Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

Digital Image Processing

Newspapers and the popular scientific press today publish many examples of highly impressive images. These images range, for example, from those showing regions of star birth in the distant Universe to the extent of the stratospheric ozone depletion over Antarctica in springtime, and to those regions of the human brain affected by Alzheimer’s disease. Processed digitally to generate spectacular images, often in false colour, they all make an immediate and deep impact on the viewer’s imagination and understanding. Professor Jonathan Blackledge’s erudite but very useful new treatise Digital Image Processing: Mathematical and Computational Methods explains both the underlying theory and the techniques used to produce such images in considerable detail. It also provides many valuable example problems - and their solutions - so that the reader can test his/her grasp of the physical, mathematical and numerical aspects of the particular topics and methods discussed. As such, this magnum opus complements the author’s earlier work Digital Signal Processing. Both books are a wonderful resource for students who wish to make their careers in this fascinating and rapidly developing field which has an ever increasing number of areas of application. The strengths of this large book lie in: • excellent explanatory introduction to the subject; • thorough treatment of the theoretical foundations, dealing with both electromagnetic and acoustic wave scattering and allied techniques; • comprehensive discussion of all the basic principles, the mathematical transforms (e.g. the Fourier and Radon transforms), their interrelationships and, in particular, Born scattering theory and its application to imaging systems modelling; discussion in detail - including the assumptions and limitations - of optical imaging, seismic imaging, medical imaging (using ultrasound), X-ray computer aided tomography, tomography when the wavelength of the probing radiation is of the same order as the dimensions of the scatterer, Synthetic Aperture Radar (airborne or spaceborne), digital watermarking and holography; detail devoted to the methods of implementation of the analytical schemes in various case studies and also as numerical packages (especially in C/C++); • coverage of deconvolution, de-blurring (or sharpening) an image, maximum entropy techniques, Bayesian estimators, techniques for enhancing the dynamic range of an image, methods of filtering images and techniques for noise reduction; • discussion of thresholding, techniques for detecting edges in an image and for contrast stretching, stochastic scattering (random walk models) and models for characterizing an image statistically; • investigation of fractal images, fractal dimension segmentation, image texture, the coding and storing of large quantities of data, and image compression such as JPEG; • valuable summary of the important results obtained in each Chapter given at its end; • suggestions for further reading at the end of each Chapter. I warmly commend this text to all readers, and trust that they will find it to be invaluable. Professor Michael J Rycroft Visiting Professor at the International Space University, Strasbourg, France, and at Cranfield University, England

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Conjoint probabilistic subband modeling

Thesis (Ph. D.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1997.Includes bibliographical references (leaves 125-133).by Ashok Chhabedia Popat.Ph.D

Colour and texture image analysis in a Local Binary Pattern framework

In this Thesis we use colour and Local Binary Pattern based texture analysis for image classification and reconstruction. In complementary work we offer a new texture description called the Sudoku transform, an extension of the Local Binary Pattern. Our new method when used to classify members of benchmark datasets shows a performance increment over traditional methods including the Local Binary Pattern. Finally we consider the invertibility of texture descriptions and show how with our new method - Quadratic Reconstruction - that a highly accurate image can be recovered purely from its textural information

Supervised and unsupervised segmentation of textured images by efficient multi-level pattern classification

This thesis proposes new, efficient methodologies for supervised and unsupervised image segmentation based on texture information. For the supervised case, a technique for pixel classification based on a multi-level strategy that iteratively refines the resulting segmentation is proposed. This strategy utilizes pattern recognition methods based on prototypes (determined by clustering algorithms) and support vector machines. In order to obtain the best performance, an algorithm for automatic parameter selection and methods to reduce the computational cost associated with the segmentation process are also included. For the unsupervised case, the previous methodology is adapted by means of an initial pattern discovery stage, which allows transforming the original unsupervised problem into a supervised one. Several sets of experiments considering a wide variety of images are carried out in order to validate the developed techniques.Esta tesis propone metodologías nuevas y eficientes para segmentar imágenes a partir de información de textura en entornos supervisados y no supervisados. Para el caso supervisado, se propone una técnica basada en una estrategia de clasificación de píxeles multinivel que refina la segmentación resultante de forma iterativa. Dicha estrategia utiliza métodos de reconocimiento de patrones basados en prototipos (determinados mediante algoritmos de agrupamiento) y máquinas de vectores de soporte. Con el objetivo de obtener el mejor rendimiento, se incluyen además un algoritmo para selección automática de parámetros y métodos para reducir el coste computacional asociado al proceso de segmentación. Para el caso no supervisado, se propone una adaptación de la metodología anterior mediante una etapa inicial de descubrimiento de patrones que permite transformar el problema no supervisado en supervisado. Las técnicas desarrolladas en esta tesis se validan mediante diversos experimentos considerando una gran variedad de imágenes

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Algorithms and Circuits for Analog-Digital Hybrid Multibeam Arrays

Fifth generation (5G) and beyond wireless communication systems will rely heavily on larger antenna arrays combined with beamforming to mitigate the high free-space path-loss that prevails in millimeter-wave (mmW) and above frequencies. Sharp beams that can support wide bandwidths are desired both at the transmitter and the receiver to leverage the glut of bandwidth available at these frequency bands. Further, multiple simultaneous sharp beams are imperative for such systems to exploit mmW/sub-THz wireless channels using multiple reflected paths simultaneously. Therefore, multibeam antenna arrays that can support wider bandwidths are a key enabler for 5G and beyond systems. In general, N-beam systems using N-element antenna arrays will involve circuit complexities of the order of N2. This dissertation investigates new analog, digital and hybrid low complexity multibeam beamforming algorithms and circuits for reducing the associated high size, weight, and power (SWaP) complexities in larger multibeam arrays. The research efforts on the digital beamforming aspect propose the use of a new class of discrete Fourier transform (DFT) approximations for multibeam generation to eliminate the need for digital multipliers in the beamforming circuitry. For this, 8-, 16- and 32-beam multiplierless multibeam algorithms have been proposed for uniform linear array applications. A 2.4 GHz 16-element array receiver setup and a 5.8 GHz 32-element array receiver system which use field programmable gate arrays (FPGAs) as digital backend have been built for real-time experimental verification of the digital multiplierless algorithms. The multiplierless algorithms have been experimentally verified by digitally measuring beams. It has been shown that the measured beams from the multiplierless algorithms are in good agreement with the exact counterpart algorithms. Analog realizations of the proposed approximate DFT transforms have also been investigated leading to low-complex, high bandwidth circuits in CMOS. Further, a novel approach for reducing the circuit complexity of analog true-time delay (TTD) N-beam beamforming networks using N-element arrays has been proposed for wideband squint-free operation. A sparse factorization of the N-beam delay Vandermonde beamforming matrix is used to reduce the total amount of TTD elements that are needed for obtaining N number of beams in a wideband array. The method has been verified using measured responses of CMOS all-pass filters (APFs). The wideband squint-free multibeam algorithm is also used to propose a new low-complexity hybrid beamforming architecture targeting future 5G mmW systems. Apart from that, the dissertation also explores multibeam beamforming architectures for uniform circular arrays (UCAs). An algorithm having N log N circuit complexity for simultaneous generation of N-beams in an N-element UCA is explored and verified