Architecture-Aware Optimization Strategies in Real-time Image Processing


Book Description

In the field of image processing, many applications require real-time execution, particularly those in the domains of medicine, robotics and transmission, to name but a few. Recent technological developments have allowed for the integration of more complex algorithms with large data volume into embedded systems, in turn producing a series of new sophisticated electronic architectures at affordable prices. This book performs an in-depth survey on this topic. It is primarily written for those who are familiar with the basics of image processing and want to implement the target processing design using different electronic platforms for computing acceleration. The authors present techniques and approaches, step by step, through illustrative examples. This book is also suitable for electronics/embedded systems engineers who want to consider image processing applications as sufficient imaging algorithm details are given to facilitate their understanding.




Architecture-Aware Optimization Strategies in Real-time Image Processing


Book Description

In the field of image processing, many applications require real-time execution, particularly those in the domains of medicine, robotics and transmission, to name but a few. Recent technological developments have allowed for the integration of more complex algorithms with large data volume into embedded systems, in turn producing a series of new sophisticated electronic architectures at affordable prices. This book performs an in-depth survey on this topic. It is primarily written for those who are familiar with the basics of image processing and want to implement the target processing design using different electronic platforms for computing acceleration. The authors present techniques and approaches, step by step, through illustrative examples. This book is also suitable for electronics/embedded systems engineers who want to consider image processing applications as sufficient imaging algorithm details are given to facilitate their understanding.




Topographical Tools for Filtering and Segmentation 2


Book Description

Mathematical morphology has developed a powerful methodology for segmenting images, based on connected filters and watersheds. We have chosen the abstract framework of node- or edge-weighted graphs for an extensive mathematical and algorithmic description of these tools. Volume 2 proposes two physical models for describing valid flooding on a node- or edge-weighted graph, and establishes how to pass from one to another. Many new flooding algorithms are derived, allowing parallel and local flooding of graphs. Watersheds and flooding are then combined for solving real problems. Their ability to model a real hydrographic basin represented by its digital elevation model constitutes a good validity check of the underlying physical models. The last part of Volume 2 explains why so many different watershed partitions exist for the same graph. Marker-based segmentation is the method of choice for curbing this proliferation. This book proposes new algorithms combining the advantages of the previous methods which treated node- and edge-weighted graphs differently.




Topographical Tools for Filtering and Segmentation 1


Book Description

Mathematical morphology has developed a powerful methodology for segmenting images, based on connected filters and watersheds. We have chosen the abstract framework of node- or edge-weighted graphs for an extensive mathematical and algorithmic description of these tools. Volume 1 is devoted to watersheds. The topography of a graph appears by observing the evolution of a drop of water moving from node to node on a weighted graph, along flowing paths, until it reaches regional minima. The upstream nodes of a regional minimum constitute its catchment zone. The catchment zones may be constructed independently of each other and locally, in contrast with the traditional approach where the catchment basins have to be constructed all at the same time. Catchment zones may overlap, and thus, a new segmentation paradigm is proposed in which catchment zones cover each other according to a priority order. The resulting partition may then be corrected, by local and parallel treatments, in order to achieve the desired precision.




From Algebraic Structures to Tensors


Book Description

Nowadays, tensors play a central role for the representation, mining, analysis, and fusion of multidimensional, multimodal, and heterogeneous big data in numerous fields. This set on Matrices and Tensors in Signal Processing aims at giving a self-contained and comprehensive presentation of various concepts and methods, starting from fundamental algebraic structures to advanced tensor-based applications, including recently developed tensor models and efficient algorithms for dimensionality reduction and parameter estimation. Although its title suggests an orientation towards signal processing, the results presented in this set will also be of use to readers interested in other disciplines. This first book provides an introduction to matrices and tensors of higher-order based on the structures of vector space and tensor space. Some standard algebraic structures are first described, with a focus on the hilbertian approach for signal representation, and function approximation based on Fourier series and orthogonal polynomial series. Matrices and hypermatrices associated with linear, bilinear and multilinear maps are more particularly studied. Some basic results are presented for block matrices. The notions of decomposition, rank, eigenvalue, singular value, and unfolding of a tensor are introduced, by emphasizing similarities and differences between matrices and tensors of higher-order.




Real-time Imaging


Book Description




Architectures and Compilation Techniques for Fine and Medium Grain Parallelism


Book Description

Medium and especially fine grain parallelism has been a focus of the data-research area since its inception in the 1970's. Much experience has been gained but the interest both in the academia and the industry continues to flourish.The development of multiple ALU superscalars/superpipelined machines/VLIWs (small resources Von Neumann machines) has meant the related software and hardware topics for finding higher degrees of fine and medium grain parallelism on such machines has become increasingly important.This volume presents new parallelization ideas being discovered in this relatively unchartered research area, including some which are likely to have immediate practical impact. With invited papers from prominent specialists, it is hoped it also offers a critical review of the accomplishments of the data-flow research area to date.







TinyML


Book Description

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size




Scientific Visualization


Book Description

Numerical simulations of global warming, Mars observation data, and aircraft design are but a few of the topics where the use of human visual perception for data understanding are considered essential. Ten years agoa handful of pioneers professed the value of visualization to skeptical audiences. Today, with supercomputers and sensors producing ever-increasing amounts of data, scientific visualization is accepted throughout much of science and engineering as the fundamental tool for data analysis. Written by a world-wide panel of visualization experts, Scientific Visualization: Advances and Challenges presents astute coverage of prevailing trends, issues, and practice of scientific visualization. From algorithmic topics such as volume graphics and the modeling and visualization of large data sets, to foundations, perception, and interface technology (including virtual reality), this book provides the latest advances in the area. The book demonstrates new techniques, examines diverse application areas, and discusses current limitations and upcoming requirements. Scientific Visualization:Advances and Challenges $> presents readers with a unique opportunity to examine expert thinking and current practice, and to obtain a vision of potential future directions. It will be essential reading for scientific and engineering practitioners and visualization researchers alike. Offers extremely topical and timely coverage of a rapidly evolving area Includes contributions from an international panel of visualization experts in one accessible volume Provides scientific and engineering practitioners as well as visualization researchers with an essential guide to the literature