Open Access

A Biologically Motivated Multiresolution Approach to Contour Detection

  • Giuseppe Papari1Email author,
  • Patrizio Campisi2,
  • Nicolai Petkov1 and
  • Alessandro Neri2
EURASIP Journal on Advances in Signal Processing20072007:071828

https://doi.org/10.1155/2007/71828

Received: 3 January 2006

Accepted: 3 November 2006

Published: 22 January 2007

Abstract

Standard edge detectors react to all local luminance changes, irrespective of whether they are due to the contours of the objects represented in a scene or due to natural textures like grass, foliage, water, and so forth. Moreover, edges due to texture are often stronger than edges due to object contours. This implies that further processing is needed to discriminate object contours from texture edges. In this paper, we propose a biologically motivated multiresolution contour detection method using Bayesian denoising and a surround inhibition technique. Specifically, the proposed approach deploys computation of the gradient at different resolutions, followed by Bayesian denoising of the edge image. Then, a biologically motivated surround inhibition step is applied in order to suppress edges that are due to texture. We propose an improvement of the surround suppression used in previous works. Finally, a contour-oriented binarization algorithm is used, relying on the observation that object contours lead to long connected components rather than to short rods obtained from textures. Experimental results show that our contour detection method outperforms standard edge detectors as well as other methods that deploy inhibition.

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Authors’ Affiliations

(1)
Institute of Mathematics and Computing Science, University of Groningen
(2)
Dipartimento di Elettronica Applicata, Università degli Studi di Roma "Roma Tre"

