PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Geometrical-based algorithm for variational segmentation and smoothing of vector-valued images
Sasan Mahmoodi and Bayan Sharif
IET image processing Volume 1, Number 2, pp. 112-122, 2006.


An optimisation method based on a nonlinear functional is considered for segmentation and smoothing of vector-valued images. An edge-based approach is proposed to initially segment the image using geometrical properties such as metric tensor of the linearly smoothed image. The nonlinear functional is then minimised for each segmented region to yield the smoothed image. The functional is characterised with a unique solution in contrast with the Mumford–Shah functional for vector-valued images. An operator for edge detection is introduced as a result of this unique solution. This operator is analytically calculated and its detection performance and localisation are then compared with those of the DroGoperator. The implementations are applied on colour images as examples of vector-valued images, and the results demonstrate robust performance in noisy environments.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Theory & Algorithms
ID Code:4240
Deposited By:Sasan Mahmoodi
Deposited On:20 December 2008