Download or read book Optimisation in Signal and Image Processing written by Patrick Siarry and published by John Wiley & Sons. This book was released on 2013-03-01 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the optimization methods most commonly encountered in signal and image processing: artificial evolution and Parisian approach; wavelets and fractals; information criteria; training and quadratic programming; Bayesian formalism; probabilistic modeling; Markovian approach; hidden Markov models; and metaheuristics (genetic algorithms, ant colony algorithms, cross-entropy, particle swarm optimization, estimation of distribution algorithms, and artificial immune systems).

Download or read book Architecture Aware Optimization Strategies in Real time Image Processing written by Chao Li and published by John Wiley & Sons. This book was released on 2017-11-29 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Optimisation in Signal and Image Processing written by Patrick Siarry and published by Wiley-ISTE. This book was released on 2009-10-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the optimization methods most commonly encountered in signal and image processing: artificial evolution and Parisian approach; wavelets and fractals; information criteria; training and quadratic programming; Bayesian formalism; probabilistic modeling; Markovian approach; hidden Markov models; and metaheuristics (genetic algorithms, ant colony algorithms, cross-entropy, particle swarm optimization, estimation of distribution algorithms, and artificial immune systems).

Download or read book Optimisation in Signal and Image Processing written by Patrick Siarry and published by Wiley-ISTE. This book was released on 2009-10-12 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the optimization methods most commonly encountered in signal and image processing: artificial evolution and Parisian approach; wavelets and fractals; information criteria; training and quadratic programming; Bayesian formalism; probabilistic modeling; Markovian approach; hidden Markov models; and metaheuristics (genetic algorithms, ant colony algorithms, cross-entropy, particle swarm optimization, estimation of distribution algorithms, and artificial immune systems).

Download or read book Convex Optimization in Signal Processing and Communications written by Daniel P. Palomar and published by Cambridge University Press. This book was released on 2010 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leading experts provide the theoretical underpinnings of the subject plus tutorials on a wide range of applications, from automatic code generation to robust broadband beamforming. Emphasis on cutting-edge research and formulating problems in convex form make this an ideal textbook for advanced graduate courses and a useful self-study guide.

Download or read book Optimization Techniques in Computer Vision written by Mongi A. Abidi and published by Springer. This book was released on 2016-12-06 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems. Unconstrained optimization gives the best solution based on numerical minimization of a single, scalar-valued objective function or cost function. Unconstrained optimization problems have been intensively studied, and many algorithms and tools have been developed to solve them. Most practical optimization problems, however, arise with a set of constraints. Typical examples of constraints include: (i) pre-specified pixel intensity range, (ii) smoothness or correlation with neighboring information, (iii) existence on a certain contour of lines or curves, and (iv) given statistical or spectral characteristics of the solution. Regularized optimization is a special method used to solve a class of constrained optimization problems. The term regularization refers to the transformation of an objective function with constraints into a different objective function, automatically reflecting constraints in the unconstrained minimization process. Because of its simplicity and efficiency, regularized optimization has many application areas, such as image restoration, image reconstruction, optical flow estimation, etc. Optimization plays a major role in a wide variety of theories for image processing and computer vision. Various optimization techniques are used at different levels for these problems, and this volume summarizes and explains these techniques as applied to image processing and computer vision.

Download or read book Advances and Applications of Optimised Algorithms in Image Processing written by Diego Oliva and published by Springer. This book was released on 2016-11-21 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a study of the use of optimization algorithms in complex image processing problems. The problems selected explore areas ranging from the theory of image segmentation to the detection of complex objects in medical images. Furthermore, the concepts of machine learning and optimization are analyzed to provide an overview of the application of these tools in image processing. The material has been compiled from a teaching perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics, and can be used for courses on Artificial Intelligence, Advanced Image Processing, Computational Intelligence, etc. Likewise, the material can be useful for research from the evolutionary computation, artificial intelligence and image processing communities.

Download or read book Nature Inspired Optimization Techniques for Image Processing Applications written by Jude Hemanth and published by Springer. This book was released on 2018-09-19 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a platform for exploring nature-inspired optimization techniques in the context of imaging applications. Optimization has become part and parcel of all computational vision applications, and since the amount of data used in these applications is vast, the need for optimization techniques has increased exponentially. These accuracy and complexity are a major area of concern when it comes to practical applications. However, these optimization techniques have not yet been fully explored in the context of imaging applications. By presenting interdisciplinary concepts, ranging from optimization to image processing, the book appeals to a broad readership, while also encouraging budding engineers to pursue and employ innovative nature-inspired techniques for image processing applications.

