Abstract
Foveated sampling and representation of images is a powerful tool for various vision applications. However, there are many inherent difficulties in implementing it. We present a simple and efficient mechanism to manipulate image analysis operators directly on the foveated image; A single typed table-based structure is used to represent various known operators. Using the Complex Log as our foveation method, we show how several operators such as edge detection and Hough transform could be efficiently computed almost at frame rate, and discuss the complexity of our approach.
This work was supported by the Minerva Minkowski center for Geometry, and by a grant from the Israel Academy of Science for geometric Computing.
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© 2000 Springer-Verlag Berlin Heidelberg
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Nattel, E., Yeshurun, Y. (2000). An Efficient Data Structure for Feature Extraction in a Foveated Environment. In: Lee, SW., Bülthoff, H.H., Poggio, T. (eds) Biologically Motivated Computer Vision. BMCV 2000. Lecture Notes in Computer Science, vol 1811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45482-9_21
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DOI: https://doi.org/10.1007/3-540-45482-9_21
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