Title :
Key-point detection with multi-layer center-surround inhibition
Author :
Foti Coleca;Sabrina Zîrnovean;Thomas Käster;Thomas Martinetz;Erhardt Barth
Author_Institution :
Institute for Neuro- and Bioinformatics, University of Lü
Abstract :
We present a biologically inspired algorithm for key-point detection based on multi-layer and nonlinear center-surround inhibition. A Bag-of-Visual-Words framework is used to evaluate the performance of the detector on the Oxford III-T Pet Dataset for pet recognition. The results demonstrate an increased performance of our algorithm compared to the SIFT key-point detector. We further improve the recognition rate by separately training codebooks for the ON- and OFF-type key points. The results show that our key-point detection algorithms outperform the SIFT detector by having a lower recognition-error rate over a whole range of different key-point densities. Randomly selected key-points are also outperformed.
Keywords :
"Detectors","Feature extraction","Positron emission tomography","Training","Image edge detection","Biological information theory","Ions"
Conference_Titel :
Computer Vision Theory and Applications (VISAPP), 2014 International Conference on