Machine Vision Algorithms in Java

Machine Vision Algorithms in Java
Author: Paul F. Whelan
Publisher: Springer Science & Business Media
Total Pages: 293
Release: 2012-12-06
Genre: Computers
ISBN: 1447102517

This book presents key machine vision techniques and algorithms, along with the associated Java source code. Special features include a complete self-contained treatment of all topics and techniques essential to the understanding and implementation of machine vision; an introduction to object-oriented programming and to the Java programming language, with particular reference to its imaging capabilities; Java source code for a wide range of real-world image processing and analysis functions; an introduction to the Java 2D imaging and Java Advanced Imaging (JAI) API; and a wide range of illustrative examples.

Efficient Implementations of Machine Vision Algorithms Using a Dynamically Typed Programming Language

Efficient Implementations of Machine Vision Algorithms Using a Dynamically Typed Programming Language
Author: Jan Wedekind
Publisher:
Total Pages:
Release: 2012
Genre:
ISBN:

Current machine vision systems (or at least their performance critical parts) are predominantly implemented using statically typed programming languages such as C, C++, or Java. Statically typed languages however are unsuitable for development and maintenance of large scale systems. When choosing a programming language, dynamically typed languages are usually not considered due to their lack of support for high-performance array operations. This thesis presents efficient implementations of machine vision algorithms with the (dynamically typed) Ruby programming language. The Ruby programming language was used, because it has the best support for meta-programming among the currently popular programming languages. Although the Ruby programming language was used, the approach presented in this thesis could be applied to any programming language which has equal or stronger support for meta-programming (e.g. Racket (former PLT Scheme)). A Ruby library for performing I/O and array operations was developed as part of this thesis. It is demonstrated how the library facilitates concise implementations of machine vision algorithms commonly used in industrial automation. I.e. this thesis is about a different way of implementing machine vision systems. The work could be applied to prototype and in some cases implement machine vision systems in industrial automation and robotics. The development of real-time machine vision software is facilitated as follows 1. A JIT compiler is used to achieve real-time performance. It is demonstrated that the Ruby syntax is sufficient to integrate the JIT compiler transparently. 2. Various I/O devices are integrated for seamless acquisition, display, and storage of video and audio data. In combination these two developments preserve the expressiveness of the Ruby programming language while providing good run-time performance of the resulting implementation. To validate this approach, the performance of different operations is compared with the performance of equivalent C/C++ programs.

Hands-On Java Deep Learning for Computer Vision

Hands-On Java Deep Learning for Computer Vision
Author: Klevis Ramo
Publisher: Packt Publishing Ltd
Total Pages: 253
Release: 2019-02-21
Genre: Computers
ISBN: 1838552138

Leverage the power of Java and deep learning to build production-grade Computer Vision applications Key FeaturesBuild real-world Computer Vision applications using the power of neural networks Implement image classification, object detection, and face recognitionKnow best practices on effectively building and deploying deep learning models in JavaBook Description Although machine learning is an exciting world to explore, you may feel confused by all of its theoretical aspects. As a Java developer, you will be used to telling the computer exactly what to do, instead of being shown how data is generated; this causes many developers to struggle to adapt to machine learning. The goal of this book is to walk you through the process of efficiently training machine learning and deep learning models for Computer Vision using the most up-to-date techniques. The book is designed to familiarize you with neural networks, enabling you to train them efficiently, customize existing state-of-the-art architectures, build real-world Java applications, and get great results in a short space of time. You will build real-world Computer Vision applications, ranging from a simple Java handwritten digit recognition model to real-time Java autonomous car driving systems and face recognition models. By the end of this book, you will have mastered the best practices and modern techniques needed to build advanced Computer Vision Java applications and achieve production-grade accuracy. What you will learnDiscover neural networks and their applications in Computer VisionExplore the popular Java frameworks and libraries for deep learningBuild deep neural networks in Java Implement an end-to-end image classification application in JavaPerform real-time video object detection using deep learningEnhance performance and deploy applications for productionWho this book is for This book is for data scientists, machine learning developers and deep learning practitioners with Java knowledge who want to implement machine learning and deep neural networks in the computer vision domain. You will need to have a basic knowledge of Java programming.

Machine Vision

Machine Vision
Author: Wesley E. Snyder
Publisher: Cambridge University Press
Total Pages: 452
Release: 2010-11-25
Genre: Computers
ISBN: 1139489925

This 2004 book is an accessible and comprehensive introduction to machine vision. It provides all the necessary theoretical tools and shows how they are applied in actual image processing and machine vision systems. A key feature is the inclusion of many programming exercises that give insights into the development of practical image processing algorithms. The authors begin with a review of mathematical principles and go on to discuss key issues in image processing such as the description and characterization of images, edge detection, restoration and feature extraction, segmentation, texture and shape. They also discuss image matching, statistical pattern recognition, clustering, and syntactic pattern recognition. Important applications are described, including optical character recognition and automatic target recognition. Software and data used in the book can be found at www.cambridge.org/9780521830461. A useful reference for practitioners, the book is aimed at graduate students in electrical engineering, computer science and mathematics.

