A fundamental goal of texture research is to develop automated computational methods
for retrieving visual information and understanding image content based on textural
properties in images. A synergy between biological and computer vision research
in low-level vision can give substantial insights about the processes for extracting
color, edge, motion, and spatial frequency information from images. In this thesis,
we seek to understand the texture processing that takes place in low level human
vision in order to develop new and effective methods for texture analysis in computer
vision.
The different representations formed by the early stages of HVS and visual computations
carried out by them to handle various texture patterns is of interest. Such
information is needed to identify the mechanisms that can be use in texture analysis
tasks. We examine two types of cells, namely the bar and grating cells, which
have been identified in literature to play an important role in texture processing, and
develop functional models for the same. The model for the bar cell is based on the
notion of surround inhibition and excitation. Whereas, the model for the grating cell
is based on the fact that a grating cell receives direct inputs from the M-type ganglion
cells. The representations derived by these cells are used to design solutions to two
important problems of texture: texture based segmentation and classification. The
former is addressed in the domain of natural image understanding and the latter is
addressed in the domain of document image understanding.
Based on our work, we conclude that the early stages of HVS effectively represent
various texture patterns and also provide ample information to solve the higher level
texture analysis tasks. The richness of information emerges from the capability of
the HVS to extract global visual primitives from local features. The presented work
is an initial attempt to integrate the current knowledge of HVS mechanisms and
computational theories developed for texture analysis.
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Year of completion:
Dec 2006
Advisor(s) :
Jayanthi Sivaswamy
Related Publications
Gopal Datt Joshi, Saurabh Garg and Jayanthi Sivaswamy
A generalised framework for script identification
in International Journal on Document Analysis and Recognition (IJDAR) , 2007, DOI 10.1007/s10032-007-0043-3 pp 1433-2833.
Gopal Datt Joshi and Jayanthi Sivaswamy
A Computational Model for Boundary Detection
in 5th Indian Conference on Computer Vision, Graphics & Image Processing (ICVGIP), 2006, pp 172-183.
Gopal Datt Joshi, Saurabh Garg and Jayanthi Sivaswamy
Script Identification from Indian Documents
in Document Analysis Systems (DAS), 2006, pp 255-267.
Gopal Datt Joshi and Jayanthi Sivaswamy
A Simple Scheme for Contour Detection
in International Conference on Computer Vision Theory and Applications (VISAPP), 2006, pp 236-242.
Gopal Datt Joshi and Jayanthi Sivaswamy
Multi-scale Approach to Salient Contour Detection
in International Conference on Cognition and Recognition (ICCR), 2005, pp 186-193.