Abstract
This work presents scale invariant region detectors that apply evolved operators to extract an interest measure. We evaluate operators using their repeatability rate, and have experimentally identified a plateau of local optima within a space of possible interest operators Ω. The space Ω contains operators constructed with Gaussian derivatives and standard arithmetic operations. From this set of local extrema, we have chosen two operators, obtained by searching within Ω using Genetic Programming, that are optimized for high repeatability and global separability when imaging conditions are modified by a known transformation. Then, by embedding the operators into the linear scale space generated with a Gaussian kernel we can characterize scale invariant features by detecting extrema within the scale space response of each operator. Our scale invariant region detectors exhibit a high performance when compared with state-of-the-art techniques on standard tests.
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Trujillo, L., Olague, G. (2007). Scale Invariance for Evolved Interest Operators. In: Giacobini, M. (eds) Applications of Evolutionary Computing. EvoWorkshops 2007. Lecture Notes in Computer Science, vol 4448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71805-5_47
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DOI: https://doi.org/10.1007/978-3-540-71805-5_47
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