Large jacket version



In the classic 'cookbook' style of the original, this new edition guides researchers and practitioners through techniques for the digital manipulation and analysis of images, from the simplest steps to advanced functions. Drawing on their long experience as users and developers of image analysis algorithms and software, the authors present a practical description and implementation of the most suitable procedures. Each section treats a single operation, describing typical situations that use the operation, and discusses the algorithm and implementation. Sections start with a 'before' and 'after' pictorial example and a reference listing typical applications, keywords, and related procedures. This new edition includes extra sections on Gabor filtering and threshholding by connectivity, an expanded program listing, and suggested classroom projects. The accompanying CD-ROM features C programs not only as source code for carrying out the procedures, but also as executables with a graphical user interface for Windows and Linux.





Pattern recognition is a branch of artificial intelligence concerned with the classification or description of observations. It aims to classify data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space. A complete pattern recognition system consists of a sensor that gathers the observations to be classified or described; a feature extraction mechanism that computes numeric or symbolic information from the observations; and a classification or description scheme that does the actual job of classifying or describing observations, relying on the extracted features. The research activities of ALISR consist in developing advanced methodologies for solving real challenging problems related to this field. The related application domains are those, which require any automatic image/signal processing and/or pattern recognition methodology, in particular:


A.   Biometric security systems

  • Person recognition using for example finger knuckle, palmprint, ECG and PCG signals


B.  Biomedical signal analysis and classification

  • Electrocardiogram (ECG) and Phonocardiogram (PCG) signal processing for cardiac pathology detection
  • Electroencephalogram (EEG) signal processing for brain abnormality detection (e.g., Seizures, Epilepsy)
  • Microarray image analysis for gene expression


D.  Spectrophotometry

  • Automatic food quality analysis



Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on that data. 

Computer vision is everywhere-in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It stitches Google maps and Google Earth together, checks the pixels on LCD screens, and makes sure the stitches in your shirt are sewn properly. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time. 

Learning OpenCV will teach any developer or hobbyist to use the framework quickly with the help of hands-on exercises in each chapter. This book includes:

  • A thorough introduction to OpenCV
  • Getting input from cameras
  • Transforming images
  • Segmenting images and shape matching
  • Pattern recognition, including face detection
  • Tracking and motion in 2 and 3 dimensions
  • 3D reconstruction from stereo vision
  • Machine learning algorithms

Getting machines to see is a challenging but entertaining goal. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book you need to get started.

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Book Description:

This book brings together the analytic aspects of image processing with the practicalities of applying the techniques in an industrial setting. It is excellent grounding for a machine vision researcher.
John Billingsley, University of Southern Queensland

The book in its previous incarnations has established its place as a unique repository of detailed analysis of important image processing and computer vision algorithms.
Farzin Deravi, University of 

This book is an essential reference for anyone developing techniques for machine vision analysis, including systems for industrial inspection, biomedical analysis, and much more.
Majid Mirmehdi, University of 

The book contains a large number of experimental design and evaluation procedures that are of keen interest to industrial application engineers of machine vision.
William Wee, University of Cincinnati

Author E.R. Davies covers essential elements of the theory while addressing algorithmic and practical design constraints.
Mechanical Engineering, August 2006



Field-Programmable Gate Arrays (FPGAs) are revolutionizing digital signal processing as novel FPGA families are replacing ASICs and PDSPs for front-end digital signal processing algorithms. So the efficient implementation of these algorithms is critical and is the main goal of this book. It starts with an overview of today's FPGA technology, devices, and tools for designing state-of-the-art DSP systems. A case study in the first chapter is the basis for more than 30 design examples throughout. The following chapters deal with computer arithmetic concepts, theory and the implementation of FIR and IIR filters, multirate digital signal processing systems, DFT and FFT algorithms, and advanced algorithms with high future potential. Each chapter contains exercises. The VERILOG source code and a glossary are given in the appendices, while the accompanying CD-ROM contains the examples in VHDL and Verilog code as well as the newest Altera.

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