DocumentCode :
3063661
Title :
The role of local scale and orientation in feature location using neural nets
Author :
Lisboa, P.J.G. ; Mallaiah, M.
Author_Institution :
Dept. of Electr. Eng. & Electron., Liverpool Univ., UK
fYear :
1992
fDate :
30 Aug-3 Sep 1992
Firstpage :
672
Lastpage :
675
Abstract :
The application of one pass orientation and scale selective filters to feature location is investigated. The particular case study developed deals with the location of eyes in head-and-shoulders images using artificial neural networks trained by back-error-propagation. Three types of filters were studied. Conventional Marr edge detectors, Gabor filters selective to the horizontal and vertical directions, and also edge detectors which extract high resolution information along each of the two directions using 2D separable wavelet filters. Tests were conducted to locate the centre of the pupil in the right eye in sixty head-and-shoulders images. The results are compared for the different filters, and also using the raw pixel image directly
Keywords :
backpropagation; edge detection; feature extraction; filtering and prediction theory; neural nets; 2D separable wavelet filters; Gabor filters; Marr edge detectors; back-error-propagation; feature extraction; feature location; head-and-shoulders images; local scale; neural nets; one pass orientation; scale selective filters; Artificial neural networks; Data mining; Detectors; Eyes; Gabor filters; Image edge detection; Information filtering; Information filters; Pixel; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 1992. Vol.III. Conference C: Image, Speech and Signal Analysis, Proceedings., 11th IAPR International Conference on
Conference_Location :
The Hague
Print_ISBN :
0-8186-2920-7
Type :
conf
DOI :
10.1109/ICPR.1992.202076
Filename :
202076
Link To Document :
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