, perimeter, moments, circularity, elongation DC & CV Lab. CSIE NTU

  6. 18.2.1 Global Feature Representation • Shape Recognition by Moments • f: binary image function • : 2D shape • digital th moment of S: • area of S: number of pixels of S DC & CV Lab. CSIE NTU

  7. 18.2.1 Global Feature Representation • moment invariants: functions of moments invariant under shape transform • prefer moment invariants: under translation, rotation, scaling • skewing center of gravity of S: DC & CV Lab. CSIE NTU

  8. 18.2.1 Global Feature Representation • central th moment of S: • central moments: translation invariant • normalized central moments of S: DC & CV Lab. CSIE NTU

  9. 18.2.1 Global Feature Representation • seven functions that are rotation invariant DC & CV Lab. CSIE NTU

  10. 18.2.1 Global Feature Representation • Shape Recognition with Fourier Descriptors • Fourier descriptors: another way for extracting features from 2D shapes • Fourier descriptors: defined to characterize boundary • The main idea is to represent the boundary as a function of one variable , expand in its Fourier series, and use the coefficients of the series as Fourier descriptors (FDs). • finite number of FDs: can be used to describe the shape DC & CV Lab. CSIE NTU

  11. 18.2.1 Global Feature Representation DC & CV Lab. CSIE NTU

  12. 18.2.1 Global Feature Representation DC & CV Lab. CSIE NTU

  13. 18.2.1 Global Feature Representation DC & CV Lab. CSIE NTU

  14. 18.2.2 Local Feature Representation • 2D object characterized by: local features, attributes, relationships • most commonly used local features: holes, corners • holes: found by connected component procedure followed by boundary tracing • holes: detected by binary mathematical morphology, if hole shapes known • hole properties: areas, shapes • corner detection: can be performed on binary or gray tone image • corner property: angle at which lines meet DC & CV Lab. CSIE NTU

  15. joke DC & CV Lab. CSIE NTU

  16. 18.2.3 Boundary Representation • boundary representation: most common representation for 2D objects • 3 main ways to represent object boundary: • 1. sequence of points • 2. chain code • 3. sequence of line segments DC & CV Lab. CSIE NTU

  17. 18.2.3 Boundary Representation • The Boundary as a Sequence of Points • boundary points from border-following or edge-tracking algorithms • interest points: boundary points with special property useful in matching DC & CV Lab. CSIE NTU

  18. 18.2.3 Boundary Representation • The Chain Code Representation • chain encoding: can be used at any level of quantization • chain encoding: saves space required for row and column coordinates • boundary encoded: first quantized by placing over square grid • grid side length: determines resolution of encoding • marked points: grid intersections closest to curve and used in encoding • : marks starting point of curve DC & CV Lab. CSIE NTU

  19. 18.2.3 Boundary Representation • chain encoding of boundary curve DC & CV Lab. CSIE NTU

  20. 18.2.3 Boundary Representation • line segments: links: to be used to approximate the curve • encoding scheme: eight possible directions assigned integer between 0, 7 • chain: chain encoding: in the form or DC & CV Lab. CSIE NTU

  21. 18.2.3 Boundary Representation • length of chain code with n chains: can be simply estimated as n • : number of odd chain codes • : number of even chain codes • : number of corners • : unbiased estimate of perimeter length • Freeman suggested: DC & CV Lab. CSIE NTU

  22. 18.2.3 Boundary Representation • The Boundary as a Sequence of Line Segments • line segment sequence: after boundary segmented into near-linear portion • line segment sequence: used in shape recognition or other matching tasks • : coordinate location where pair of lines meet • : angle magnitude where pair of lines meet • sequence of junction points to represent line segment sequence DC & CV Lab. CSIE NTU

  23. 18.2.3 Boundary Representation • sequence of junction points representing test object T • an association • goal: given O, T, to find F satisfying i < j F(i) < F(j) or F(i) = missing or F(j) = missing DC & CV Lab. CSIE NTU

  24. 18.2.4 Skeleton Representation • strokes: long, sometimes thin parts forming shapes • line segments that characterize the strokes of set of characters DC & CV Lab. CSIE NTU

