TY - GEN
T1 - Mean-shift segmentation with wavelet-based bandwidth selection
AU - Singh, M. K.
AU - Ahuja, N.
N1 - Publisher Copyright:
© 2002 IEEE.
PY - 2002
Y1 - 2002
N2 - Recently, various non-linear techniques for segmentation have been proposed based on non-parametric density estimation. These approaches model image data as clusters of pixels in the combined range-domain space, using kernel based techniques to represent the underlying, multi-modal Probability Density Function (PDF). In Mean-shift based segmentation, pixel clusters or image segments are identified with unique modes of the multi-modal PDF by mapping each pixel to a mode using a convergent, iterative process. The advantages of such approaches include flexible modeling of the image and noise processes and consequent robustness in segmentation. An important issue is the automatic selection of scale parameters a problem far from satisfactorily addressed. In this paper, we propose a regression-based model which admits a realistic framework to choose scale parameters. Results on real images are presented.
AB - Recently, various non-linear techniques for segmentation have been proposed based on non-parametric density estimation. These approaches model image data as clusters of pixels in the combined range-domain space, using kernel based techniques to represent the underlying, multi-modal Probability Density Function (PDF). In Mean-shift based segmentation, pixel clusters or image segments are identified with unique modes of the multi-modal PDF by mapping each pixel to a mode using a convergent, iterative process. The advantages of such approaches include flexible modeling of the image and noise processes and consequent robustness in segmentation. An important issue is the automatic selection of scale parameters a problem far from satisfactorily addressed. In this paper, we propose a regression-based model which admits a realistic framework to choose scale parameters. Results on real images are presented.
KW - Bandwidth
UR - http://www.scopus.com/inward/record.url?scp=33745742480&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33745742480&partnerID=8YFLogxK
U2 - 10.1109/ACV.2002.1182154
DO - 10.1109/ACV.2002.1182154
M3 - Conference contribution
AN - SCOPUS:33745742480
T3 - Proceedings of IEEE Workshop on Applications of Computer Vision
SP - 43
EP - 47
BT - Proceedings - 6th IEEE Workshop on Applications of Computer Vision, WACV 2002
PB - IEEE Computer Society
T2 - 6th IEEE Workshop on Applications of Computer Vision, WACV 2002
Y2 - 3 December 2002 through 4 December 2002
ER -