Using Orientation Information for Qualitative Spatial Reasoning

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A presentation of Freksa's 1992 paper on qualitative spatial reasoning with cardinal directions.

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Using Orientation Information for Qualitative Spatial Reasoning

  1. 1. Using Orientation Information for Qualitative Spatial Reasoning Christian Freksa, 1992 presented by Amenity Applewhite 23.5.2008 1
  2. 2. Outline • Introduction • Motivation • Previous approaches • Argument for qualitative orientation • Directional orientation in 2D • Augmenting qualitative relations • Conceptual neighborhood theory • Using the orientation-based framework • Applications • Further work 2
  3. 3. Introduction An approach to represent spatial knowledge using qualitative, neighborhood-oriented spatial information. 3
  4. 4. Motivation: Why qualitative? Quantitative knowledge obtained by measuring: 4
  5. 5. Motivation: Why qualitative? Quantitative knowledge obtained by measuring: “thirteen centimeters” 5
  6. 6. Motivation: Why qualitative? Quantitative knowledge obtained by measuring: “thirteen centimeters” • Requires mapping between object domain and scale domain • Mapping can produce distortions 6
  7. 7. Motivation: Why qualitative? Qualitative knowledge obtained by comparison rather than measuring: 7
  8. 8. Motivation: Why qualitative? Qualitative knowledge obtained by comparison rather than measuring: “longer” 8
  9. 9. Motivation: Why qualitative? Qualitative knowledge obtained by comparison rather than measuring: “longer” • Direct evaluation entirely within object domain • Focuses knowledge processing on information relevant to decision making 9
  10. 10. Motivation: Why spatial? • Spatial reasoning is essential to numerous actions and decisions • Arguably, physical space is more fundamental than logical reason: – Spatial reasoning more “primitive” in nature – Logic as an abstraction of spatial reasoning 10
  11. 11. Previous approaches to qualitative spatial reasoning Cartesian framework Güsgen, 1989 11
  12. 12. Previous approaches to qualitative spatial reasoning Cartesian framework Güsgen, 1989 String representations Chang & Jungert, 1986 12
  13. 13. Previous approaches to qualitative spatial reasoning Cartesian framework Güsgen, 1989 String representations Chang & Jungert, 1986 Object-boundary and interior intersections Egenhofer & Franzosa, 1991 13
  14. 14. Previous approaches to qualitative spatial reasoning Cartesian framework Güsgen, 1989 String representations Chang & Jungert, 1986 Object-boundary and interior intersections Egenhofer & Franzosa, 1991 Cardinal direction grids Frank, 1991 14
  15. 15. Why qualitative orientation? Availability of spatial information: • Qualitative orientation is available through pure perception • Other representations, such as Cartesian coordinates or cardinal orientation, refer to extra-perceptual information 15
  16. 16. Directional orientation in 2D Directional orientation a 1D feature determined by an oriented line Oriented line specified by an ordered set of two points A B orientation ab 16
  17. 17. Directional orientation in 2D Directional orientation a 1D feature determined by an oriented line Oriented line specified by an ordered set of two points A B orientation ba 17
  18. 18. Directional orientation in 2D Relative Orientation specified by two oriented lines Orientation of line bc relative to line ab 18
  19. 19. Properties of qualitative orientation wrt. location b and orientation ab 19
  20. 20. Properties of qualitative orientation wrt. location b and orientation ab 20
  21. 21. Properties of qualitative orientation Same transitive if ab=bc and bc=cd then ab=cd wrt. location b and orientation ab 21
  22. 22. Properties of qualitative orientation Same transitive if ab=bc and bc=cd then ab=cd wrt. location b and orientation ab 22
  23. 23. Properties of qualitative orientation Same transitive if ab=bc and bc=cd then ab=cd wrt. location b and orientation ab 23
  24. 24. Properties of qualitative orientation Same transitive if ab=bc and bc=cd then ab=cd wrt. location b and orientation ab 24
  25. 25. Properties of qualitative orientation Same transitive if ab=bc and bc=cd then ab=cd Opposite periodic wrt. location b and orientation ab 25
  26. 26. Properties of qualitative orientation Same transitive if ab=bc and bc=cd then ab=cd Opposite periodic opposite ∞ left yields right wrt. location b and orientation ab 26
  27. 27. Properties of qualitative orientation Same transitive if ab=bc and bc=cd then ab=cd Opposite periodic opposite ∞ left yields right wrt. location b and orientation ab 27
  28. 28. Properties of qualitative orientation Same transitive if ab=bc and bc=cd then ab=cd Opposite periodic opposite ∞ left yields right opposite ∞ opposite ∞ left yields left wrt. location b and orientation ab 28
  29. 29. Properties of qualitative orientation Same transitive if ab=bc and bc=cd then ab=cd Opposite periodic opposite ∞ left yields right opposite ∞ opposite ∞ left yields left opposite ∞ opposite ∞ opposite ∞ wrt. location b and orientation ab left yields right 29
  30. 30. Properties of qualitative orientation Same transitive if ab=bc and bc=cd then ab=cd Opposite periodic opposite ∞ left yields right opposite ∞ opposite ∞ left yields left opposite ∞ opposite ∞ opposite ∞ wrt. location b and orientation ab left yields right Orientation is a circular dimension 30
  31. 31. Augmenting qualitative relations Front-back segmentation • cognitively meaningful to people and animals • most objects have this implicit dichotomy 8 disjoint orientation relations 0 straight-front straight-back 4 1 right-front 5 left-back 2 right-neutral 6 left-neutral 3 right- back 7 left-front 31
