All ebooks in the collection "Advances in Computer Vision and Pattern Recognition" - Springer

Automatic Digital Document Processing and Management

Stefano Ferilli

This text reviews the issues involved in handling and processing digital documents. Examining the full range of a document’s lifetime, the book covers acquisition, representation, security, pre-processing, layout analysis, understanding, analysis of single components,...

Machine Learning for Vision-Based Motion Analysis

Li Cheng , Matti Pietikäinen , Liang Wang , Guoying Zhao

Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the...

Human Recognition at a Distance in Video

Bir Bhanu , Ju Han

Most biometric systems employed for human recognition require physical contact with, or close proximity to, a cooperative subject. Far more challenging is the ability to reliably recognize individuals at a distance, when viewed from an arbitrary angle under real-world...

Guide to Three Dimensional Structure and Motion Factorization

Guanghui Wang , Jonathan Wu

The problem of structure and motion recovery from image sequences is an important theme in computer vision. Considerable progress has been made in this field during the past two decades, resulting in successful applications in robot navigation, augmented reality,...

Multimedia Interaction and Intelligent User Interfaces

Minoru Etoh , Jiebo Luo , Caifeng Shan , Ling Shao

Consumer electronics (CE) devices, providing multimedia entertainment and enabling communication, have become ubiquitous in daily life. However, consumer interaction with such equipment currently requires the use of devices such as remote controls and keyboards, which...

Support Vector Machines for Pattern Classification

Shigeo Abe

A guide on the use of SVMs in pattern classification, including a rigorous performance comparison of classifiers and regressors. The book presents architectures for multiclass classification and function approximation problems, as well as evaluation criteria for...

An Introduction to Object Recognition

Marco Alexander Treiber

Rapid development of computer hardware has enabled usage of automatic object recognition in an increasing number of applications, ranging from industrial image processing to medical applications, as well as tasks triggered by the widespread use of the internet. Each...

Guide to OCR for Indic Scripts

Venu Govindaraju , Srirangaraj (Ranga) Setlur

Embedded Computer Vision

Shuvra S. Bhattacharyya , Sek Chai , Branislav Kisacanin

As a graduate student at Ohio State in the mid-1970s, I inherited a unique c- puter vision laboratory from the doctoral research of previous students. They had designed and built an early frame-grabber to deliver digitized color video from a (very large) electronic...

Digital Document Processing

Bidyut B. Chaudhuri

In this book, all the major and frontier topics in the field of document analysis are brought together into a single volume creating a unique reference source. Highlights include: - Document structure analysis followed by OCR of Japanese, Tibetan and Indian printed...

Computer Vision Beyond the Visible Spectrum

Bir Bhanu , Ioannis Pavlidis

Recently, there has been a dramatic increase in the use of sensors in the non-visible bands. As a result, there is a need for existing computer vision methods and algorithms to be adapted for use with non-visible sensors, or for the development of completely new methods...

Hexagonal Image Processing

Lee Middleton , Jayanthi Sivaswamy

The sampling lattice used to digitize continuous image data is a signi?cant determinant of the quality of the resulting digital image, and therefore, of the e?cacy of its processing.The nature of sampling lattices is intimately tied to the tessellations of the...

Support Vector Machines for Pattern Classification

Shigeo Abe

I was shocked to see a student’s report on performance comparisons between support vector machines (SVMs) and fuzzy classi?ers that we had developed withourbestendeavors.Classi?cationperformanceofourfuzzyclassi?erswas comparable, but in most cases inferior, to that of...