References

  1. Canny J: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1986,8(6):679-698.View ArticleGoogle Scholar
  2. Frei W, Chen C-C: Fast boundary detection: a generalization and a new algorithm. IEEE Transactions on Computers 1977,26(10):988-998.View ArticleGoogle Scholar
  3. Hildreth EC: The detection of intensity changes by computer and biological vision systems. Computer Vision, Graphics, and Image Processing 1983,22(1):1-27. 10.1016/0734-189X(83)90093-2View ArticleGoogle Scholar
  4. Martens J-B: Local orientation analysis in images by means of the Hermite transform. IEEE Transactions on Image Processing 1997,6(8):1103-1116. 10.1109/83.605408View ArticleGoogle Scholar
  5. Nevatia R, Babu KR: Linear feature extraction and description. Computer Vision, Graphics, and Image Processing 1980,13(3):257-269. 10.1016/0146-664X(80)90049-0View ArticleGoogle Scholar
  6. Gregson PH: Using angular dispersion of gradient direction for detecting edge ribbons. IEEE Transactions on Pattern Analysis and Machine Intelligence 1993,15(7):682-696. 10.1109/34.221169View ArticleGoogle Scholar
  7. Zuniga OA, Haralick RM: Integrated directional derivative gradient operator. IEEE Transactions on Systems, Man and Cybernetics 1987,17(3):508-517.View ArticleGoogle Scholar
  8. Chen G, Yang YHH: Edge detection by regularized cubic B-spline fitting. IEEE Transactions on Systems, Man and Cybernetics 1995,25(4):636-643. 10.1109/21.370194View ArticleGoogle Scholar
  9. Ghosal S, Mehrotra R: Detection of composite edges. IEEE Transactions on Image Processing 1994,3(1):14-25. 10.1109/83.265977View ArticleGoogle Scholar
  10. Haralick RM: Digital step edges from zero crossing of second directional derivatives. IEEE Transactions on Pattern Analysis and Machine Intelligence 1984,6(1):58-68.View ArticleGoogle Scholar
  11. Nalwa VS, Binford TO: On detecting edges. IEEE Transactions on Pattern Analysis and Machine Intelligence 1986,8(6):699-714.View ArticleGoogle Scholar
  12. Morrone MC, Burr DC: Feature detection in human vision: a phase-dependent energy model. Proceedings of the Royal Society of London. Series B, Biological sciences 1988,235(1280):221-245. 10.1098/rspb.1988.0073View ArticleGoogle Scholar
  13. Folsom TC, Pinter RB: Primitive features by steering, quadrature, and scale. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998,20(11):1161-1173. 10.1109/34.730552View ArticleGoogle Scholar
  14. Heitger F: Feature detection using suppression and enhancement. In Tech. Rep. TR-163. Communication Technology Laboratory, Swiss Federal Institute of Technology, Zurich, Switzerland; 1995.Google Scholar
  15. Kovesi P: Image features from phase congruency. Videre: Journal on Computer Vision Research 1999,1(3):2-27.Google Scholar
  16. Morrone MC, Owens RA: Feature detection from local energy. Pattern Recognition Letters 1987,6(5):303-313. 10.1016/0167-8655(87)90013-4View ArticleGoogle Scholar
  17. Zhou YT, Venkateswar V, Chellappa R: Edge detection and linear feature extraction using a 2-D random field model. IEEE Transactions on Pattern Analysis and Machine Intelligence 1989,11(1):84-95. 10.1109/34.23115View ArticleGoogle Scholar
  18. Ando S: Image field categorization and edge/corner detection from gradient covariance. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000,22(2):179-190. 10.1109/34.825756View ArticleGoogle Scholar
  19. Meer P, Georgescu B: Edge detection with embedded confidence. IEEE Transactions on Pattern Analysis and Machine Intelligence 2001,23(12):1351-1365. 10.1109/34.977560View ArticleGoogle Scholar
  20. Black MJ, Sapiro G, Marimont DH, Heeger D: Robust anisotropic diffusion. IEEE Transactions on Image Processing 1998,7(3):421-432. 10.1109/83.661192View ArticleGoogle Scholar
  21. Chen Y, Barcelos CAZ, Mair BA: Smoothing and edge detection by time-varying coupled nonlinear diffusion equations. Computer Vision and Image Understanding 2001,82(2):85-100. 10.1006/cviu.2001.0903View ArticleMATHGoogle Scholar
  22. Perona P, Malik J: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990,12(7):629-639. 10.1109/34.56205View ArticleGoogle Scholar
  23. Weickert J: A review of nonlinear diffusion filtering. In Scale-Space Theory in Computer Vision, Lecture Notes in Computer Science. Volume 1252. Springer, New York, NY, USA; 1997:3-28.Google Scholar
  24. Ma W-Y, Manjunath BS: EdgeFlow: a technique for boundary detection and image segmentation. IEEE Transactions on Image Processing 2000,9(8):1375-1388. 10.1109/83.855433MathSciNetView ArticleMATHGoogle Scholar
  25. Malik J, Belongie S, Leung T, Shi J: Contour and texture analysis for image segmentation. International Journal of Computer Vision 2001,43(1):7-27. 10.1023/A:1011174803800View ArticleMATHGoogle Scholar
  26. Manjunath BS, Chellappa RS: A unified approach to boundary perception: edges, textures, and illusory contours. IEEE Transactions on Neural Networks 1993,4(1):96-108. 10.1109/72.182699View ArticleGoogle Scholar
