Multi-modal estimation of collinearity and parallelism in natural image sequences

Network: Computation in Neural Systems

Volumn 13, Issue 4, 2002, Pages 553-576

  Krüger, Norbert ^a   Wörgötter, Florentin ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; PSYCHOLOGICAL MODEL; REVIEW;

ARTIFICIAL INTELLIGENCE; MODELS, PSYCHOLOGICAL;

EID: 19044394506     PISSN: 0954898X     EISSN: None     Source Type: Journal    
DOI: 10.1088/0954-898X/13/4/307     Document Type: Article

References (78)

1. Aloimonos J and Shulman D 1989 Integration of Visual Modules - An Extension of the Marr Paradigm (London: Academic)
2. Ayache N 1990 Stereovision Sensor Fusion (Cambridge, MA: MIT Press)
3. Barlow H 2001 Redundancy reduction revisited Network: Comput. Neural Syst. 12 241-54
4. Barlow H B 1961 Possible principles underlying the transformation of sensory messages Sensor Communication ed W Rosenblith (Cambridge, MA: MIT Press) pp 217-34
5. Barth E, Caelli T and Zetsche C 1993 Image encoding, labeling, and reconstruction from differential geometry Graph. Models Image Process. 55 428-46
6. Bell A J and Sejnowski T 1996 Edges are the 'independent components' of natural scenes Adv. Neural Information Process. Syst. 9 831-7
7. Bertenthal B I, Campos J J and Haith M M 1980 Development of visual organisation: the perception of subjective contours Child Dev. 51 1072-80
8. Blakemore C and Cooper G F 1970 Development of the brain depends on the visual environment Nature 228 477-8
9. Boyer K L and Sarkar S 1999 Perceptual organization in computer vision: status, challenges, and potential Percept. Organiz. Comput. Vis. 76 1-5 (special issue)
10. Brunswik E and Kamiya J 1953 Ecological cue-validity of 'proximity' and of other Gestalt factors Am. J. Psychol. LXVI 20-32
11. Chung RCK and Nevatia R 1995 Use of monocular groupings and occlusion analysis in a hierarchical stereo system Comput. Vis. Image Underst. 62 245-68
12. Cozzi A and Wörgötter F 2001 Comvis: a communication framework for computer vision Int. J. Comput. Vis. 41 183-94
13. Desolneux A, Moisan L and Morel J M 2001 Edge detection by the Helmholtz principle J. Math. Imaging Vis. 14 271-84
14. Elder H and Goldberg R M 1998 Inferential reliability of contour grouping cues in natural images Perception Suppl. 27 11
15. Faugeras O and Robert L 1996 What can two images tell us about the third one? Int. J. Comput. Vis. 18 5-19
16. Faugeras O D 1993 Three-Dimensional Computer Vision (Cambridge, MA: MIT Press)
17. Felsberg M and Sommer G 2000 A new extension of linear signal processing for estimating local properties and detecting features Proc. DAGM 2000 ed G Sommer, N Krüger and Ch Perwass (Berlin: Springer) pp 195-202
18. Felsberg M and Sommer G 2001 The monogenic signal IEEE Trans. Signal Process. 41 891-906
19. Field D 1987 Relations between the statistics of natural images and the response properties of cortical cells J. Opt. Soc. Am. 4 2379-94
20. Gazzaniga M S 1995 The Cognitive Neuroscience (Cambridge, MA: MIT Press)
21. Geisler W S, Perry J S, Super B J and Gallogly D P 2001 Edge co-occurrence in natural images predicts contour grouping performance Vis. Res. 41 711-24
22. Geman S, Bienenstock E and Doursat R 1995 Neural networks and the bias/variance dilemma Neural Comput. 4 1-58
23. Gödecke I and Bonhoeffer T 1996 Development of identical orientation maps for two eyes without common visual experience Nature 379 251-5
24. Granlund G H and Knutsson H 1995 Signal Processing for Computer Vision (Dordrecht: Kluwer)
25. Guy G and Medioni G 1996 Inferring global perceptual contours from local features Int. J. Comput. Vis. 20 113-33
