Original Contribution: A modular artificial neural network for texture processing

Authors:
M. M. Van Hulle;T. Tollenaere
Affiliations:
-;-
Venue:
Neural Networks
Year:
1993

Citing 12
Cited 5

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Computer Vision, Graphics, and Image Processing
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Texture discrimination by Gabor functions

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Spatial frequency channels and perceptual grouping in texture segregation

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Multichannel Texture Analysis Using Localized Spatial Filters

IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial and temporal processing in central auditory networks

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Computing with arrays of coupled oscillators: an application to preattentive texture discrimination

Neural Computation
Organizing and integrating edge segments for texture discrimination

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Parallel implementation and capabilities of entropy-driven artificial neural networks

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Parallel Computing

A discrete region competition approach incorporating weak edge enhancement for ultrasound image segmentation

Pattern Recognition Letters - Speciqal issue: Ultrasonic image processing and analysis
Applying wavelets transform, rough set theory and support vector machine for copper clad laminate defects classification

Expert Systems with Applications: An International Journal
Applying wavelets transform and support vector machine for copper clad laminate defects classification

Computers and Industrial Engineering
A novel Bayesian learning method for information aggregation in modular neural networks

Expert Systems with Applications: An International Journal
Micro-crack inspection in heterogeneously textured solar wafers using anisotropic diffusion

Image and Vision Computing

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Abstract

This paper presents a new network-based model for segregating broadband noise textures. The model starts with the oriented local energy maps obtained from filtering the textures with a bank of quadrature pair Gabor filters with different preferred orientations and spatial frequencies, and squaring and summing the quadrature pair filter outputs point-wise. Rather than detecting differences in first-order statistics from these maps, a sequence of two network modules is used for each spatial frequency channel. The modules are based on the Entropy Driven Artificial Neural Network (EDANN) model, a previously developed adaptive network module for line- and edge detection. The first EDANN module performs orientation extraction and the second performs filling-in of missing orientation information. The aim of both network modules is to produce a reliable texture segregation based on an enlarged local difference in first-order statistics in the mean and at the same time a reduced importance of differences in spatial variability; the texture boundary is detected using a third EDANN module, following the second one. Other major features of the model are: (a) texture segregation proceeds in each spatial frequency/orientation channel separately, and (b) texture segregation as well as texture boundary detection can be performed using the same core network module.