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Motion-Free Super-Resolution
With the explosion of Internet technology and graphics engines, digital images are now everywhere. Image capturing tools are all pervading - in our pockets to inside a satellite. And although imaging applications demand an availability to high resolution images, such images are not picture perfect and may be lacking sufficient details. This requires that these images be super-resolved for improved details. How to achieve this is what constitutes research in the area of image super-resolution. Motion-Free Super-Resolution explores new technology for image super-resolution - applying cues other than the motion cue in super-resolving a scene. This book will serve as an essential reference for both academecians and practicing engineers in the area of image processing and computer vision, as well as providing a basis for ongoing research in this field.
Image Processing : Dealing with Texture ; 2nd ed.
Updates the classic work on texture analysis theory and methods without abandoning the foundational essentials of this landmark work. Like the first, the new edition offers an analysis of texture in digital images that are essential to a diverse range of applications such as: robotics, defense, medicine and the geo-sciences. Designed to easily locate information on specific problems, the text is structured around a series of helpful questions and answers. Updated to include the most recent developments in the field, many chapters have been completely revised including: Fractals and Multifractals, Image Statistics, Texture Repair, Local Phase Features, Dual Tree Complex Wavelet Transform, Ridgelets and Curvelets and Deep Texture Features. The book takes a two-level mathematical approach: light math is covered in the main level of the book, with harder math identified in separate boxes.
Hyperspectral Data Compression
HYPERSPECTRAL DATA COMPRESSION presents the most recent results in the field of compression of remote sensing 3D data, with a focus on multispectral and hyperspectral imagery. This book is essential for researchers working across related fields including: multi-dimensional data compression, multispectral and hyperspectral data archives, remote sensing, scientific image processing, military and aerospace image processing, image segmentation, image classification, and target detection.
Handbook of big data analytics ; Vol.2 : Applications in ICT, security and business analytics
Big Data analytics is the complex process of examining big data to uncover information such as correlations, hidden patterns, trends and user and customer preferences, to allow organizations and businesses to make more informed decisions. These methods and technologies have become ubiquitous in all fields of science, engineering, business and management due to the rise of data-driven models as well as data engineering developments using parallel and distributed computational analytics frameworks, data and algorithm parallelization, and GPGPU programming. However, there remain potential issues that need to be addressed to enable big data processing and analytics in real time.
Fundamentals of image data mining : Analysis, features, classification and retrieval
Presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. The coverage spans all aspects of image analysis and understanding, offering deep insights into areas of feature extraction, machine learning, and image retrieval. The theoretical coverage is supported by practical mathematical models and algorithms, utilizing data from real-world examples and experiments. Topics and features: Describes essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transforms / Develops many new exercises (most with MATLAB code and instructions) / Includes review summaries at the end of each chapter / Analyses state-of-the-art models, algorithms, and procedures for image mining / Integrates new sections on pre-processing, discrete cosine transform, and statistical inference and testing / Demonstrates how features like color, texture, and shape can be mined or extracted for image representation / Applies powerful classification approaches: Bayesian classification, support vector machines, neural / networks, and decision trees / Implements imaging techniques for indexing, ranking, and presentation, as well as database visualization
Embedded image processing on the TMS320C6000™ DSP : Examples in code composer studio™ and MATLAB
the author also explains the uses and rationale behind a plethora of technologies, most notably several industry-standard and essential TI developer technologies, including the Code Composer Studio™ IDE. Highlights include numerous debugged MATLAB and C/C++ Visual Studio prototype applications and efficient C implementations of real-world algorithms tested on both the C6416 DSK and C6701 EVM development platforms
Data science in theory and practice : Techniques for big data analytics and complex data sets
Delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. Readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets
Comprehensive mathematics for computer scientists 2 : Calculus and ODEs, splines, probability, fourier and wavelet theory, fractals and neural networks, categories and lambda calculus
This second volume of a comprehensive tour through mathematical core subjects for computer scientists completes the first volume in two - gards: Part III first adds topology, di?erential, and integral calculus to the t- ics of sets, graphs, algebra, formal logic, machines, and linear geometry, of volume 1. With this spectrum of fundamentals in mathematical e- cation, young professionals should be able to successfully attack more involved subjects, which may be relevant to the computational sciences. In a second regard, the end of part III and part IV add a selection of more advanced topics. In view of the overwhelming variety of mathematical approaches in the computational sciences, any selection, even the most empirical, requires a methodological justi?cation. Our primary criterion has been the search for harmonization and optimization of thematic - versity and logical coherence. This is why we have, for instance, bundled such seemingly distant subjects as recursive constructions, ordinary d- ferential equations, and fractals under the unifying perspective of c- traction theory.
Comprehensive mathematics for computer scientists 1 : Sets and numbers, graphs and algebra, logic and machines, linear geometry
This two-volume textbook Comprehensive Mathematics for Computer Scientists is a self-contained comprehensive presentation of mathematics including sets, numbers, graphs, algebra, logic, grammars, machines, linear geometry, calculus, ODEs, and special themes such as neural networks, Fourier theory, wavelets, numerical issues, statistics, categories, and manifolds. The concept framework is streamlined but defining and proving virtually everything.
Bézier and Splines in image processing and machine vision
Digital image processing and machine vision have grown considerably during the last few decades. Of the various techniques, developed so far splines play a positive and significant role in many of them. This book deals with various image processing and machine vision problems efficiently with splines.
