Abstract
a multichannel texture segmentation algorithm is presented based on the image pyramids produced with the Bamberger directional filter bank. An extensive evaluation of Bamberger pyramids and their design parameters is presented. The impact on segmentation performance of factors like the number of pyramid levels, number of directional channels, redundancy and filter specifications is considered. The proposed system is shown to provide some of the best results reported to date when compared with other multichannel representations under similar evaluation conditions. It is further shown that segmentation results using the maximally decimated directional filter bank rival those of the undecimated case. To the knowledge of the authors, such performance has not been previously observed for decompositions with decimated channels.
Highlights
Image segmentation has received considerable attention over the last few decades
Our results indicate that the superior directional selectivity found in Bamberger pyramids is directly related to improved segmentation performance
In this paper we presented the use of Bamberger pyramids for multichannel texture segmentation
Summary
Image segmentation has received considerable attention over the last few decades. The goal of segmentation is to split an image into regions according to some criteria such that each region is homogeneous in a sense. Textures are often analyzed across different spatial scales and orientations to generate good feature sets. This approach is supported and motivated to some extent by findings reported in the literature on visual perception in humans and mammals [2, 3]. Gabor functions have been extensively studied for texture segmentation [3, 8, 9] because they allow the design of filters tuned to arbitrary scales and orientations, and they provide good models of neuron responses in the primary visual cortex. The Bamberger directional filter bank (BDFB), originally introduced by Bamberger and Smith [25], is a purely directional decomposition that provides excellent frequency domain selectivity with low computational complexity.
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