Jason M. Kinser – författare

For decades, researchers have been developing algorithms to manipulate and analyze images. From this, a common set of image tools now appear in many high-level programming languages. Consequently, the amount of coding required by a user has significantly lessened over the years. While the libraries for image analysis are coalescing to a common toolkit, the language of image analysis has remained stagnant. Often, textual descriptions of an analytical protocol consume far more real estate than does the computer code required to execute the processes. Furthermore, the textual explanations are sometimes vague or incomplete. This book offers a precise mathematical language for the field of image processing. Defined operators correspond directly to standard library routines, greatly facilitating the translation between mathematical descriptions and computer script. This text is presented with Python 3 examples.

This text will provide a unified language for image processing

Provides the theoretical foundations with accompanied Python® scripts to precisely describe steps in image processing applications

Linkage between scripts and theory through operators will be presented

All chapters will contain theories, operator equivalents, examples, Python® codes, and exercises

For decades, researchers have been developing algorithms to manipulate and analyze images. From this, a common set of image tools now appear in many high-level programming languages. Consequently, the amount of coding required by a user has significantly lessened over the years. While the libraries for image analysis are coalescing to a common toolkit, the language of image analysis has remained stagnant. Often, textual descriptions of an analytical protocol consume far more real estate than does the computer code required to execute the processes. Furthermore, the textual explanations are sometimes vague or incomplete. This book offers a precise mathematical language for the field of image processing. Defined operators correspond directly to standard library routines, greatly facilitating the translation between mathematical descriptions and computer script. This text is presented with Python 3 examples.

This text will provide a unified language for image processing

Provides the theoretical foundations with accompanied Python® scripts to precisely describe steps in image processing applications

Linkage between scripts and theory through operators will be presented

All chapters will contain theories, operator equivalents, examples, Python® codes, and exercises

The use of Python as a powerful computational tool is expanding with great strides. Python is a language which is easy to use, and the libraries of tools provides it with efficient versatility. As the tools continue to expand, users can create insightful models and simulations. While the tools offer an easy method to create a pipeline, such constructions are not guaranteed to provide correct results. A lot of things can go wrong when building a simulation - deviously so. Users need to understand more than just how to build a process pipeline.Modeling and Simulation in Python introduces fundamental computational modeling techniques that are used in a variety of science and engineering disciplines. It emphasizes algorithmic thinking skills using different computational environments, and includes a number of interesting examples, including Shakespeare, movie databases, virus spread, and Chess.Key Features:Several theories and applications are provided, each with working Python scripts.All Python functions written for this book are archived on GitHub.Readers do not have to be Python experts, but a working knowledge of the language is required. Students who want to know more about the foundations of modeling and simulation will find this an educational and foundational resource.

The use of Python as a powerful computational tool is expanding with great strides. Python is a language which is easy to use, and the libraries of tools provides it with efficient versatility. As the tools continue to expand, users can create insightful models and simulations. While the tools offer an easy method to create a pipeline, such constructions are not guaranteed to provide correct results. A lot of things can go wrong when building a simulation - deviously so. Users need to understand more than just how to build a process pipeline.Modeling and Simulation in Python introduces fundamental computational modeling techniques that are used in a variety of science and engineering disciplines. It emphasizes algorithmic thinking skills using different computational environments, and includes a number of interesting examples, including Shakespeare, movie databases, virus spread, and Chess.Key Features:Several theories and applications are provided, each with working Python scripts.All Python functions written for this book are archived on GitHub.Readers do not have to be Python experts, but a working knowledge of the language is required. Students who want to know more about the foundations of modeling and simulation will find this an educational and foundational resource.

For decades, researchers have been developing algorithms to manipulate and analyze images. From this, a common set of image tools now appear in many high-level programming languages. Consequently, the amount of coding required by a user has significantly lessened over the years. While the libraries for image analysis are coalescing to a common toolkit, the language of image analysis has remained stagnant. Often, textual descriptions of an analytical protocol consume far more real estate than does the computer code required to execute the processes. Furthermore, the textual explanations are sometimes vague or incomplete. This book offers a precise mathematical language for the field of image processing. Defined operators correspond directly to standard library routines, greatly facilitating the translation between mathematical descriptions and computer script. This text is presented with Python 3 examples.This text will provide a unified language for image processing Provides the theoretical foundations with accompanied Python® scripts to precisely describe steps in image processing applications Linkage between scripts and theory through operators will be presented All chapters will contain theories, operator equivalents, examples, Python® codes, and exercises

Pulse-coupled neural networks represent a new and exciting advance in image processing research. When exposed to grey scale or colour images they produce a series of binary pulse images which allow the content of the image to be assessed much more accurately than from the original. In this volume Thomas Lindblad and Jason Kinser provide a much needed introduction to the topic of PCNNs. They review the theoretical foundations, and then look at a number of image processing applications including segmentation, edge extraction, texture extraction, object identification, object isolation, motion processing, foveation, noise suppression and image fusion. They also look at the PCNNs ability to process logical arguments and at how to implement it in specialised hardware. It will be of particular interest to researchers and practitioners working in image processing, especially those involved with medical, military or industrial applications. It will also be of interest to graduate-level students.

For decades, researchers have been developing algorithms to manipulate and analyze images. From this, a common set of image tools now appear in many high-level programming languages. Consequently, the amount of coding required by a user has significantly lessened over the years. While the libraries for image analysis are coalescing to a common toolkit, the language of image analysis has remained stagnant. Often, textual descriptions of an analytical protocol consume far more real estate than does the computer code required to execute the processes. Furthermore, the textual explanations are sometimes vague or incomplete. This book offers a precise mathematical language for the field of image processing. Defined operators correspond directly to standard library routines, greatly facilitating the translation between mathematical descriptions and computer script. This text is presented with Python 3 examples.This text will provide a unified language for image processing Provides the theoretical foundations with accompanied Python® scripts to precisely describe steps in image processing applications Linkage between scripts and theory through operators will be presented All chapters will contain theories, operator equivalents, examples, Python® codes, and exercises

It was stated in the preface to the ?rst edition of this book that image p- cessing by electronic means has been a very active ?eld for decades. This is certainly still true and the goal has been, and still is, to have a machine perform the same image functions which humans do quite easily. In reaching this goal we have learnt about the human mechanisms and how to apply this knowledgetoimageprocessingproblems. Althoughthereisstillalongwayto go, we have learnt a lot during the last ?ve or six years. This new information andsomeideasbaseduponithasbeenaddedtothesecondeditionofourbook The present edition includes the theory and application of two cortical models: the PCNN (pulse coupled neural network) and the ICM (intersecting cortical model). These models are based upon biological models of the visual cortex and it is prudent to review the algorithms that strongly in?uenced the development of the PCNN and ICM. The outline of the book is otherwise very much the same as in the ?rst edition although several new application examples have been added. In Chap. 7 a few of these applications will be reviewed including original ideas by co-workers and colleagues.Special thanks are due to Soonil D. D. V. Rughooputh,thedeanoftheFacultyofScienceattheUniversityofMauritius Guisong,andHarryC. S. Rughooputh,thedeanoftheFacultyofEngineering at the University of Mauritius. We should also like to acknowledge that Guisong Wang, a doctoral c- didate in the School of Computational Sciences at GMU, made a signi?cant contribution to Chap. 5. We would also like to acknowledge the work of several diploma and Ph. D. studentsatKTH,inparticularJennyAtmer,NilsZetterlundandUlfEkblad.

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.

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.