26. Hahn M and Krüger N 2000 Junction detection and semantic interpretation using Hough lines EIS' 2000
27. Hamming R W 1980 Coding and Information Theory (Englewood Cliffs, NJ: Prentice-Hall)
28. Hancock P J B, Baddeley R J and Smith L S 1992 The principal components of natural images Network: Comput. Neural Syst. 3 61-72
29. Heitger F, von der Heydt R, Peterhans E, Rosenthaler L and Kübler O 1998 Simulation of neural contour mechanisms: representing anomalous contours Image Vis. Comput. 16 407-21
30. Hoffman D D (ed) 1980 Visual Intelligence: How We Create What We See (New York: Norton)
31. Horn B K P and Weldon E J 1988 Direct methods for recovering motion Int. J. Comput. Vis. 2 51-76
32. Hubel D H and Wiesel T N 1979 Brain mechanisms of vision Sci. Am. 241 130-44
33. Ikeuchi K and Horn B K P 1981 Numerical shape from shading and occluding boundaries Artif. Intell. 17 141-84
34. Jähne B 1997 Digital Image Processing - Concepts, Algorithms, and Scientific Applications (Berlin: Springer)
35. Jones J P and Palmer L A 1987 An evaluation of the two dimensional Gabor filter model of simple receptive fields in striate cortex J. Neurophysiol. 58 1223-58
36. Kalocsai P 1998 Contour completion algorithm quantitatively and qualitatively improves the performance of a recognition model (evidence against geon theory) Proc. IEEE Computer Society Workshop on Perceptual Organization in Computer Vision http://marathon.csee.usf.edu/∼sarkar/pocv_program.html
37. Kay J, Floreano D and Phillps W A 1998 Contextually guided unsupervised learning using local multivariate binary processors Neural Net. 11 117-40
38. Kellman P J and Arterberry M E (ed) 1998 The Cradle of Knowledge (Cambridge, MA: MIT Press)
39. Klette R, Schlüns K and Koschan A 1998 Computer Vision - Three-Dimensional Data from Images (Berlin: Springer)
40. Knudsen E I, du Lac S and Esterly S D 1987 Computational maps in the brain Ann. Rev. Neurosci. 10 41-65
41. Koch R 1994 Model-based 3D scene analysis from stereoscopic image sequences ISPRS J. Photogr. Remote Sensing 49 23-30
42. Kofka K (ed) 1935 Principles of Gestalt Psychology (New York: Harcourt and Brace)
43. Koschan A 1994 How to utilize colour information in dense stereo matching and in edge based stereo matching? Proc. 3rd Int. Conf. on Automation, Robotics and Computer Vision (ICARCV'94 Singapore. November 1994) pp 419-423
44. Kovesi P 1999 Image features from phase congruency Videre: J. Comput. Vis. Res. 1 1-26
45. Krüger N 1998 Collinearity and parallelism are statistically significant second order relations of complex cell responses Neural Process. Lett. 8 117-29
46. Krüger N 2001 Learning object representations using a priori constraints within orassyll Neural Comput. 13 389-410
47. Krüger N, Ackermann M, and Sommer G 2002 Accumulation of object representations utilizing interaction of robot action and perception Knowl. Based Syst. 13 111-18
48. Krüger N, Felsberg M, Gebken C and Pörksen M 2002 An explicit and compact coding of geometric and structural information applied to stereo processing Vision, Modeling and Visualization 2002 ed G Griener et al (Berlin: Springer)
49. Krüger N and Wörgötter F 2002 Different degrees of genetical prestructuring in the ontogenesis of visual abilities based on deterministic and statistical regularities Proc. Workshop on Growing Up Artifacts that Live, Basic Principles and Future Trends (SAB'02, Edinburgh, April 2002)
50. Ledgeway T and Hess R F 2002 Rules for combining the outputs of local motion detectors to define simple contours Vis. Res. 42 653-9
51. Lettvin J Y, Maturana H R, McCulloch W S and Pitts WH 1959 What the frog's eye tells the frog's brain Proc. Inst. Radio Eng. 47 1950-61
