1 / 115

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.

landryp
Download Presentation

Computer and Robot Vision II

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related