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Image Processing Using Pulse-Coupled Neural Networks

Author: Thomas Lindblad

Publisher: Springer Science & Business Media

Published: 2005-08-02

Total Pages: 184

ISBN-13: 9783540242185

* Weitere Angaben Verfasser: Thomas Lindblad is a professor at the Royal Institute of Technology (Physics) in Stockholm. Working and teaching nuclear and environmental physics his main interest is with sensors, signal processing and intelligent data analysis of torrent data from experiments on-line accelerators, in space, etc. Jason Kinser is an associate professor at George Mason University. He has developed a plethora of image processing applications in the medical, military, and industrial fields. He has been responsible for the conversion of PCNN theory into practical applications providing many improvements in both speed and performance

Image Processing using Pulse-Coupled Neural Networks

Author: Thomas Lindblad

Publisher: Springer Science & Business Media

Published: 2013-04-17

Total Pages: 154

ISBN-13: 1447136179

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PCNNs represent a new advance in imaging technology, allowing images to be refined to levels well beyond that of the original. This volume provides an introduction to the topic by reviewing the theoretical foundations as well as a number of image processing applications, including segmentation, edge extraction, texture extraction, object identification, object isolation, motion processing, noise suppression, and image fusion. This is the first book to cover PCNN technology, an area which will have many applications in medical, military and industrial imaging.

Image Processing using Pulse-Coupled Neural Networks

Author: Thomas Lindblad

Publisher: Springer Science & Business Media

Published: 2013-05-13

Total Pages: 246

ISBN-13: 3642368778

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Image processing algorithms based on the mammalian visual cortex are powerful tools for extraction information and manipulating images. This book reviews the neural theory and translates them into digital models. Applications are given in areas of image recognition, foveation, image fusion and information extraction. The third edition reflects renewed international interest in pulse image processing with updated sections presenting several newly developed applications. This edition also introduces a suite of Python scripts that assist readers in replicating results presented in the text and to further develop their own applications.

Applications of Pulse-Coupled Neural Networks

Author: Yide Ma

Publisher: Springer Science & Business Media

Published: 2011-09-02

Total Pages: 206

ISBN-13: 3642137458

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"Applications of Pulse-Coupled Neural Networks" explores the fields of image processing, including image filtering, image segmentation, image fusion, image coding, image retrieval, and biometric recognition, and the role of pulse-coupled neural networks in these fields. This book is intended for researchers and graduate students in artificial intelligence, pattern recognition, electronic engineering, and computer science. Prof. Yide Ma conducts research on intelligent information processing, biomedical image processing, and embedded system development at the School of Information Science and Engineering, Lanzhou University, China.

Image Processing Using Pulse-Coupled Neural Networks

Author: Thomas Lindblad

Publisher: Springer

Published: 2009-09-02

Total Pages: 164

ISBN-13: 9783540806509

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Practical Machine Learning and Image Processing

Author: Himanshu Singh

Publisher: Apress

Published: 2019-02-26

Total Pages: 177

ISBN-13: 1484241495

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Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. What You Will LearnDiscover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software developers interested in image processing and computer vision.

Non-Cooperative Target Tracking, Fusion and Control

Author: Zhongliang Jing

Publisher: Springer

Published: 2018-06-25

Total Pages: 340

ISBN-13: 3319907166

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This book gives a concise and comprehensive overview of non-cooperative target tracking, fusion and control. Focusing on algorithms rather than theories for non-cooperative targets including air and space-borne targets, this work explores a number of advanced techniques, including Gaussian mixture cardinalized probability hypothesis density (CPHD) filter, optimization on manifold, construction of filter banks and tight frames, structured sparse representation, and others. Containing a variety of illustrative and computational examples, Non-cooperative Target Tracking, Fusion and Control will be useful for students as well as engineers with an interest in information fusion, aerospace applications, radar data processing and remote sensing.

VLSI Design of Neural Networks

Author: Ulrich Ramacher

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 346

ISBN-13: 1461539943

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The early era of neural network hardware design (starting at 1985) was mainly technology driven. Designers used almost exclusively analog signal processing concepts for the recall mode. Learning was deemed not to cause a problem because the number of implementable synapses was still so low that the determination of weights and thresholds could be left to conventional computers. Instead, designers tried to directly map neural parallelity into hardware. The architectural concepts were accordingly simple and produced the so called interconnection problem which, in turn, made many engineers believe it could be solved by optical implementation in adequate fashion only. Furthermore, the inherent fault-tolerance and limited computation accuracy of neural networks were claimed to justify that little effort is to be spend on careful design, but most effort be put on technology issues. As a result, it was almost impossible to predict whether an electronic neural network would function in the way it was simulated to do. This limited the use of the first neuro-chips for further experimentation, not to mention that real-world applications called for much more synapses than could be implemented on a single chip at that time. Meanwhile matters have matured. It is recognized that isolated definition of the effort of analog multiplication, for instance, would be just as inappropriate on the part ofthe chip designer as determination of the weights by simulation, without allowing for the computing accuracy that can be achieved, on the part of the user.

Nature-Inspired Design of Hybrid Intelligent Systems

Author: Patricia Melin

Publisher: Springer

Published: 2016-12-08

Total Pages: 838

ISBN-13: 331947054X

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This book highlights recent advances in the design of hybrid intelligent systems based on nature-inspired optimization and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction, and optimization of complex problems. The book is divided into seven main parts, the first of which addresses theoretical aspects of and new concepts and algorithms based on type-2 and intuitionistic fuzzy logic systems. The second part focuses on neural network theory, and explores the applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The book’s third part presents enhancements to meta-heuristics based on fuzzy logic techniques and describes new nature-inspired optimization algorithms that employ fuzzy dynamic adaptation of parameters, while the fourth part presents diverse applications of nature-inspired optimization algorithms. In turn, the fifth part investigates applications of fuzzy logic in diverse areas, such as time series prediction and pattern recognition. The sixth part examines new optimization algorithms and their applications. Lastly, the seventh part is dedicated to the design and application of different hybrid intelligent systems.

Intelligence Science and Big Data Engineering

Author: Yi Sun

Publisher: Springer

Published: 2017-09-14

Total Pages: 0

ISBN-13: 9783319677767

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This book constitutes the proceedings of the 7th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2017, held in Dalian, China, in September 2017.The 48 full papers and 14 short papers presented in this volume were carefully reviewed and selected from 121 submissions. They deal with statistics and learning; deep neural networks; faces and people; objects; classification and clustering; imaging; biomedical signal processing; and recommendation.