  32. 32. Orientation-based qualitative location + = front-back dichotomy wrt. front-back dichotomy wrt. ab in b ba in a 32
  33. 33. Orientation-based qualitative location c + = front-back dichotomy wrt. front-back dichotomy wrt. ab in b ba in a 33
  34. 34. Conceptual neighborhood Conceptual neighbor a relation that represents a direct transition in the object domain from the initial relation • Based on studies of temporal cognition • Cognitive & computational advantages 34
  35. 35. Conceptual neighborhood • Conceptual neighbor a relation that represents a direct transition in the object domain from the initial relation 35
  36. 36. Conceptual neighborhood • Conceptual neighbor a relation that represents a direct transition in the object domain from the initial relation 36
  37. 37. Conceptual neighborhood • Conceptual neighbor a relation that represents a direct transition in the object domain from the initial relation Conceptual neighbors: i and 6 i and 7 i and 0 6 and 7 7 and 0 37
  38. 38. Conceptual neighborhood • Conceptual neighbor a relation that represents a direct transition in the object domain from the initial relation Conceptual neighbors: i and 6 i and 7 i and 0 6 and 7 7 and 0 Not conceptual neighbors: Require intermediate relations 6 and 0 38
  39. 39. Conceptual neigborhood in represented domain 39
  40. 40. Conceptual neigborhood in represented domain 40
  41. 41. Conceptual neigborhood in represented domain • 15 qualitative relations • 105 (unordered) pairs • 30 conceptual neighbors 41
  42. 42. Conceptual neigborhood in represented domain Utility • Reflects represented world • Reasoning strategies entirely within the represented domain • Assist domain visualization • Computationally restrict problem space to feasible operations 42
  43. 43. Orientation-based qualitative distance • Finer spatial resolution conveys distance • Does not increase orientation precision 43
  44. 44. Represented entities Most approaches • spatially extended objects • convex or rectangular shapes Points as basic entities, fundamental approach • Properties & relations hold for entire spatial domain • Shapes can be described as points with various levels of abstraction and precision; flexible 0D point city on wide area map 1D extension length of a road 2D projection area of a lake 3D constellation shape or group of objects 44
  45. 45. Qualitative spatial reasoning Using the orientation-based framework for inferences • Describe one spatial vector with relation to another • Infer unknown vector relations based on known relations 45
  46. 46. Qualitative spatial reasoning Using the orientation-based framework for inferences Given • relation of vector bc to vector ab • relation of vector cd to vector bc Infer location of d to vector ab 46
  47. 47. Qualitative spatial reasoning Using the orientation-based framework for inferences • Consider front-back dichotomies for known vectors 47
  48. 48. Qualitative spatial reasoning Using the orientation-based framework for inferences • Consider front-back dichotomies for known vectors • c right-front (1) ab 48
  49. 49. Qualitative spatial reasoning Using the orientation-based framework for inferences • Consider front-back dichotomies for known vectors • c right-front (1) ab • d right-back (3) cb 49
  50. 50. Qualitative spatial reasoning Using the orientation-based framework for inferences • Consider front-back dichotomies for known vectors • c right-front (1) ab • d right-back (3) cb Wrt. to original vector ab, vector bd is either right-front (1), front (0), or left front (2) 50
  51. 51. Composition table • Organize qualitative orientation-based inferences • Neighboring rows and columns show conceptual neighbors 51
  52. 52. Composition table • Initial conditions 52
  53. 53. Composition table • Possible locations of c 53
  54. 54. Composition table • Possible locations of d 54
  55. 55. Composition table • Orientation-less c=d d=b 55
  56. 56. Composition table 56
  57. 57. Composition table 57
  58. 58. Composition table 25% of all cases hold uncertainty • neighboring possibilities increase orientation angle up to 180˚ • degree of uncertainty is precisely known 58
  59. 59. Composition table r orientation of c wrt. ab s the orientation of d wrt. bc t orientation of d wrt. ab 59
  60. 60. 4 Composition table 3 5 2 6 1 0 7 r orientation of c wrt. ab s the orientation of d wrt. bc t orientation of d wrt. ab 7 0 1 6 2 5 3 4 60
  61. 61. 4 Composition table 3 5 2 6 1 0 7 r orientation of c wrt. ab s s the orientation of d wrt. bc 3 t orientation of d wrt. ab 7 0 1 6 2 r 1 5 3 4 61
  62. 62. Composition table r orientation of c wrt. ab s s the orientation of d wrt. bc 3 t orientation wrt. ab r and s are odd: t r t ={(r+s-1)…(r+s+1)} mod 8 1 7,0,1 t ={(1+ 3 -1)…(1+ 3+ 1)} mod 8 t = {3 … 5} mod 8 62
  63. 63. Fine grain composition table • Higher resolution • 2 front-back dichotomies • Produced by producing sub-rows and sub-columns from previous table 63
  64. 64. Applications • Determine an unknown location in space based on own location and known location • Wayfinding & route descriptions 64
  65. 65. Referencing work 257 citations in Google Scholar A few examples: – Computational methods for representing geographical concepts (Egenhofer, Glasgow, et al) – Schematic maps for robot navigation (Freksa, et al) – Pictorial language for retrieval of spatial relations from image databases (Papadias, et al) 65
  66. 66. Questions? 66

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