Visual-cognitive neuronal networks
Vision, brain, and cooperative computation
Image algebra techniques for parallel image processing
Journal of Parallel and Distributed Computing
Computer Vision, Graphics, and Image Processing
Cortical connections and parallel processing structure and function
Vision, brain, and cooperative computation
Neural network architectures: an introduction
Neural network architectures: an introduction
Classification of lattice transformations in image processing
CVGIP: Image Understanding
Morphology neural networks: an introduction with applications
Circuits, Systems, and Signal Processing - Special issue: networks for neural processing
DARPA Neural Network Stdy
Neural Structures to Compute Homothetic Invariances for Artificial Perception Systems
EUROCAST '91 Proceedings of the A Selection of Papers from the Second International Workshop on Computer Aided Systems Theory
An Approach to Object Recognition: Aligning Pictorial Descriptions
An Approach to Object Recognition: Aligning Pictorial Descriptions
Reconstruction of Patterns from Noisy Inputs Using Morphological Associative Memories
Journal of Mathematical Imaging and Vision
Morphological Hetero-Associative Memories Applied to Restore True-Color Patterns
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Morphological auto-associative memories applied to true-color image patterns
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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In this paper we propose a theoretical approach toinvariant perception. Invariant perception is an importantaspect in both natural and artificial perception systems, and itremains an important unsolved problem in heuristically basedpattern recognition. Our approach is based on a general theoryof neural networks and studies of invariant perception by thecortex. The neural structures that we propose uphold both thearchitecture and functionality of the cortex as currentlyunderstood.The formulation of the proposed neural structuresis in the language of image algebra, a mathematical environmentfor expressing image processing algorithms. Thus, an additionalbenefit of our study is the implication that image algebraprovides an excellent environment for expressing and developingartificial perception systems.The focus of our study is oninvariances that are expressible in terms of affinetransformations, specifically, homothetic transformations. Ourdiscussion will include both one-dimensional andtwo-dimensional signal patterns. The main contribution of thispaper is the formulation of several novel morphological neuralnetworks that compute homothetic auditory and visualinvariances. With respect to the latter, we employ the theoryand trends of currently popular artificial vision systems.