52. Mallot H A 2001 personal discussion
53. Mel B 1996 Seemore: a view-based approach to 3D object recognition using multiple visual cues Adv. Neural Information Process. Syst. 8 865-71
54. Mundy J L, Liu A, Pillow Nie, Zisserman A, Abdallah S, Utcke S, Nayar S and Rothwell C 1996 An experimental comparison of appearance and geometric model based recognition Object Representation in Computer Vision II (Lecture Notes in Computer Science 1144) ed J Ponce, A Zisserman and M Herbert (Berlin: Springer) pp 247-69
55. Nagel H-H 1987 On the estimation of optic flow: relations between different approaches and some new results Artif. Intell. 33 299-324
56. Olshausen B A and Field D 1996 Emergence of simple-cell receptive field properties by learning a sparse code for natural images Nature 381 607-9
57. Phillips W A and Singer W 1997 In search of common foundations for cortical processing Behav. Brain Sci. 20 657-82
58. Posch S 1997 Perzeptives Gruppieren und Bildanalyse Habilitationsschrift, Universität Bielefeld, Deutsche Universitäts Verlag
59. Prodöhl C, Würtz R and von der Malsburg C 2002 Learning the Gestalt rule collinearity from object motion Neural Comput. submitted
60. Rakic P 1995 Corticogenisis in human and nonhuman primates The Cognitive Neuroscience ed M S Gazzaniga (Cambridge, MA: MIT Press) pp 127-45
61. Rohr K 1992 Recognizing corners by fitting parametric models Int. J. Comput. Vis. 9 213-30
62. Sarkar S and Boyer K L 1994 Computing Perceptual Organization in Computer Vision (Singapore: World Scientific)
63. Schiele B and Crowley J L 1996 Probabilistic object recognition using multi-dimensional receptive field histograms Adv. Neural Information Process. Syst. 8 865-71
64. Schmid C and Zisserman A 1997 Automatic line matching across views Proc. IEEE Conf. on Computer Vision and Pattern Recognition pp 666-71
65. Schwartz O and Simoncelli E 2001 Natural signal statistics and sensory gain control Nat. Neurosci. 4 819-25
66. Sigman M, Cecchi G A, Gilbert C D and Magnasco M O 2001 On a common circle: natural scenes and Gestalt rules Proc. Natl Acad. Sci. USA 98 1935-49
67. Simoncelli E P and Ohlshausen B A 2001 Natural image statistics and neural representations Annu. Rev. Neurosci. 24 1193-216
68. Sur M, Garraghty P E and Roe A W 1988 Experimentally induced visual projections into auditory thalamus and cortex Science 242 1437-41
69. von der Malsburg C 1981 The correlation theory of brain function Internal report
70. Watt R J and Phillips W A 2000 The function of dynamic grouping in vision Trends Cognitive Sci. 4 447-154
71. Wertheimer M (ed) 1935 Laws of Organisation in Perceptual Forms (London: Harcourt Brace Jovanovich)
72. Wiesel T N and Hubel D H 1974 Ordered arrangement of orientation columns in monkeys lacking visual experience J. Comp. Neurol. 158 307-18
73. Willshaw D J (ed) 2001 Network: Comput. Neural Syst. 12 (Special issue)
74. Wiskott L, Fellous J M, Krüger N and von der Malsburg C 1997 Face recognition by elastic bunch graph matching IEEE Trans. Pattern Anal. Mach. Intell. 19 775-80
75. Wiskott L and Sejnowski T 2002 Slow feature analysis: unsupervised learning of invariances Neural Comput. 14 715-70
76. Wörgötter F, Cozzi A and Gerdes V 1999 A parallel noise robust algorithm to recover depth information from radial flow Neural Comput. 11 381-416
77. Wuescher D M and Boyer K L 1991 Robust contour decomposition using constant curvature criterion IEEE Trans. Pattern Anal. Mach. Intell. 13 41-51
78. Zetzsche C and Krieger G 2001 Nonlinear mechanisms and higher-order statistics in biological vision and electronic image processing: review and perspectives J. Electron. Imaging 10 56-99