1. Biomimetics Pattern Recognition and Machine Thinking in Image Lab of Artificial Neural Networks & Machine Thinking in Image, Institute of Semiconductors, CAS ( 中科院半导体所神经网络与形象思维实验室 ) Wang Shoujue( 王守觉 ) 2004.6

2. 1. Development of Information sciences in recent five decades

3. Comparison between 1950 and 2000

4. computing speed, storage capacity, quality of intelligence computing speed : thousands billion calculation per second, corresponds to about 10 12 times of human brain, as 100 times of total number of human being all over the world. storage capacity : a 100G hard disk corresponds to all information included in a library with 100000 books. quality of intelligence : not even comparable with an animal

5. a 、 Thinking in Logic b 、 Thinking in Image Two kinds of thinking in human brain

6.  3.14159265358… ? whole life paid for ‘  ’ calculating

7. Baby recognizes its mother but doesn’t know 1+1=?

8. the Way to Solve the Image problem to solve image problem by symbolic logic description to solve image problem by connectionism computing ( artificial neural networks)

9. 2. Discussion on a Basic Problem of Information Sciences

10. （ 1 ） What’s Information

11. In digital world, any information should be described as large amounts of digital numbers

12. a picture, a photo, a speech, a knowledge each of them corresponds to a point in the High Dimensional Space

13. Basic general problem in information sciences —— Point Set Analysis in the High Dimensional Space

14. (2) A brief review of conventional concepts, from point set analysis in the High Dimensional Space

15. signal in time domain corresponds to a point in high dimensional space

16. (x 1,x 2,……x n ) A signal in time domain— large amount of digital numbers — a point in the High Dimensional Space x1x1 x5x5 n x x a point in Rn

17. Fourier Transformation

18. ... sin , sin2 , sin3 , …... cos , cos2 , cos3 , …... O

19. “there are no more than n lines existed, which perpendicular to each other, in n-dimensional space”

20. Theorem Nyquist Sampling Theorem

22. Principal Component Analysis ( P C A )

23. （ 3 ） high dimensional geometrical concepts are useful for developing new algorithms for Point Sets Analysis

26. A A B C D A A A 1 1 2 3 4

27. O f I A C B H d g K e J m

28. A new method to get sharper picture from a blur picture

29. Original picture ( blur ) Final picture ( sharper )

31. blur sharper

33. 3. Biomimetics Pattern Recognition —— application of High Dimensional Geometrical Point Set Analysis in pattern recognition

34. (1) discussing a basic conception

35. What’s the job of What’s the job of Pattern Recognition

36. (2) The Conceptional Start Point of Biomimetics Pattern Recognition

37. Pattern Recognition classification separation （ conventional Pattern Recognition ） cognition ( Biomimetics Pattern Recognition ) （ better close to the fact of human being ）

39. (3) Theoretical starting point of the Biomimetics Pattern Recognition The Principle of Homology-Continuity (PHC).

40. The difference between two samples of the same class must be gradually changed. So every sample in the gradually changing sequence between two samples, must be belonging to the same class.

41. The Mathematical Description of PHC: If A is a point set including all samples in class A in feature space, there must be a set B: B={ x 1, x 2, x 3, …, x n | x 1 = x, x n = y, n N, ρ(x m, x m+1 ) 0, n-1 m 1, m N }, B A

42. Conventional Pattern Recognition —— optimal classification of many classes Biomimetics Pattern Recognition —— cognizing different classes one by one, by the connectivity of samples in the same class (point set analysis in the High Dimensional Space)

43. (4) Actual results of Biomimetics Pattern Recognition compared with SVM (Support Vector Machine )

44. (a) Experiments on recognition of omnidirectionally oriented rigid objects on a plane

45. objects for recognition

46. objects for rejection testing

47. procedure in experiment number of training samples: 338 ~ 169 totally for 8 objects testing sample set A: 3200 samples for 8 objects ( training samples included ) testing sample set B: 3200 samples for 8 objects ( no training samples included ) testing sample set C: 2400 samples for 6 objects for correct rejection test

49. （ b ） human face recognizing

50. Olivetti Research Laboratory face database 40 persons, 10 pictures per each

51. ten pictures from one human face in ORL face database

52. 35 persons, 3 pictures / each 105 pictures as training set

53. testing set A: remained 7 pictures per each of the 35 persons. 7 × 35 ＝ 245 pictures testing set B: 10 pictures per each of the remained 5 persons. 10 × 5 ＝ 50 pictures for correct rejection testing

54. Results comparison of different recognition methods methods correct recognition test correct rejection test testing set A error rate testing set B error rate Minimum Distance （ RBF ） 4.90%22% Support Vector Machine ( SVM) 1.64%10% Biomimetics Pattern Recognition ( BPR ) 0.81%2%

55. Tools forpoint set analysis in the High Dimensional Space 4. Tools for point set analysis in the High Dimensional Space

56. High Dimensional descriptive geometry （ 1 ） High Dimensional descriptive geometry

59. 2 3 4 5 6 7 8 9 10

64. （ 2 ） multi-weight neural networks for high dimensional point set computing

65. mathematical model of neuron in the CASSANN-II neurocomputer W i : DIRECTION weight W i ’ : KERN weight mathematical model of a conventional neuron

66. Generalized mathematical model of an artificial neuron Y = F { distance from X to a manifold  } the equation of the manifold  is as follows:

67. Display in three dimension case

69. in 100 dimensional feature space if D 2 ＝ D 1 L ＝ 5D 1 V1V1 V 2 times hundred billion billion billion ( 10 29 ) D2D2 D1D1 L

70. 5. Make Machine Thinking in Image

71. Recognition of Imperfect Pictures

73. Experiment 1 For random Imperfection 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

74. 判别阈值

76. Experiment 2 For Imperfection in the middle 0% 4.35% 8.70% 13.04% 17.39% 21.74% 26.09% 30.43% 34.78% 39.13% 43.48% 47.83%

77. 判别阈值

79. Experiment 3 For Imperfection on one side 0% 4.35% 8.70% 13.04% 17.39% 21.74% 26.09% 30.43% 34.78% 39.13% 43.48% 47.83%

80. 判别阈值

82. 6. Conclusion point set analysis in the High Dimensional Space may be a new tool for making “ machine thinking in image” (1) Geometrical method of point set analysis in the High Dimensional Space may be a new tool for making “ machine thinking in image” (2) Biomimetics Pattern Recognition ( BPR ), an application of point set analysis in the High Dimensional Space, is much better than conventional pattern recognition such as SVM, RBF, etc.

83. Thank you for your attention!