Machine Vision Algorithms and Applications

Machine Vision Algorithms and Applications
Author: Carsten Steger
Publisher: John Wiley & Sons
Total Pages: 533
Release: 2018-03-12
Genre: Science
ISBN: 3527413650

The second edition of this successful machine vision textbook is completely updated, revised and expanded by 35% to reflect the developments of recent years in the fields of image acquisition, machine vision algorithms and applications. The new content includes, but is not limited to, a discussion of new camera and image acquisition interfaces, 3D sensors and technologies, 3D reconstruction, 3D object recognition and state-of-the-art classification algorithms. The authors retain their balanced approach with sufficient coverage of the theory and a strong focus on applications. All examples are based on the latest version of the machine vision software HALCON 13.

Digital Image Processing

Digital Image Processing
Author: Wilhelm Burger
Publisher: Springer Science & Business Media
Total Pages: 596
Release: 2012-01-19
Genre: Computers
ISBN: 9781846283796

Written as an introduction for undergraduate students, this textbook covers the most important methods in digital image processing. Formal and mathematical aspects are discussed at a fundamental level and various practical examples and exercises supplement the text. The book uses the image processing environment ImageJ, freely distributed by the National Institute of Health. A comprehensive website supports the book, and contains full source code for all examples in the book, a question and answer forum, slides for instructors, etc. Digital Image Processing in Java is the definitive textbook for computer science students studying image processing and digital processing.

Practical Java Machine Learning

Practical Java Machine Learning
Author: Mark Wickham
Publisher:
Total Pages:
Release: 2018
Genre: Java (Computer program language)
ISBN: 9781484239520

Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and must be considered throughout all phases of the project life cycle. Practical Java Machine Learning helps you understand the importance of data and how to organize it for use within your ML project. You will be introduced to tools which can help you identify and manage your data including JSON, visualization, NoSQL databases, and cloud platforms including Google Cloud Platform and Amazon Web Services. Practical Java Machine Learning includes multiple projects, with particular focus on the Android mobile platform and features such as sensors, camera, and connectivity, each of which produce data that can power unique machine learning solutions. You will learn to build a variety of applications that demonstrate the capabilities of the Google Cloud Platform machine learning API, including data visualization for Java; document classification using the Weka ML environment; audio file classification for Android using ML with spectrogram voice data; and machine learning using device sensor data. After reading this book, you will come away with case study examples and projects that you can take away as templates for re-use and exploration for your own machine learning programming projects with Java. You will: Identify, organize, and architect the data required for ML projects Deploy ML solutions in conjunction with cloud providers such as Google and Amazon Determine which algorithm is the most appropriate for a specific ML problem Implement Java ML solutions on Android mobile devices Create Java ML solutions to work with sensor data Build Java streaming based solutions.

Numerical Algorithms

Numerical Algorithms
Author: Justin Solomon
Publisher: CRC Press
Total Pages: 400
Release: 2015-06-24
Genre: Computers
ISBN: 1482251892

Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic desig

Programming Computer Vision with Python

Programming Computer Vision with Python
Author: Jan Erik Solem
Publisher: "O'Reilly Media, Inc."
Total Pages: 264
Release: 2012-06-19
Genre: Computers
ISBN: 1449341934

If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python. Programming Computer Vision with Python explains computer vision in broad terms that won’t bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what you’ve learned. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills. Learn techniques used in robot navigation, medical image analysis, and other computer vision applications Work with image mappings and transforms, such as texture warping and panorama creation Compute 3D reconstructions from several images of the same scene Organize images based on similarity or content, using clustering methods Build efficient image retrieval techniques to search for images based on visual content Use algorithms to classify image content and recognize objects Access the popular OpenCV library through a Python interface

Intelligent Machine Vision

Intelligent Machine Vision
Author: Bruce Batchelor
Publisher: Springer
Total Pages: 456
Release: 2001-08-07
Genre: Computers
ISBN:

A number of important aspects of intelligent machine vision in one volume, describing the state of the art and current developments in the field, including: fundamentals of 'intelligent'image processing for machine vision systems; algorithm optimisation; implementation in high-speed electronic digital hardware; implementation in an integrated high-level software environment and applications for industrial product quality and process control. Backed by numerous illustrations, created using the authors IP software, this book will be of interest to researchers in the field of machine vision wishing to understand the discipline and develop new techniques. Also useful for under- and postgraduates.