Histogram Equalization


Book Description

What is Histogram Equalization Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Histogram Equalization Chapter 2: Cumulative Distribution Function Chapter 3: Histogram Chapter 4: Random Variable Chapter 5: Order Statistic Chapter 6: HSL and HSV Chapter 7: Color Histogram Chapter 8: Continuous Uniform Distribution Chapter 9: Optical Resolution Chapter 10: Empirical Distribution Function (II) Answering the public top questions about histogram equalization. (III) Real world examples for the usage of histogram equalization in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Histogram Equalization.




Practical Computer Vision


Book Description

A practical guide designed to get you from basics to current state of art in computer vision systems. Key Features Master the different tasks associated with Computer Vision and develop your own Computer Vision applications with ease Leverage the power of Python, Tensorflow, Keras, and OpenCV to perform image processing, object detection, feature detection and more With real-world datasets and fully functional code, this book is your one-stop guide to understanding Computer Vision Book Description In this book, you will find several recently proposed methods in various domains of computer vision. You will start by setting up the proper Python environment to work on practical applications. This includes setting up libraries such as OpenCV, TensorFlow, and Keras using Anaconda. Using these libraries, you'll start to understand the concepts of image transformation and filtering. You will find a detailed explanation of feature detectors such as FAST and ORB; you'll use them to find similar-looking objects. With an introduction to convolutional neural nets, you will learn how to build a deep neural net using Keras and how to use it to classify the Fashion-MNIST dataset. With regard to object detection, you will learn the implementation of a simple face detector as well as the workings of complex deep-learning-based object detectors such as Faster R-CNN and SSD using TensorFlow. You'll get started with semantic segmentation using FCN models and track objects with Deep SORT. Not only this, you will also use Visual SLAM techniques such as ORB-SLAM on a standard dataset. By the end of this book, you will have a firm understanding of the different computer vision techniques and how to apply them in your applications. What you will learn Learn the basics of image manipulation with OpenCV Implement and visualize image filters such as smoothing, dilation, histogram equalization, and more Set up various libraries and platforms, such as OpenCV, Keras, and Tensorflow, in order to start using computer vision, along with appropriate datasets for each chapter, such as MSCOCO, MOT, and Fashion-MNIST Understand image transformation and downsampling with practical implementations. Explore neural networks for computer vision and convolutional neural networks using Keras Understand working on deep-learning-based object detection such as Faster-R-CNN, SSD, and more Explore deep-learning-based object tracking in action Understand Visual SLAM techniques such as ORB-SLAM Who this book is for This book is for machine learning practitioners and deep learning enthusiasts who want to understand and implement various tasks associated with Computer Vision and image processing in the most practical manner possible. Some programming experience would be beneficial while knowing Python would be an added bonus.




Digital Image Processing


Book Description

A comprehensive digital image processing book that reflects new trends in this field such as document image compression and data compression standards. The book includes a complete rewrite of image data compression, a new chapter on image analysis, and a new section on image morphology.




Biomedical Image Analysis


Book Description

Computers have become an integral part of medical imaging systems and are used for everything from data acquisition and image generation to image display and analysis. As the scope and complexity of imaging technology steadily increase, more advanced techniques are required to solve the emerging challenges. Biomedical Image Analysis demonstr




