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Interest Point detector based on Maximal Self-Dissimilarities
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Maximal Self-Dissimilarity Interest Point Detector Computer Vision Laboratory, University of Bologna www.vision.deis.unibo.it/fede This is an implementation of the work presented in the paper [1] F. Tombari, L. Di Stefano “Interest Points via Maximal Self-Dissimilarities” 12th Asian Conference on Computer Vision (ACCV), 2014 We kindly request to cite and adequately refer to this work any time this code is being used. In the current distribution you can find -an implementation of the MSD descriptor -a test that demonstrates how to use it on a test image The current implementation of MSD extracts interest point out of grayscale images at different scales. The input can be either a grayscale of a color image. The detector returns a std::vector of cv::KeyPoint, an OpenCV structure which associates each interest point to a 2D coordinate, a scale, a saliency value and (optionally) a canonical orientation. The MSD algorithm can be used with the set of default parameters (those used in [1]), as well as with a set of user-defined parameters. The MSD code is released under the GNU GPLv3 license. A version of this code under a different licensing agreement, intended for commercial use, is also available. Please contact me if interested. DEPENDENCIES and BUILD: The only required dependency is OpenCV (opencv.org, v3.0 or above). Boost (v1.49 or above, www.boost.org) is not required, but used for multi-thread optimization. It is expected that the running time of MSD can be sped up notably on a multi-core architecture by using the provided Boost multi-thread optimization. We provide a CMakeFile, so that the code can be build over different compilers and platforms by making use of CMake (www.cmake.org). CONTACTS: Federico Tombari <[email protected]>
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