  25. 18.2.4 Skeleton Representation • symmetric axis transform: set of maximal circular disks inside object • symmetric axis: locus of centers of these maximal disks • symmetric axes of the characters DC & CV Lab. CSIE NTU

  26. 18.2.4 Skeleton Representation • symmetric axis: one example of skeleton description of 2D object • symmetric axis of rectangle: consists of five line segments not single line • symmetric axis: extremely sensitive to noise • symmetric axis: difficult to use in matching DC & CV Lab. CSIE NTU

  27. DC & CV Lab. CSIE NTU

  28. 18.2.4 Skeleton Representation • axis of smoothed local symmetries: separate definition for skeleton • local symmetry: midpoint P of line segment BA joining pair of points A, B • : angle between BA and outward normal at A • : angle between BA and inward normal at B DC & CV Lab. CSIE NTU

  29. 18.2.4 Skeleton Representation • point P that is local symmetry with respect to boundary points A and B DC & CV Lab. CSIE NTU

  30. 18.2.4 Skeleton Representation • axes: spines: loci of local symmetries maximal w.r.t. forming smooth curve • cover of axis: portion of shape subtended by axis • axis cover properly contained in another cover: second axis subsumes first DC & CV Lab. CSIE NTU

  31. 18.2.4 Skeleton Representation • symmetric axes of local symmetry of a rectangle DC & CV Lab. CSIE NTU

  32. 18.2.4 Skeleton Representation • axes of smoothed local symmetries of several objects DC & CV Lab. CSIE NTU

  33. 18.2.5 Two-Dimensional Part Representation • parts, attributes, interrelationships: form structural description of shape • nuclei: regions where primary convex subset overlap • nuclei: shaded areas of overlap DC & CV Lab. CSIE NTU

  34. 18.2.5 Two-Dimensional Part Representation • decomposition of shape into primary convex subsets and nuclei DC & CV Lab. CSIE NTU

  35. 18.2.5 Two-Dimensional Part Representation • near-convexity: allows noisy distorted instances to have same decompositions • , : two points on object boundary • relation: visibility relation • if line completely interior to object boundary, • the graph-theoretic clustering to determine clusters of visibility relation DC & CV Lab. CSIE NTU

  36. 18.2.5 Two-Dimensional Part Representation • decomposition of three similar shapes into near-convex pieces DC & CV Lab. CSIE NTU

  37. joke DC & CV Lab. CSIE NTU

  38. 18.3 Three-Dimensional Object Representations DC & CV Lab. CSIE NTU

  39. 18.3.1 Local Features Representation • range data: obtained from laser range finder, light striping, stereo, etc. • from depth, try to infer surfaces, edges, corners, holes, other features • 3D matching more difficult than 2D because of occlusion DC & CV Lab. CSIE NTU

  40. 18.3.2 Wire Frame Representation • wire frame model: 3D object model with only edges of object DC & CV Lab. CSIE NTU

  41. 18.3.2 Wire Frame Representation • two-color hyperboloid and its line drawing DC & CV Lab. CSIE NTU

  42. 18.3.2 Wire Frame Representation DC & CV Lab. CSIE NTU

  43. 18.3.2 Wire Frame Representation • Necker cube: lower-vertical face or upper vertical face closer to viewer • Schroder staircase: viewed either from above or from below DC & CV Lab. CSIE NTU

  44. two well-known ambiguous line drawings DC & CV Lab. CSIE NTU

  45. two well-known ambiguous line drawings DC & CV Lab. CSIE NTU

  46. two well-known ambiguous line drawings DC & CV Lab. CSIE NTU

  47. inherent ambiguity of line drawing owing to complete loss of depth DC & CV Lab. CSIE NTU

  48. 18.3.2 Wire Frame Representation • general-viewpoint assumption: none of the following situations • 1. two vertices of scene objects represented at same picture point • 2. two scene edges seen as single line in picture • 3. vertex seen exactly in line with unrelated edge DC & CV Lab. CSIE NTU

  49. 18.3.2 Wire Frame Representation • general-viewpoint assumption: heart of line-drawing interpretation • viewpoint in perspective projection: center of projection • viewpoint in orthographic projection: direction of projection DC & CV Lab. CSIE NTU

  50. subjective contours of Kanizsa: white occluding triangle in space DC & CV Lab. CSIE NTU

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