Extrapolative spatial models for detecting perceptual boundaries in colour images

International Journal of Computer Vision

Volumn 73, Issue 2, 2007, Pages 179-194

  Ng, Jeffrey ^a   Bharath, Anil A ^a   Kin, Patrick Chow Pak ^b  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b SINGAPORE POLYTECHNIC   (Singapore)

Author keywords

Boundary detection; Edge detection; Extrapolative spatial model; Phase invariance; Steerable filter

Indexed keywords

ALGORITHMS; BOUNDARY CONDITIONS; DATABASE SYSTEMS; EDGE DETECTION; MATHEMATICAL MODELS; SIGNAL FILTERING AND PREDICTION;

BOUNDARY DETECTION; EXTRAPOLATIVE SPATIAL MODELS; PHASE INVARIANCE; STEERABLE FILTERS;

COLOR IMAGE PROCESSING;

EID: 33846610910     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-006-9782-8     Document Type: Article

References (29)

1. Bharath, A. and Ng, J. 2005. A steerable complex wavelet construction and its application to image denoising. IEEE Transactions on Image Processing, 14(7):948-959.
2. Bouganis, C.-S., Cheung, P.K., Ng, J., and Bharath, A. 2004. A steerable complex wavelet construction and its implementation on FPGA. In International Conference on Field Programmable Logic and Application, volume 3203 of Lecture Notes in Computer Science, pp. 394-403.
3. Canny, J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6): 679-698.
4. Choi, S.-B., Ban, S.-W., and Lee, M. 2004. Biologically motivated visual attention system using bottom-up saliency map and top-down inhibition. Neural Information Processing-Letters and Reviews, 2(1):19-25.
5. Cios, K. and Sarieh, A. 1990. An edge extraction technique for noisy images. IEEE Transactions on Biomedical Engineering, 37(5): 520-524.
6. Freeman, W. and Adelson, E. 1991. The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(9):891-906.
7. Granlund, G. and Knutsson, H. 1994. Signal Processing for Computer Vision. Kluwer Academic Publishers.
8. Knutsson, H. and Granlund, G. 1983. Texture analysis using two-dimensional quadrature filters. In IEEE Computer Society Workshop on Computer Architecture, Pattern Analysis and Image Database Management, pp. 206-213.
9. Knutsson, H. and Westin, C.-F. 1993. Normalized and differential convolution: Methods for interpolation and filtering of incomplete and uncertain data. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 515-523.
10. Kovesi, P. 2002. Edges are not just steps. In Proceedings of the Asian Conference on Computer Vision, pp. 822-827.
11. Kubota, T., Huntsberger, T., and Martin, J. 2001. Edge based probabilistic relaxation for sub-pixel contour extraction. In International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, volume 2134 of Lecture Notes in Computer Science, pp. 328-343.
12. Lai, G. and Figueiredo, R. 2000. A novel algorithm for edge detection from direction-derived statistics. In Proceedings of IEEE International Symposium on Circuits and Systems, vol. 5, pp. 37-40.
13. Littenberg, B. and Moses, L. 1993. Estimating diagnostic accuracy from multiple conflicting reports: A new meta-analytic method. Medical Decision Making, 13:313-321.
14. Luthon, F., Lievin, M., and Faux, F. 2004. On the use of entropy power for threshold selection. Signal Processing, 84(10):1789-1804.
15. Martin, D., Fowlkes, C., Tal, D., and Malik, J. 2001. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings of 8th International Conference on Computer Vision, Vancouver, Canada, vol. 2, pp. 416-425.
16. Medina-Carnicer, R., Madrid-Cievas, F., Fernandez-Garcia, N., and Carmona-Poyato, A. 2005. Evaluation of global thresholding techniques in non-contextual edge detection. Pattern Recognition Letters, 26(10):1423-1434.
17. Morrone, M. and Burr, D. 1988. Feature detection in human vision: A phase-dependent energy model. Proceedings of the Royal Society of London, B, 235:221-245.
18. Ng, J. and Bharath, A. 2004. Steering in scale space to optimally detect image structures. In European Conference on Computer Vision, volume 3021 of Lecture Notes in Computer Science, pp. 482-494.
19. Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., and Poggio, T. 1997. Pedestrian detection using wavelet templates. In Proceedings of Computer Vision and Pattern Recognition, pp. 193-199.
20. Otsu, N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, SMC-9(1):62-66.
21. Papoulis, A. 1984. Probability, Random Variables and Stochastic Processes. New York: McGraw-Hill.
22. Pearson, K. 1894. Contributions to the mathematical theory of evolution. Philosophical Transactions of the Royal Society, 185:71-110.
23. Pellegrino, F., Vanzella, W., and Torre, V. 2004. Edge detection revisited. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 34(3): 1500-1518.
24. Romberg, J., Choi, H., and Baraniuk, R. 2001. Bayesian tree-structured image modeling using wavelet domain hidden Markov models. IEEE Transactions on Image Processing, 10(7):1056-1068.
25. Rosin, P. 2001. Unimodal thresholding. Pattern Recognition, 34(11): 2083-2096.
26. Scarabottolo, N., Sorrenti, D., and Spertini, M. 1993. Edge detection on massively parallel machines: A local threshold approach. In Euromicro Workshop on Parallel and Distributed Processing, pp. 14-21.
27. Scharcanski, J. and Venetsanopoulos, A. 1997. Edge detection of color images using directional operators. IEEE Transactions on Circuits and Systems for Video Technology, 7(2):397-401.
28. Scharr, H., Black, M., and Haussecker, H. 2003. Image statistics and anisotropic diffusion. In Proceedings of the International Conference on Computer Vision, vol. 2, pp. 840-847.
29. Zhang, A.-H., Yu, S.-S., and Zhou, J.-L. 2003. A local-threshold segment algorithm based on edge-detection. Mini-Micro Systems, 24(4):661-663.