[Allusers] Palestra - A GENERIC FRAMEWORK FOR COLOUR TEXTURE, SEGMENTATION

[Joao P. Barreto]

Venho convidar-vos a assistir à seguinte palestra:

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A GENERIC FRAMEWORK FOR COLOUR TEXTURE SEGMENTATION
                       by
             Dr. PADMAPRIYA NAMMALWAR,

   Quarta-feira, 20/02 às 14:30, Anfiteatro do ISR
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Abstract
A novel method to combine the colour and the texture for colour texture 
segmentation has been proposed. The objective is to derive a framework 
for colour texture segmentation and to determine the contribution of 
colour in colour texture analysis. The colour texture processing is 
based on the feature extraction from colour-textured images. The texture 
features were obtained from the luminance plane along with the colour
features from the chrominance planes. Based on the above mentioned 
approach, a method was developed for colour texture segmentation. The 
proposed method unifies colour and texture features to solve the colour 
texture segmentation problem. Two of the grey scale texture analysis 
techniques, Local Binary Pattern (LBP) and Discrete Cosine Transform
(DCT) based filter approach were extended to colour images. An 
unsupervised k-means clustering was used to cluster pixels in the 
chrominance planes. Non-parametric test was used to test the similarity 
between colour texture regions. An unsupervised texture
segmentation method was followed to obtain the segmented image. A 
quantitative estimation of colour and texture performance in 
segmentation was presented. The use of different colour spaces was also 
investigated in this study. The proposed method was tested using 
different mosaic and natural images obtained from VisTex and other
predominant image database used in computer vision. The applications for 
the proposed colour texture segmentation method are, Irish Script On 
Screen (ISOS) images for the segmentation of the colour textured regions 
in the document, skin cancer images to identify the diseased area and 
Sediment Profile Imagery (SPI) to segment underwater images. The 
inclusion of colour and texture as distributions of regions provided a 
good discrimination of the colour and the texture. The results indicate 
that the incorporation of colour information enhances the texture 
analysis techniques and the methodology proved effective and efficient.
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