Journal ArticleDOI
PsyCOP-a psychologically motivated connectionist system for object perception
Jayanta Basak,Sankar K. Pal +1 more
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TLDR
A connectionist system has been designed for learning and simultaneous recognition of flat industrial objects by integrating the psychological hypotheses with the generalized Hough transform technique, which uses the mechanism of selective attention for initial hypotheses generation.Abstract:
A connectionist system has been designed for learning and simultaneous recognition of flat industrial objects (based an the concepts of conventional and structured connectionist computing) by integrating the psychological hypotheses with the generalized Hough transform technique. The psychological facts include the evidence of separation of two regions for identification ("what it is") and pose estimation ("where it is"). The system uses the mechanism of selective attention for initial hypotheses generation. A special two-stage training paradigm has been developed for learning the structural relationships between the features and objects and the importance values of the features with respect to the objects. The performance of the system has been demonstrated on real-life data both for single and mixed (overlapped) instances of object categories. The robustness of the system with respect to noise and false alarming has been theoretically investigated. >read more
Citations
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Journal ArticleDOI
Image processing with neural networks–a review
TL;DR: The various applications of neural networks in image processing are categorised into a novel two-dimensional taxonomy for image processing algorithms and their specific conditions are discussed in detail.
Neocognitron--A New Algorithm for Pattern Recognition Tolerant of Deformations and Shifts in Position
Kunihiko Fukushima,Sei Miyake +1 more
TL;DR: The neocognitron recognizes stimulus patterns correctly without being affected by shifts in position or even by considerable distortions in shape of the stimulus patterns.
Journal ArticleDOI
Modularity in neural computing
Terry Caelli,Ling Guan,W. Wen +2 more
TL;DR: It is argued that this modular approach to neural computing is more in line with the neurophysiology of the vertebrate cerebral cortex, particularly with respect to sensation and perception, and has the potential to aid in solutions to large-scale network computational problems.
Journal ArticleDOI
A connectionist model for corner detection in binary and gray images
TL;DR: A connectionist model along with its state dynamics is developed for detecting corner points in binary and gray images and it is found that the network is able to detect corner points even in the noisy images and for open object boundaries.
Journal ArticleDOI
Hough transform network: learning conoidal structures in a connectionist framework
TL;DR: A two-layer neural-network model is designed which accepts image coordinates as the input and learns the parametric form of conoidal shapes (lines/circles/ellipses) adaptively, which not only reduces the large space requirements of the classical Hough transform, but also represents parameters with a higher precision.
References
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Generalizing the hough transform to detect arbitrary shapes
TL;DR: It is shown how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough transform space, which makes the generalized Houghtransform a kind of universal transform which can beused to find arbitrarily complex shapes.
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