Machine Vision Algorithms In Java

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Machine Vision Algorithms In Java

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Author : Paul F. Whelan
language : en
Publisher: Springer Science & Business Media
Release Date : 2001

Machine Vision Algorithms In Java written by Paul F. Whelan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Computers categories.

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.

Hands On Java Deep Learning For Computer Vision

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Author : Klevis Ramo
language : en
Publisher: Packt Publishing
Release Date : 2019-02-21

Hands On Java Deep Learning For Computer Vision written by Klevis Ramo and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-21 with categories.

Leverage the power of Java and deep learning to build production-grade Computer Vision applications Key Features Build real-world Computer Vision applications using the power of neural networks Implement image classification, object detection, and face recognition Know best practices on effectively building and deploying deep learning models in Java Book 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 course 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 learn Discover neural Networks and their applications in Computer Vision Explore the popular Java frameworks and libraries for deep learning Build deep neural networks in Java Implement an end-to-end image classification application in Java Perform real-time video object detection using deep learning Enhance performance and deploy applications for production Who 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.

Programming Computer Vision With Python

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Author : Jan Solem
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2012-06-19

Programming Computer Vision With Python written by Jan Solem and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-06-19 with Computers categories.

For readers needing a basic understanding of Computer Vision's underlying theory and algorithms, this hands-on introduction is the ideal place to start. Examples written in Python are provided with modules for handling images, mathematical computing, and data mining.

Ultimate Java For Data Analytics And Machine Learning

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Author : Abhishek Kumar
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2024-08-08

Ultimate Java For Data Analytics And Machine Learning written by Abhishek Kumar and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-08 with Computers categories.

TAGLINE Empower Your Data Insights with Java's Top Tools and Frameworks. KEY FEATURES ● Explore diverse techniques and algorithms for data analytics using Java. ● Learn through hands-on examples and practical applications in each chapter. ● Master essential tools and frameworks such as JFreeChart for data visualization and Deeplearning4j for deep learning. DESCRIPTION This book is a comprehensive guide to data analysis using Java. It starts with the fundamentals, covering the purpose of data analysis, different data types and structures, and how to pre-process datasets. It then introduces popular Java libraries like WEKA and Rapidminer for efficient data analysis. The middle section of the book dives deeper into statistical techniques like descriptive analysis and random sampling, along with practical skills in working with relational databases (JDBC, SQL, MySQL) and NoSQL databases. It also explores various analysis methods like regression, classification, and clustering, along with applications in business intelligence and time series prediction. The final part of the book gives a brief overview of big data analysis with Java frameworks like MapReduce, and introduces deep learning with the Deeplearning4J library. Whether you're new to data analysis or want to improve your Java skills, this book offers a step-by-step approach with real-world examples to help you master data analysis using Java. WHAT WILL YOU LEARN ● Understand foundational principles and types of data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics. ● Master techniques for preprocessing data, including cleaning and munging, to prepare it for analysis. ● Learn how to create various charts and plots including bar charts, histograms, and scatter plots for effective data visualization. ● Explore Java-based libraries such as WEKA and Deeplearning4j for implementing machine learning algorithms. ● Develop expertise in statistical techniques including hypothesis testing, regression (linear and polynomial), and probability distributions. ● Acquire practical skills in SQL querying and JDBC for relational databases. ● Explore applications in business intelligence and deep learning, including image recognition and natural language processing. WHO IS THIS BOOK FOR? This book is ideal for IT professionals, software developers, and data scientists interested in using Java for data analytics. It is also suitable for students and researchers seeking practical insights into Java-based data analysis. Readers should have a basic understanding of Java programming and fundamental concepts in data analysis. TABLE OF CONTENTS 1. Data Analytics Using Java 2. Datasets 3. Data Visualization 4. Java Machine Learning Libraries 5. Statistical Analysis 6. Relational Databases 7. Regression Analysis 8. Classification Analysis 9. Sentiment Analysis 10. Cluster Analysis 11. Working with NoSQL Databases 12. Recommender Systems 13. Applications of Data Analysis 14. Big Data Analysis with Java 15. Deep Learning with Java Index

Machine Vision Algorithms And Applications

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Author : Carsten Steger
language : en
Publisher: John Wiley & Sons
Release Date : 2018-03-12

Machine Vision Algorithms And Applications written by Carsten Steger and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-12 with Science categories.

Die zweite Auflage dieses erfolgreichen Lehrbuchs zum maschinellen Sehen ist vollständig aktualisiert, überarbeitet und erweitert, um die Entwicklungen der vergangenen Jahre auf den Gebieten der Bilderfassung, Algorithmen des maschinellen Sehens und dessen Anwendungen zu berücksichtigen. Hinzugekommen sind insbesondere neue Kameratechniken und Schnittstellen, 3D-Sensorik und -technologie, 3D-Objekterkennung und 3D-Bildrekonstruktion. Die Autoren folgen weiterhin dem Ansatz "soviel Theorie wie nötig, soviel Anwendungsbezug wie möglich". Alle Beispiele basieren auf der aktuellen Version der Software HALCON, von der nach Registrierung auf der Autorenwebseite eine Testversion erhältlich ist.

Digital Image Processing

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Author : Wilhelm Burger
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-01-19

Digital Image Processing written by Wilhelm Burger and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-01-19 with Computers categories.

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 Machine Learning

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Author : Ally S. Nyamawe
language : en
Publisher: CRC Press
Release Date : 2025-02-07

Practical Machine Learning written by Ally S. Nyamawe and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-07 with Computers categories.

The book provides an accessible, comprehensive introduction for beginners to machine learning, equipping them with the fundamental skills and techniques essential for this field. It enables beginners to construct practical, real-world solutions powered by machine learning across diverse application domains. It demonstrates the fundamental techniques involved in data collection, integration, cleansing, transformation, development, and deployment of machine learning models. This book emphasizes the importance of integrating responsible and explainable AI into machine learning models, ensuring these principles are prioritized rather than treated as an afterthought. To support learning, this book also offers information on accessing additional machine learning resources such as datasets, libraries, pre-trained models, and tools for tracking machine learning models. This is a core resource for students and instructors of machine learning and data science looking for a beginner-friendly material which offers real-world applications and takes ethical discussions into account. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.

Machine Learning For Data Streams

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Author : Albert Bifet
language : en
Publisher: MIT Press
Release Date : 2018-03-16

Machine Learning For Data Streams written by Albert Bifet and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-16 with Computers categories.

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.

Machine Learning Paradigms

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Author : George A. Tsihrintzis
language : en
Publisher: Springer
Release Date : 2019-07-06

Machine Learning Paradigms written by George A. Tsihrintzis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-06 with Computers categories.

This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation. To maximize the dissemination of research findings, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. This book is intended for professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter allows readers to probe further into those application areas that interest them most.

Research Anthology On Machine Learning Techniques Methods And Applications

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Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2022-05-13

Research Anthology On Machine Learning Techniques Methods And Applications written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-13 with Computers categories.

Machine learning continues to have myriad applications across industries and fields. To ensure this technology is utilized appropriately and to its full potential, organizations must better understand exactly how and where it can be adapted. Further study on the applications of machine learning is required to discover its best practices, challenges, and strategies. The Research Anthology on Machine Learning Techniques, Methods, and Applications provides a thorough consideration of the innovative and emerging research within the area of machine learning. The book discusses how the technology has been used in the past as well as potential ways it can be used in the future to ensure industries continue to develop and grow. Covering a range of topics such as artificial intelligence, deep learning, cybersecurity, and robotics, this major reference work is ideal for computer scientists, managers, researchers, scholars, practitioners, academicians, instructors, and students.