1994 Special Issue: A fast dynamic link matching algorithm for invariant pattern recognition
Neural Networks - Special issue: models of neurodynamics and behavior
Data-driven registration for local deformations
Pattern Recognition Letters
Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing maps
On the performance of neuronal matching algorithms
Neural Networks
Parallel implementation of self-organizing maps
Self-Organizing neural networks
Computer and Robot Vision
Topology preserving deformable image matching using constrained hierarchical parametric models
IEEE Transactions on Image Processing
Extracting drug utilization knowledge using self-organizing map and rough set theory
Expert Systems with Applications: An International Journal
Application of Kohonen network for automatic point correspondence in 2D medical images
Computers in Biology and Medicine
Visual object tracking by an evolutionary self-organizing neural network
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
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A general approach to the problem of image matching which exploits a multi-scale representation of local image structure and the principles of self-organizing neural networks is introduced. The problem considered is relevant in many imaging applications and has been largely investigated in medical imagery, especially as regards the integration of different imaging procedures.A given pair of images to be matched, named target and stimulus respectively, are represented by Gabor Wavelets. Correspondence is computed by exploiting the learning procedure of a neural network derived from Kohonen's SOM. The SOM units coincide with the pixels of the target image and their weight are pointers to those of the stimulus images. The standard SOM rule is modified so as to account for image features. The properties of our method are tested by experiments performed on synthetic images. The considered implementation has shown that is able to recover a wide range of transformations including global affine transformations and local distortions. Tests in the presence of additive noise indicate considerable robustness against statistical variability. Applications to clinical images are presented.