  27. Grigorescu C, Petkov N, Westenberg MA: Contour detection based on nonclassical receptive field inhibition. IEEE Transactions on Image Processing 2003,12(7):729-739. 10.1109/TIP.2003.814250View ArticleGoogle Scholar
  28. Li Z: Visual segmentation by contextual influences via intra-cortical interactions in the primary visual cortex. Network: Computation in Neural Systems 1999,10(2):187-212. 10.1088/0954-898X/10/2/305View ArticleMATHGoogle Scholar
  29. Petkov N, Kruizinga P: Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells. Biological Cybernetics 1997,76(2):83-96. 10.1007/s004220050323View ArticleMATHGoogle Scholar
  30. Petkov N, Westenberg MA: Suppression of contour perception by band-limited noise and its relation to nonclassical receptive field inhibition. Biological Cybernetics 2003,88(3):236-246. 10.1007/s00422-002-0378-2View ArticleMATHGoogle Scholar
  31. Marr D, Hildreth EC: Theory of edge detection. Proceedings of the Royal Society of London. Series B, Biological sciences 1980,207(1167):187-217. 10.1098/rspb.1980.0020View ArticleGoogle Scholar
  32. Field DJ, Hayes A, Hess RF: Contour integration by the human visual system: evidence for a local "association field". Vision Research 1993,33(2):173-193. 10.1016/0042-6989(93)90156-QView ArticleGoogle Scholar
  33. Kanizsa G: Organization in Vision: Essays on Gestalt Perception. Praeger, New York, NY, USA; 1979.Google Scholar
  34. Nothdurft HC: Texture segmentation and pop-out from orientation contrast. Vision Research 1991,31(6):1073-1078. 10.1016/0042-6989(91)90211-MView ArticleGoogle Scholar
  35. Solomon JA, Pelli DG: The visual filter mediating letter identification. Nature 1994,369(6479):395-397. 10.1038/369395a0View ArticleGoogle Scholar
  36. Kapadia MK, Westheimer G, Gilbert CD: Spatial distribution of contextual interactions in primary visual cortex and in visual perception. Journal of Neurophysiology 2000,84(4):2048-2062.Google Scholar
  37. Knierim JJ, van Essen DC: Neuronal responses to static texture patterns in area V1 of the alert macaque monkey. Journal of Neurophysiology 1992,67(4):961-980.Google Scholar
  38. Nothdurft HC, Gallant JL, van Essen DC: Response modulation by texture surround in primate area V1: correlates of "popout" under anesthesia. Visual Neuroscience 1999,16(1):15-34.View ArticleGoogle Scholar
  39. Jones HE, Grieve KL, Wang W, Sillito AM: Surround suppression in primate V1. Journal of Neurophysiology 2001,86(10):2011-2028.Google Scholar
  40. Portilla J, Strela V, Wainwright MJ, Simoncelli EP: Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Transactions on Image Processing 2003,12(11):1338-1351. 10.1109/TIP.2003.818640MathSciNetView ArticleMATHGoogle Scholar
  41. Julesz B: Visual pattern discrimination. IRE Transactions on Information Theory 1962,8(2):84-92. 10.1109/TIT.1962.1057698View ArticleGoogle Scholar
  42. Campbell FW, Robson JG: Application of Fourier analysis to the visibility of gratings. Journal of Physiology 1968,197(3):551-566.View ArticleGoogle Scholar
  43. Mallat SG: Multifrequency channel decompositions of images and wavelet models. IEEE Transactions on Acoustics, Speech, and Signal Processing 1989,37(12):2091-2110. 10.1109/29.45554View ArticleGoogle Scholar
  44. Morrone MC, Burr DC: Capture and transparency in coarse quantized images. Vision Research 1997,37(18):2609-2629. 10.1016/S0042-6989(97)00052-7View ArticleGoogle Scholar
  45. Richards W, Nishihara HK, Dawson B: CARTOON: a biologically motivated edge detection algorithm. In Natural Computation, MIT A.I. Memo no. 668. Edited by: Richards W. MIT Press, Cambridge, Mass, USA; 1988:55-69. chapter 4Google Scholar
  46. Papari G, Campisi P, Petkov N, Neri A: A multiscale approach to conour detection by texture suppression. Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, January 2006, San Jose, Calif, USA, Proceedings of the SPIE 6064: 107-118.Google Scholar
  47. Wainwright MJ, Simoncelli EP, Willsky AS: Random cascades on wavelet trees and their use in analyzing and modeling natural images. Applied and Computational Harmonic Analysis 2001,11(1):89-123. special issue on wavelet applications 10.1006/acha.2000.0350MathSciNetView ArticleMATHGoogle Scholar
  48. Simoncelli EP: Statistical modeling of photographic images. In Handbook of Image and Video Processing. 2nd edition. Edited by: Bovik A. Academic Press, Boston, Mass, USA; 2005:431-441. chapter 4.7View ArticleGoogle Scholar
  49. Crouse MS, Nowak RD, Baraniuk RG: Wavelet-based statistical signal processing using hidden Markov models. IEEE Transactions on Signal Processing 1998,46(4):886-902. 10.1109/78.668544MathSciNetView ArticleGoogle Scholar
  50. Mihçak MK, Kozintsev I, Ramchandran K, Moulin P: Low-complexity image denoising based on statistical modeling of wavelet coefficients. IEEE Signal Processing Letters 1999,6(12):300-303. 10.1109/97.803428View ArticleGoogle Scholar
