The advancement of security technologies has allowed information systems to store more crucial and sensitive data. With these advancements, organisations turn to physiological and behavioral methods of identification in order to guard against unwanted intrusion. Research Developments in Biometrics and Video Processing Techniques investigates advanced techniques in user identification and security, including retinal, facial, and finger print scans as well as signature and voice authentication models. Through its in-depth examination of computer vision applications and other biometric security technologies, this reference volume will provide researchers, engineers, developers, and students with insight into the latest research on enhanced security systems design and development.

Similar to the way in which computer vision and computer graphics act as the dual fields that connect image processing in modern computer science, the field of image processing can be considered a crucial middle road between the vision and graphics fields. Research Developments in Computer Vision and Image Processing: Methodologies and Applications brings together various research methodologies and trends in emerging areas of application of computer vision and image processing. This book is useful for students, researchers, scientists, and engineers interested in the research developments of this rapidly growing field.

Due to its inherent time-scale locality characteristics, the discrete wavelet transform (DWT) has received considerable attention in signal/image processing. Wavelet transforms have excellent energy compaction characteristics and can provide perfect reconstruction. The shifting (translation) and scaling (dilation) are unique to wavelets. Orthogonality of wavelets with respect to dilations leads to multigrid representation. As the computation of DWT involves filtering, an efficient filtering process is essential in DWT hardware implementation. In the multistage DWT, coefficients are calculated recursively, and in addition to the wavelet decomposition stage, extra space is required to store the intermediate coefficients. Hence, the overall performance depends significantly on the precision of the intermediate DWT coefficients. This work presents new implementation techniques of DWT, that are efficient in terms of computation, storage, and with better signal-to-noise ratio in the reconstructed signal.

Good quality digital images have high storage and bandwidth requirements. In modern times, with increasing user expectation for image quality, efficient compression is necessary to keep memory and transmission time within reasonable limits.

Image compression is concerned with minimization of the number of information carrying units used to represent an image. Lossy compression techniques incur some loss of information which is usually imperceptible. In return for accepting this distortion, we obtain much higher compression ratios than is possible with lossless compression.

Salient features of this book include: four new image compression algorithms and implementation of these algorithms; detailed discussion of fuzzy geometry measures and their application in image compression algorithms; new domain decomposition based algorithms using image quality measures and study of various quality measures for gray scale image compression; compression algorithms for different parallel architectures and evaluation of time complexity for encoding on all architectures; parallel implementation of image compression algorithms on a cluster in Parallel Virtual Machine (PVM) environment.

This book will be of interest to graduate students, researchers and practicing engineers looking for new image compression techniques that provide good perceived quality in digital images with higher compression ratios than is possible with conventional algorithms.

In the early phase of real-time system design only an approximate idea of the tasks and their characteristics are known. Therefore, uncertainty or impreciseness is associated with the task deadlines and processing times. It is therefore appropriate to use fuzzy numbers to model task deadlines and processing times. The book fulfills this requirement by developing several models and algorithms for fuzzy real-time scheduling using different membership function choices. It also gives task priority algorithms using a new distance measure based fuzzy criteria and considers energy efficient real-time scheduling problem with fuzzy timing constraints for the first time. This book should be of great interest to postgraduate and research students of Computer Engineering, Electronics/ Communication Engineering as well as practicing engineers using fuzzy models for designing energy efficient embedded real time systems.

In recent times, smart hand-held devices, sensor networks and pervasive computing have proliferated vastly. These devices contain embedded processors and are energy/ resource constrained. New processor technologies incorporate dynamic voltage scaling offering opportunity to optimize real-time task scheduling from the energy perspective. Often, due to uncertainties in their environment, tasks overrun their average case execution time resulting in possible overload in the system. In such systems, over provisioning done by traditional scheduling algorithms based on worst-case execution times, is undesirable as the peak load may be orders of magnitude greater than the envisaged average load. Several new overload handling techniques are discussed here that are useful with soft task deadlines. The book presents 20 new task-scheduling algorithms that take care of work overload and energy efficiency in soft real time embedded systems. This book should be of great interest to postgraduate and research students of Computer Engineering, Electronics/ Communication Engineering as well as practicing engineers designing embedded real time systems in energy/ resource constrained environments.