ITEM METADATA RECORD
Title: A MODULAR ARTIFICIAL NEURAL NETWORK FOR TEXTURE PROCESSING
Authors: Van Hulle, Marc ×
TOLLENAERE, T #
Issue Date: 1993
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Series Title: Neural networks vol:6 issue:1 pages:7-32
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.
ISSN: 0893-6080
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Group Neurophysiology
× corresponding author
# (joint) last author

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