We present a new method to extract scale-invariant features from an image by using a Cosine Modulated Gaussian (CM-Gaussian) filter. Its balanced scale-space atom with minimal spread in scale and space leads to an outstanding scale-invariant feature detection quality, albeit at reduced planar rotational invariance. Both sharp and distributed features like corners and blobs are reliably detected, irrespective of various image artifacts and camera parameter variations, except for planar rotation. The CM-Gaussian filters are approximated with the sum of exponentials as a single, fixed-length filter and equal approximation error over all scales, providing constant-time, low-cost image filtering implementations. The approximation error of the corresponding digital signal processing is below the noise threshold. It is scalable with the filter order, providing many quality-complexity trade-off working points. We validate the efficiency of the proposed feature detection algorithm on image registration applications over a wide range of testbench conditions.
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The authors would like to thank Rachid Deriche from INRIA, Prof. Lucas J. Van Vliet and Prof. Ian T. Young from TU/Delft for discussions and answering our emails regarding the approximation design methods for the filters. Author Bert Geelen was supported by IWT SBO-project 100021 “CHAMELEON”.
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Mainali, P., Lafruit, G., Yang, Q. et al. SIFER: Scale-Invariant Feature Detector with Error Resilience. Int J Comput Vis 104, 172–197 (2013). https://doi.org/10.1007/s11263-013-0622-3