The idea proposed in this paper is to use a log polar sampling scheme followed by a FFT in order to obtain a rotation and to some degree scale invariant feature descriptor.
The feature vector will be rather short since just the most discriminative part of the FFT is used.
- A short feature vector for image matching: The Log-Polar Magnitude feature descriptor. DJ. Matuszewski, A. Hast, C. Wählby C, IM. Sintorn. PLOS ONE 12(11): e0188496, Journal. pp. 1-12. 2017.
@article{Mat17a, author = {DJ. Matuszewski and A. Hast and C. Wählby and C. Wählby C and IM. Sintorn}, title = {A short feature vector for image matching: The Log-Polar Magnitude feature descriptor}, journal = {PLOS ONE 12(11): e0188496}, pages = {1--12}, year = {2017} }
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Anders Hast received a PhD in Computerised Image processing at Uppsala university in 2004. In 2011 he spent one year at IIT, CNR, Pisa in Italy as an ERCIM fellow and after that he received a full time position as associate professor and in 2019 he became professor in image processing, both at Uppsala University. He has been affiliated with UPPMAX super computer centre where he worked as an application expert in Scientific Visualisation and later in the field of digital humanities together with the Centre for digital humanities in Uppsala. His recent research has focused on image processing and computer vision, for applications in microscopy, aerial photography, object recognition and especially hand written text recognition and face recognition.
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Anders Hast 