The Colour Image Processing Handbook


Book Description

1. The present state and the future of colour image processing; 2. Colour vison; 2.1 What is colous?; 2.2 The visual pathway; 2.3 Light absorption and trichromacy; 2.4 Colour appearance and opponet processes; 2.5 Other phenomena; 2.6 The uses of colour; 3. Colour science; 3.1 Introduction; 3.2 The CIE system; 3.3 Colour measurement instruments; 3.4 Uniform colour spaces and colour difference formulas; 3.5 Colour appearance modelling; 4. Colour spaces; 4.1 Basic RGB colour space; 4.2 XYZ colour spae; 4.3 Television colour spaces; 4.4 Opponent colour space; 4.5 Ohta I1I2I3 colour space; 4.6 IHS and related percentual colour spaces; 4.7 Perceptually unifor colour spaces; 4.8 Munsell colour system; 4.9 Kodak Photo YCC colour space; 4.10 Summary of colour space properties. 5. Colour video systems and signals; 5.1 Video communication; 5.2 Colour reproduction; 5.3 Encoded-colour systems; 6. Image sources; 6.1 Overview of sources for image processing; 6.2 Cameras; 7. Practical system considerations; 7.1 Image acquisition technique; 7.2 Image storage; 7.3 Colorimetric calibration of acquisition hardware; 8. Noise removal and contrast enhancement; 8.1 Noise removal; 8.2 Contrast enhancement; 9. Segmentation and edge detection; 9.1 Pixel-based segmentation; 9.2 Region-based segmentation; 9.3 Edge detection and boundary tracking; 9.4 Segmentation adn edge detection quality metrics; 10 Vector filtering; 10.1 the vector median filter; 10.2 Vector direcitonal filters; 10.3 Adaptive vector processing filters; 10.4 Application to colour images; 11. Morphological operations; 11.1 Mathematical morphology; Colour morphology; 11.3 Multiscale image analysis; 11.4 Image enhancement; 12. Frequenci domain methods; 12.1 Review of the 2D discrete Fourier transform; 12.2 Complex chromaticity; 12.3 The quaternion Fourier transform; 12.4 Disicussion; 13. Compression; 13.1 Image and video compression; 13.2 Component-wise still image compression; 13.3 Exploitation of mutual colour component dependencies; 13.4 Colour video comression; 14. Colour management for the textile industry; 14.1 Overviwe of colour flow in the textile industry; 14.2 Colour management systems; 14.3 CRT characterization; 14.4 WYSIWYG colour management; 14.5 Colour notation; 14.6 Colour quality control; 14.7 The colour talk system; 15. Colour management for the graphic arts; 15.1 Overview of the graphic arts environment; 15.2 Colour management systems overview; 15.3 Characterization and calibration of system components; 15.4 Gamut mapping; 15.5 Current colour management systems; 16 Medical imaging case study; 16.1 Wound metrics: the background and motiviation; 16.2 Principle of structured ligh; 16.3 Implementatin of the status of healing; 16.4 Assessment of the status of healing; 16.5 Automatic segmentation of the wound; 16.6 Visualization and storage of data; 17. Industrial colour inspection case studies; 17.1 Inspection of printed card; 17.2 Inspection of fast-moving beverage cans; References; Index.




Computer Vision


Book Description

Computer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos. More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles. Topics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Incorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented reality Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Includes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decade Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and software Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.




Fundamentals of Digital Image Processing


Book Description

This is an introductory to intermediate level text on the science of image processing, which employs the Matlab programming language to illustrate some of the elementary, key concepts in modern image processing and pattern recognition. The approach taken is essentially practical and the book offers a framework within which the concepts can be understood by a series of well chosen examples, exercises and computer experiments, drawing on specific examples from within science, medicine and engineering. Clearly divided into eleven distinct chapters, the book begins with a fast-start introduction to image processing to enhance the accessibility of later topics. Subsequent chapters offer increasingly advanced discussion of topics involving more challenging concepts, with the final chapter looking at the application of automated image classification (with Matlab examples) . Matlab is frequently used in the book as a tool for demonstrations, conducting experiments and for solving problems, as it is both ideally suited to this role and is widely available. Prior experience of Matlab is not required and those without access to Matlab can still benefit from the independent presentation of topics and numerous examples. Features a companion website www.wiley.com/go/solomon/fundamentals containing a Matlab fast-start primer, further exercises, examples, instructor resources and accessibility to all files corresponding to the examples and exercises within the book itself. Includes numerous examples, graded exercises and computer experiments to support both students and instructors alike.




Graphics Gems IV


Book Description

Accompanying disk contains ... "all of the code from all four volumes."--Page 4 of cover.




Image Processing in Radiology


Book Description

This book, written by leading experts from many countries, provides a comprehensive and up-to-date description of how to use 2D and 3D processing tools in clinical radiology. The opening section covers a wide range of technical aspects. In the main section, the principal clinical applications are described and discussed in depth. A third section focuses on a variety of special topics. This book will be invaluable to radiologists of any subspecialty.




Biomedical Signal and Image Processing


Book Description

All of the biomedical measurement technologies, which are now instrumental to the medical field, are essentially useless without proper signal and image processing. Biomedical Signal and Image Processing is unique in providing a comprehensive survey of all the conventional and advanced imaging modalities and the main computational methods used for processing the data obtained from each. This book offers self-contained coverage of the mathematics and biology/physiology necessary to build effective algorithms and programs for biomedical signal and image processing applications. The first part of the book details the main signal and image processing, pattern recognition, and feature extraction techniques along with computational methods from other fields such as information theory and stochastic processes. Building on this foundation, the second part explores the major one-dimensional biological signals, the biological origin and importance of each signal, and the commonly used processing techniques with an emphasis on physiology and diagnostic applications, while the third section does the same for imaging modalities. Throughout the book, the authors rely on practical examples using real data from biomedical systems. They supply several programming examples in MATLAB® to provide hands-on experience and insight Integrating all major modalities and computational techniques in a single source, Biomedical Signal and Image Processing is a perfect introduction to the field as well as an ideal reference for the established professional.