Abstract
The paper describes a robust edge and contour extraction technique under two types of degradation: random noise and aliasing. The technique employs unambiguous probabilistic relaxation to distinguish features from noise and refine their spatial locations at sub-pixel accuracy. The most important component in the probabilistic relaxation is a compatibility function. The paper suggests a function with which the optimal orientation of edges can be derived analytically, thus allowing an efficient implementation of the relaxation process. A contour extraction algorithm is designed by combining the relaxation process and a perceptual organization technique. Results on both synthetic and natural images are given and show effectiveness of our approach against noise and aliasing.
Research partially supported by ONR Grant N00014-97-1-1163
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© 2001 Springer-Verlag Berlin Heidelberg
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Kubota, T., Huntsberger, T., Martin, J.T. (2001). Edge Based Probabilistic Relaxation for Sub-pixel Contour Extraction. In: Figueiredo, M., Zerubia, J., Jain, A.K. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2001. Lecture Notes in Computer Science, vol 2134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44745-8_22
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DOI: https://doi.org/10.1007/3-540-44745-8_22
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