Abstract
Computer vision tasks such as learning, recognition, classification or segmentation applied to spatial data often requires spatial normalization of repeated features and structures. Spatial normalization, or in other words, image registration, is still a big hurdle for the image processing community. Its formulation often relies on the fact that correspondence is achieved when a similarity measure is maximized. This paper presents a novel similarity measuring technique based on a coupling function inside a template matching framework. It allows using any entropy-based similarity metric, which is crucial for registration using different acquisition devices. Results are presented using this technique on a multiresolution incremental scheme.
This work was supported by the spanish Ministry of Science and Technology and European Commission, co-funded grant TIC-2001-38008-C02-01, NIH grant P41-RR13218 and CIMIT, and spanish FPU grant AP98-52835909.
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Suárez, E., Santana, J.A., Rovaris, E., Westin, CF., Ruiz-Alzola, J. (2003). Fast Entropy-Based Nonrigid Registration. In: Moreno-Díaz, R., Pichler, F. (eds) Computer Aided Systems Theory - EUROCAST 2003. EUROCAST 2003. Lecture Notes in Computer Science, vol 2809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45210-2_55
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DOI: https://doi.org/10.1007/978-3-540-45210-2_55
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