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AN ENHANCED LOCAL TEXTURE DESCRIPTOR FOR IMAGE SEGMENTATION

Citation Author(s):
Manzur Murshed, Shyh Wei Teng, Gour Karmakar
Submitted by:
Sheikh Tania
Last updated:
3 November 2020 - 11:06pm
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters:
Sheikh Tania
Paper Code:
2389

Abstract

Texture is an indispensable property to develop many vision
based autonomous applications. Compared to colour, feature
dimension in a local texture descriptor is quite large as dense
texture features need to represent the distribution of pixel intensities
in the neighbourhood of each pixel. Large dimensional
features require additional time for further processing
that often restrict real-time applications. In this paper, a robust
local texture descriptor is enhanced by reducing feature
dimension by three folds without compromising the accuracy
in region-based image segmentation applications. Reduction
in feature dimension is achieved by exploiting the mean of
neighbourhood pixel intensities radially along lines across a
certain radius, which eliminates the need for sampling intensity
distribution at three scales. Both the results of benchmark
metrics and computational time are promising when the enhanced
texture feature is used in a region-based hierarchical
segmentation algorithm, a recent state-of-the-art technique.

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