Genomic signal processing (GSP) can be defined as the analysis, processing, and use of genomic signals to gain biological knowledge, and the translation of that knowledge into systems-based applications that can be used to diagnose and treat genetic diseases. Situated at the crossroads of engineering, biology, mathematics, statistics, and computer science, GSP requires the development of both nonlinear dynamical models that adequately represent genomic regulation, and diagnostic and therapeutic tools based on these models. This book facilitates these developments by providing rigorous...
Genomic signal processing (GSP) can be defined as the analysis, processing, and use of genomic signals to gain biological knowledge, and the transl...
-This book gives readers an intuitive appreciation for random functions, plus theory and processes necessary for sophisticated applications. It covers probability theory, random processes, canonical representation, optimal filtering, and random models. Second in the SPIE/IEEE Series on Imaging Science & Engineering.
It also presents theory along with applications, to help readers intuitively appreciate random functions.
Included are special cases in which probabilistic insight is more readily achievable. When provided, proofs are in the main body of the text and...
-This book gives readers an intuitive appreciation for random functions, plus theory and processes necessary for sophisticated applications. It covers...
-This text covers key mathematical principles and algorithms for nonlinear filters used in image processing. Readers will gain an in-depth understanding of the underlying mathematical and filter design methodologies needed to construct and use nonlinear filters in a variety of applications.
The 11 chapters explore topics of contemporary interest as well as fundamentals drawn from nonlinear filtering's historical roots in mathematical morphology and digital signal processing. This book examines various filter options and the types of applications for which they are best suited....
-This text covers key mathematical principles and algorithms for nonlinear filters used in image processing. Readers will gain an in-depth understandi...
Edward R. Daugherty Edward R. Dougherty Phillip Laplante
-An invaluable source for both imaging and software engineers, this practical guide thoroughly covers information in real-time systems, imaging, optimization, algorithms and hardware for image processing. You'll gain a comprehensive knowledge of the structure, computation, and application of the fundamental algorithms necessary to get the most out of your imaging technology.
Topics covered include:
Basic hardware architecture
Linear and non-linear image processing algorithms
Efficient algorithms
Optimization techniques
Programming...
-An invaluable source for both imaging and software engineers, this practical guide thoroughly covers information in real-time systems, imaging, optim...
Presents the statistical analysis of morphological filters and their automatic optical design, the development of morphological features for image signatures, and the design of efficient morphological algorithms. Extends the morphological paradigm to include other branches of science and mathematics.;This book is designed to be of interest to optical, electrical and electronics, and electro-optic engineers, including image processing, signal processing, machine vision, and computer vision engineers, applied mathematicians, image analysts and scientists and graduate-level students in image...
Presents the statistical analysis of morphological filters and their automatic optical design, the development of morphological features for image sig...
This unique reference presents in-depth coverage of the latest methods and applications of digital image processing describing various computer architectures ideal for satisfying specific image processing demands.
This unique reference presents in-depth coverage of the latest methods and applications of digital image processing describing various computer archit...
Studying large sets of genes and their collective function requires tools that can easily handle huge amounts of information. Recent research indicates that engineering approaches for prediction, signal processing, and control are well suited for studying multivariate interactions. A tutorial guide to the current engineering research in genomics, Introduction to Genomic Signal Processing with Control provides a state-of-the-art account of the use of control theory to obtain intervention strategies for gene regulatory networks. The book builds up the necessary molecular biology background...
Studying large sets of genes and their collective function requires tools that can easily handle huge amounts of information. Recent research indicate...
Mathematical Nonlinear Image Processing deals with a fast growing research area. The development of the subject springs from two factors: (1) the great expansion of nonlinear methods applied to problems in imaging and vision, and (2) the degree to which nonlinear approaches are both using and fostering new developments in diverse areas of mathematics. Mathematical Nonlinear Image Processing will be of interest to people working in the areas of applied mathematics as well as researchers in computer vision. Mathematical Nonlinear Image Processing is an edited volume...
Mathematical Nonlinear Image Processing deals with a fast growing research area. The development of the subject springs from two factors: (1)...
This book is the first of its kind to discuss error estimation with a model-based approach. From the basics of classifiers and error estimators to distributional and Bayesian theory, it covers important topics and essential issues pertaining to the scientific validity of pattern classification.
Error Estimation for Pattern Recognition focuses on error estimation, which is a broad and poorly understood topic that reaches all research areas using pattern classification. It includes model-based approaches and discussions of newer error estimators such as bolstered and Bayesian...
This book is the first of its kind to discuss error estimation with a model-based approach. From the basics of classifiers and error estimators to ...
