A region-based multi-sensor image fusion scheme using pulse-coupled neural network
Pattern Recognition Letters
A region-based multi-sensor image fusion scheme using pulse-coupled neural network
Pattern Recognition Letters
Feature Extraction using Unit-linking Pulse Coupled Neural Network and its Applications
Neural Processing Letters
Fingerprint orientation field estimation using ridge projection
Pattern Recognition
Fingerprint orientation field estimation using ridge projection
Pattern Recognition
Classification Using Multi-valued Pulse Coupled Neural Network
Neural Information Processing
New shape-based auroral oval segmentation driven by LLS-RHT
Pattern Recognition
Review article: Review of pulse-coupled neural networks
Image and Vision Computing
Real-time robot path planning based on a modified pulse-coupled neural network model
IEEE Transactions on Neural Networks
Shadows attenuation for robust object recognition
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
A neuron-MOS-based VLSI implementation of pulse-coupled neural networks for image feature generation
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Adaptive parameters determination method of pulse coupled neural network based on water valley area
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Object detection using unit-linking PCNN image icons
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Attention selection using global topological properties based on pulse coupled neural network
Computer Vision and Image Understanding
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This paper introduces an approach for image shadow removal by using pulse coupled neural network (PCNN), based on the phenomena of synchronous pulse bursts in the animal visual cortexes. Two shadow-removing criteria are proposed. These two criteria decide how to choose the optimal parameter (the linking strength β). The computer simulation results of shadow removal based on PCNN show that if these two criteria are satisfied, shadows are removed completely and the shadow-removed images are almost as the same as the original nonshadowed images. The shadow removal results are independent of changes of intensities of shadows in some range and variations of the places of shadows. When the first criterion is satisfied, even if the second criterion is not satisfied, as to natural grey images that have abundant grey levels, shadows also can be removed and PCNN shadow-removed images retain the shapes of the objects in original images. These two criteria also can be used for color images by dividing a color image into three channels (R, G, B). For shadows varying drastically, such as the noisy points in images, these two criteria are still right, but difficult to satisfy. Therefore, this approach can efficiently remove shadows that do not include the random noise.