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PCNN Mechanism and its Parameter Settings
IEEE Transactions on Neural Networks and Learning Systems, 2020The pulse-coupled neural network (PCNN) model is a third-generation artificial neural network without training that uses the synchronous pulse bursts of neurons to process digital images, but the lack of in-depth theoretical research limits its extensive application.
Xiangyu Deng, Chunman Yan, Yide Ma
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Leaf recognition based on PCNN
Neural Computing and Applications, 2015Plant is closely related to humans. How to quickly recognize an unknown plant without related professional knowledge is a huge challenge. With the development of image processing and pattern recognition, it is available for plant recognition based on the technique of image processing. Pulse-coupled neural network is a powerful tool for image processing.
Zhaobin Wang +4 more
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Modified PCNN Filtering for Fingerprint Enhancement
2009 Fifth International Conference on Natural Computation, 2009Enhancement is an important step in fingerprint system. This paper presents one fingerprint enhancement algorithm, which is based on a modified pulse coupled neural network (PCNN). It preprocesses fingerprint to obtain image and orientation masks, and then it iteratively enhances the image by PCNN filtering by the constraints of these two masks ...
Luping Ji, Xiaorong Pu, Guisong Liu
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2013
In this section two digital models evolved from biological cortical models will be presented. The first is the Pulse-Coupled Neural Network (PCNN) which for many years was the standard model for many image processing applications. The PCNN is based solely on the Eckhorn model but there are many other cortical models that exist.
Thomas Lindblad, Jason M. Kinser
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In this section two digital models evolved from biological cortical models will be presented. The first is the Pulse-Coupled Neural Network (PCNN) which for many years was the standard model for many image processing applications. The PCNN is based solely on the Eckhorn model but there are many other cortical models that exist.
Thomas Lindblad, Jason M. Kinser
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Image Segmentation with Simplified PCNN
2009 2nd International Congress on Image and Signal Processing, 2009Image segmentation is an important task for higher level image processing. A simplified pulse coupled neural network (PCNN) was proposed in this study. The comparative experiments were implemented to segment images by Otsu method, improved PCNN and our simplified PCNN algorithm.
Zhiheng Xiao, Jun Shi, Qian Chang
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1998
The PCNN convolution kernel is one of the main components of the PCNN. It can be manipulated to provide a variety of computations. The original Eckhorn model used a Gaussian type of interconnections, but when the PCNN is applied to image processing problems these interconnections are available to the user for altering the behaviour of the network.
Thomas Lindblad, Jason M. Kinser
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The PCNN convolution kernel is one of the main components of the PCNN. It can be manipulated to provide a variety of computations. The original Eckhorn model used a Gaussian type of interconnections, but when the PCNN is applied to image processing problems these interconnections are available to the user for altering the behaviour of the network.
Thomas Lindblad, Jason M. Kinser
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An optimized PCNN for image classification
2014 10th International Computer Engineering Conference (ICENCO), 2014In recent years, Image classification has been a growing research area in the computer vision field. Thus, many approaches were proposed in literature. Moreover, many content-based image classification approaches are widely used in developing applications and techniques for many areas such as remote-sensing and content-based image retrieval.
Mona Mahrous Mohammed +2 more
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Image Processing Applications with a PCNN
2007This paper illustrates the potentials of the PCNN for image processing. A description of three schemes for image processing using the PCNN is presented in this paper. The first scheme is related to image segmentation, the second to automatic target location, ATL, and the third to face recognition.
Mario Ignacio Chacon-Murguia +2 more
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1998
Traditional image processing is a vast and extensive field covering many different approaches. We will not attempt to cover the broad extent of this science here. However, it is important to understand some of the fundamentals of image processing so that comparisons to the PCNN can be made.
Thomas Lindblad, Jason M. Kinser
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Traditional image processing is a vast and extensive field covering many different approaches. We will not attempt to cover the broad extent of this science here. However, it is important to understand some of the fundamentals of image processing so that comparisons to the PCNN can be made.
Thomas Lindblad, Jason M. Kinser
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Delay PCNN and Its Application for Optimization
2004This paper introduces the DPCNN (Delay Pulse Coupled Neural Network) based on the PCNN and uses the DPCNN to find the shortest path. Cauflield and Kinser introduced the PCNN method to solve the maze[1] and although their method also can be used to find the shortest path, a large quantity of neurons are needed.
Xiaodong Gu 0001 +2 more
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