Image object signatures from centripetal autowaves
Pattern Recognition Letters
Image analysis for airborne reconnaissance and missile applications
Pattern Recognition Letters
Pattern Recognition Letters
Image Processing Using Pulse-Coupled Neural Networks
Image Processing Using Pulse-Coupled Neural Networks
An Analog VLSI Pulsed Neural Network Implementation for Image Segmentation
PARELEC '04 Proceedings of the international conference on Parallel Computing in Electrical Engineering
The design and analysis of a CMOS low-power large-neighborhood CNN with propagating connections
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Salient feature extraction of industrial objects for an automated assembly system
Computers in Industry
Binary Fingerprint Image Thinning Using Template-Based PCNNs
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
An accelerator for neural networks with pulse-coded model neurons
IEEE Transactions on Neural Networks
Analog implementation of pulse-coupled neural networks
IEEE Transactions on Neural Networks
Pulse-coupled neural networks for contour and motion matchings
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Image shadow removal using pulse coupled neural network
IEEE Transactions on Neural Networks
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An analog circuit for implementing pulse-coupled neural networks (PCNNs) in very-large-scale integration (VLSI) hardware has been developed using the Neuron-MOS (νMOS) technology. PCNNs are biologically inspired models having powerful ability for image feature generation. With the νMoS technology, weighted sum of multiple input signals, which is an essential of PCNNs, is implemented simply by the capacitive coupling effect in a νMOS block. By employing the switched floating gates in the νMOS blocks as temporary analog memories, the storage of image data is simply realized. Moreover, the function of decay generation, which is crucial for emulating PCNNs neuronal dynamics, is also merged into a νMOS block by utilizing the input-terminal capacitors in it. With such techniques, the circuit achieves a purely voltage-mode implementation of PCNNs in a compact structure. Inheriting the merits of PCNNs, the circuit has good discriminability against different patterns as well as robustness against rotation and translation of identical patterns, which is analogous to human image perception. The performance of the circuit has been verified by the measurements of a proof-of-concept chip fabricated in a O.35-µm double-polysilicon CMOS technology.