A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Mapping part-whole hierarchies into connectionist networks
Artificial Intelligence - On connectionist symbol processing
Pattern Recognition Letters
Finding circles by an array of accumulators
Communications of the ACM
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Computer Vision
PsyCOP-a psychologically motivated connectionist system for object perception
IEEE Transactions on Neural Networks
Online adaptive decision trees
Neural Computation
Theoretical quantification of shape distortion in fuzzy Hough transform
Fuzzy Sets and Systems
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A single-layered Hough transform network is proposed that accepts image coordinates of each object pixel as input and produces a set of outputs that indicate the belongingness of the pixel to a particular structure (e.g., a straight line). The network is able to learn adaptively the parametric forms of the linear segments present in the image. It is designed for learning and identification not only of linear segments in two-dimensional images but also the planes and hyperplanes in the higher-dimensional spaces. It provides an efficient representation of visual information embedded in the connection weights. The network not only reduces the large space requirement, as in the case of classical Hough transform, but also represents the parameters with high precision.