A vectorizer and feature extractor for document recognition
Computer Vision, Graphics, and Image Processing
Thinning Methodologies-A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast parallel algorithm for thinning digital patterns
Communications of the ACM
A novel triangulation procedure for thinning hand-written text
Pattern Recognition Letters
IEEE Transactions on Neural Networks
Perfect image segmentation using pulse coupled neural networks
IEEE Transactions on Neural Networks
Finding the shortest path in the shortest time using PCNN's
IEEE Transactions on Neural Networks
Object detection using pulse coupled neural networks
IEEE Transactions on Neural Networks
Binary Image Thinning Using Autowaves Generated by PCNN
Neural Processing Letters
Skeletonization based on error reduction
Pattern Recognition
Letters: A class of binary images thinning using two PCNNs
Neurocomputing
Feature Extraction using Unit-linking Pulse Coupled Neural Network and its Applications
Neural Processing Letters
MR Image Registration Based on Pulse-Coupled Neural Networks
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Classification Using Multi-valued Pulse Coupled Neural Network
Neural Information Processing
Review article: Review of pulse-coupled neural networks
Image and Vision Computing
Pulse Coupled Neural Networks for Automatic Urban Change Detection at Very High Spatial Resolution
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
An improved contour-based thinning method for character images
Pattern Recognition Letters
A novel CNN template design method based on GIM
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Object detection using unit-linking PCNN image icons
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
A robust approach to digit recognition in noisy environments
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Review: Pulse coupled neural networks and its applications
Expert Systems with Applications: An International Journal
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PCNN-pulse coupled neural network, based on the phenomena of synchronous pulse bursts in the animal visual cortex, is different from traditional artificial neural networks. This paper first introduces a new approach for binary image thinning by using the pulse parallel transmission characteristic of PCNN. The thinning result obtains when pulses emitted by background meet. The criterion of pulse meeting and the criterion of thinning completion are proposed. The computer simulation results of applying the method to thin binary image are present. Comparisons of skeleton structure and execution time with results from other thinning methods are present too. The PCNN skeleton retains more information of original binary image, such as the size of a quadrate, than the result from Zhang and Suen method. The procedure is faster than Arcelli et al. thinning method when the image resolution is from 600 to 1800 dpi. Combining with PCNN restoration algorithm (namely PCNN noise-reducing algorithm), the skeletons of the objects in a noisy binary image can be obtained with the accuracy. This paper also expands the application field of PCNN.