  51. Portilla J, Strela V, Wainwright MJ, Simoncelli EP: Adaptive Wiener denoising using a Gaussian scale mixture model in the wavelet domain. Proceedings of the 8th IEEE International Conference on Image Processing (ICIP '01), October 2001, Thessaloniki, Greece 2: 37-40.Google Scholar
  52. Portilla J, Simoncelli EP: A parametric texture model based on joint statistics of complex wavelet coefficients. International Journal of Computer Vision 2000,40(1):49-71. 10.1023/A:1026553619983View ArticleMATHGoogle Scholar
  53. Jacovitti J, Neri A: Anisotropic wavelet thresholding for Bayesian image denoising. Proceedings of the 11th European Signal Processing Conference (EUSIPCO '02), September 2002, Toulouse, France 3: 267-270.Google Scholar
  54. Grigorescu C, Petkov N, Westenberg MA: Contour and boundary detection improved by surround suppression of texture edges. Image and Vision Computing 2004,22(8):609-622. 10.1016/j.imavis.2003.12.004View ArticleGoogle Scholar
  55. Xiao D-K, Raiguel S, Marcar V, Koenderink J, Orban GA: Spatial heterogeneity of inhibitory surrounds in the middle temporal visual area. Proceedings of the National Academy of Sciences of the United States of America 1995,92(24):11303-11306. 10.1073/pnas.92.24.11303View ArticleGoogle Scholar
  56. Heimans HJAM: Morphological Image Operators. Academic Press, Boston, Mass, USA; 1994.Google Scholar
  57. Heijmans HJAM: Connected morphological operators for binary images. Computer Vision and Image Understanding 1999,73(1):99-120. 10.1006/cviu.1998.0703MathSciNetView ArticleMATHGoogle Scholar
  58. Lindeberg T: Edge detection and ridge detection with automatic scale selection. International Journal of Computer Vision 1998,30(2):117-154. 10.1023/A:1008097225773View ArticleGoogle Scholar
  59. Liang K-H, Tjahjadi T, Yang Y-H: Bounded diffusion for multiscale edge detection using regularized cubic B-spline fitting. IEEE Transactions on Systems, Man, and Cybernetics 1999,29(2):291-297. 10.1109/3477.752803View ArticleGoogle Scholar
  60. Ding A, Goshtasby A: On the canny edge detector. Pattern Recognition 2001,34(3):721-725. 10.1016/S0031-3203(00)00023-6View ArticleMATHGoogle Scholar
  61. Olson CF: Adaptive-scale filtering and feature detection using range data. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000,22(9):983-991. 10.1109/34.877521View ArticleGoogle Scholar
  62. Martin DR, Fowlkes CC, Malik J: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004,26(5):530-549. 10.1109/TPAMI.2004.1273918View ArticleGoogle Scholar
  63. Bowyer K, Kranenburg C, Dougherty S: Edge detector evaluation using empirical ROC curves. Computer Vision and Image Understanding 2001,84(1):77-103. 10.1006/cviu.2001.0931View ArticleMATHGoogle Scholar
  64. Kitchen L, Rosenfeld A: Edge evaluation using local edge coherence. IEEE Transactions on Systems, Man and Cybernetics 1981,11(9):597-605.View ArticleGoogle Scholar
  65. Shin M, Goldgof D, Bowyer KW: An objective comparison methodology of edge detection algorithms for structure from motion task. In Empirical Evaluation Techniques in Computer Vision. IEEE Press, New York, NY, USA; 1998:235-254.Google Scholar
  66. Shin MC, Goldgof DB, Bowyer KW: Comparison of edge detector performance through use in an object recognition task. Computer Vision and Image Understanding 2001,84(1):160-178. 10.1006/cviu.2001.0932View ArticleMATHGoogle Scholar
  67. Petkov N, Westenberg MA: Suppression of contour perception by band-limited noise and its relation to nonclassical receptive field inhibition. Biological Cybernetics 2003,88(3):236-246. 10.1007/s00422-002-0378-2View ArticleMATHGoogle Scholar
  68. Grigorescu C, Petkov N: Distance sets for shape filters and shape recognition. IEEE Transactions on Image Processing 2003,12(10):1274-1286. 10.1109/TIP.2003.816010MathSciNetView ArticleMATHGoogle Scholar
  69. Ghosh A, Petkov N: Robustness of shape descriptors to incomplete contour representations. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005,27(11):1793-1804.View ArticleGoogle Scholar
  70. Ghosh A, Petkov N: A cognitive evaluation procedure for contour based shape descriptors. International Journal of Hybrid Intelligent Systems 2005,2(4):237-252.MATHGoogle Scholar
  71. Ghosh A, Petkov N: Effect of high curvature point deletion on the performance of two contour based shape recognition algorithms. International Journal of Pattern Recognition and Artificial Intelligence 2006,20(6):913-924. 10.1142/S0218001406005046View ArticleGoogle Scholar
  72. Bergholm F: Edge focusing. IEEE Transactions on Pattern Analysis and Machine Intelligence 1987,9(6):726-741.View ArticleGoogle Scholar
  73. Goshtasby A: On edge focusing. Image and Vision Computing 1994,12(4):247-256. 10.1016/0262-8856(94)90078-7View ArticleGoogle Scholar

Copyright

© Giuseppe Papari et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.