Abstract
An important function of perceptual grouping is the restoration of contours. Edge maps produced by low level edge detectors are invariably noisy and inconsistent. It it the aim of perceptual grouping to refine these edge segments by imposing consistency based on considerations about real object outlines. In this paper we describe a method for grouping edge segments into perceptually salient contours using splines. The two important ingredients of our method are firstly the use of probability distributions for possible orientation structure in the image, and secondly the use of Kellman-Shipley relatability to find perceptually meaningful structure. The spline parameters are adjusted to optimise their probabilities in terms of image structure and bending. Consistent structure is then identified using both perceptual criteria and similarity to contour structure in the image.
Chapter PDF
References
Sha’ashua, A., Ullman, S.: Structural saliency: The detection of globally salient structures using a locally connected network. In: Proceedings of the 2nd ICCV, pp. 321–327 (1988); Also Weizmann Institute of Science Report CS88-18 (October 1988)
Sarkar, S., Boyer, K.: Computing Perceptual Organisation in Computer Vision. World Scientific, Singapore (1994)
Elder, J., Zucker, S.: Computing contour closure. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, p. 399. Springer, Heidelberg (1996)
Guy, G., Medioni, G.: Inferring global perceptual contours from local features. Int. J. Comp. Vis. 20, 113–133 (1996)
Kellman, P., Shipley, T.: A theory of visual interpolation in object perception. Cognitive Psychology 23, 141–221 (1991)
Lüdtke, N., Wilson, R., Hancock, E.: Population codes for orientation estimation. In: Proceedings of the 15th International Conference on Pattern Recognition (2000)
Lüdtke, N., Wilson, R., Hancock, E.: Probabilistic population coding of multiple edge orientation. In: Proceedings of the International Conference on Image Processing, IEEE, Los Alamitos (2002) (to appear)
Parent, P., Zucker, S.: Trace inference, curvature consistency, and curve detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 823–839 (1989)
Hancock, E., Kittler, J.: Discrete relaxation. Pattern Recognition 23, 711–733 (1990)
Gavrila, D.: Hermite deformable contours. Technical Report CS-TR-3610, Center for Automation Research, University of Maryland (1996); Also ICPR 1996
Iverson, L., Zucker, S.: Logical/linear operators for image curves. IEEE Transactions on Pattern Recognition and Machine Intelligence 17, 982–996 (1995)
Koendrink, J., van Doorn, A.: The shape of smooth objects and the way contours end. Perception 11, 129–137 (1982)
Canny, J.: A computational approach to edge detection. IEEE Transact. on Patt. Rec. and Machine Intell. 8, 679–700 (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ludtke, N., Wilson, R.C. (2004). Contour Segments from Spline Interpolation. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2004. Lecture Notes in Computer Science, vol 3138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27868-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-27868-9_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22570-6
Online ISBN: 978-3-540-27868-9
eBook Packages: Springer Book Archive