Architecture-Aware Optimization Strategies in Real-time Image Processing
Architecture-Aware Optimization Strategies in Real-time Image Processing

Author: Chao Li

Publisher: John Wiley & Sons

Published: 2017-11-02

Total Pages: 186

ISBN-13: 1119467128

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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
Topographical Tools for Filtering and Segmentation 2

Author: Fernand Meyer

Publisher: John Wiley & Sons

Published: 2019-01-23

Total Pages: 259

ISBN-13: 1119575125

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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
Topographical Tools for Filtering and Segmentation 1

Author: Fernand Meyer

Publisher: John Wiley & Sons

Published: 2019-01-23

Total Pages: 271

ISBN-13: 1119579546

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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
From Algebraic Structures to Tensors

Author: Gérard Favier

Publisher: John Wiley & Sons

Published: 2020-01-02

Total Pages: 324

ISBN-13: 1786301547

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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.

Matrix and Tensor Decompositions in Signal Processing, Volume 2
Matrix and Tensor Decompositions in Signal Processing, Volume 2

Author: Gérard Favier

Publisher: John Wiley & Sons

Published: 2021-08-17

Total Pages: 386

ISBN-13: 1119700965

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The second volume will deal with a presentation of the main matrix and tensor decompositions and their properties of uniqueness, as well as very useful tensor networks for the analysis of massive data. Parametric estimation algorithms will be presented for the identification of the main tensor decompositions. After a brief historical review of the compressed sampling methods, an overview of the main methods of retrieving matrices and tensors with missing data will be performed under the low rank hypothesis. Illustrative examples will be provided.

Applied Reconfigurable Computing
Applied Reconfigurable Computing

Author: Stephan Wong

Publisher: Springer

Published: 2017-03-30

Total Pages: 338

ISBN-13: 3319562584

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This book constitutes the refereed proceedings of the 13th International Symposium on Applied Reconfigurable Computing, ARC 2017, held in Delft, The Netherlands, in April 2017. The 17 full papers and 11 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They are organized in topical sections on adaptive architectures, embedded computing and security, simulation and synthesis, design space exploration, fault tolerance, FGPA-based designs, neural neworks, and languages and estimation techniques.

Architectures and Compilation Techniques for Fine and Medium Grain Parallelism
Architectures and Compilation Techniques for Fine and Medium Grain Parallelism

Author: Michel Cosnard

Publisher:

Published: 1993

Total Pages: 354

ISBN-13:

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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
TinyML

Author: Pete Warden

Publisher: O'Reilly Media

Published: 2019-12-16

Total Pages: 504

ISBN-13: 1492052019

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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