Download or read book Learning based Optimization for Signal and Image Processing written by Jialin Liu and published by . This book was released on 2020 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incorporating machine learning techniques into optimization problems and solvers attracts increasing attention. Given a particular type of optimization problem that needs to be solved repeatedly, machine learning techniques can find some features for this category of optimization and develop algorithms with excellent performance. This thesis deals with algorithms and convergence analysis in learning-based optimization in three aspects: learning dictionaries, learning optimization solvers and learning regularizers. Learning dictionaries for sparse coding is significant for signal processing. Convolutional sparse coding is a form of sparse coding with a structured, translation invariant dictionary. Most convolutional dictionary learning algorithms to date operate in the batch mode, requiring simultaneous access to all training images during the learning process, which results in very high memory usage, and severely limits the training data size that can be used. I proposed two online convolutional dictionary learning algorithms that offered far better scaling of memory and computational cost than batch methods and provided a rigorous theoretical analysis of these methods. Learning fast solvers for optimization is a rising research topic. In recent years, unfolding iterative algorithms as neural networks has become an empirical success in solving sparse recovery problems. However, its theoretical understanding is still immature, which prevents us from fully utilizing the power of neural networks. I studied unfolded ISTA (Iterative Shrinkage Thresholding Algorithm) for sparse signal recovery and established its convergence. Based on the properties of parameters required by convergence, the model can be significantly simplified and, consequently, has much less training cost and better recovery performance. Learning regularizers or priors improves the performance of optimization solvers, especially for signal and image processing tasks. Plug-and-play (PnP) is a non-convex framework that integrates modern priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal algorithms. Although PnP has been recently studied extensively with great empirical success, theoretical analysis addressing even the most basic question of convergence has been insufficient. In this thesis, the theoretical convergence of PnP-FBS and PnP-ADMM was established, without using diminishing stepsizes, under a certain Lipschitz condition on the denoisers. Furthermore, real spectral normalization was proposed for training deep learning-based denoisers to satisfy the proposed Lipschitz condition.

Download or read book First order Convex Optimization Methods for Signal and Image Processing written by Tobias Lindstrøm Jensen and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Digital Signal Image and Video Processing for Emerging Multimedia Technology written by Byung-Gyu Kim and published by MDPI. This book was released on 2021-01-26 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents collective works published in the recent Special Issue (SI) entitled " Digital Signal, Image and Video Processing for Emerging Multimedia Technology". These works address the emerging technology in signal processing and its new aspects, as well as the related applications. Recent developments in image/video-based deep learning technology have enabled new services in the field of multimedia and recognition technology. The applications vary and range from digital signal processing to image, video and multimedia signal processing, also including object classification, learning mechanism design and data security. Recent advances in numerical, theoretical and experimental methodologies are presented within the scope of the current book, along with the finding of new learning methods and new methodological developments and their limitations. This book brings together a collection of inter-/multidisciplinary works applied to many classification and data security applications in a coherent manner.

Download or read book Handbook of Convex Optimization Methods in Imaging Science written by Vishal Monga and published by Springer. This book was released on 2017-10-27 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers recent advances in image processing and imaging sciences from an optimization viewpoint, especially convex optimization with the goal of designing tractable algorithms. Throughout the handbook, the authors introduce topics on the most key aspects of image acquisition and processing that are based on the formulation and solution of novel optimization problems. The first part includes a review of the mathematical methods and foundations required, and covers topics in image quality optimization and assessment. The second part of the book discusses concepts in image formation and capture from color imaging to radar and multispectral imaging. The third part focuses on sparsity constrained optimization in image processing and vision and includes inverse problems such as image restoration and de-noising, image classification and recognition and learning-based problems pertinent to image understanding. Throughout, convex optimization techniques are shown to be a critically important mathematical tool for imaging science problems and applied extensively. Convex Optimization Methods in Imaging Science is the first book of its kind and will appeal to undergraduate and graduate students, industrial researchers and engineers and those generally interested in computational aspects of modern, real-world imaging and image processing problems.

Download or read book Convex Optimization in Signal Processing and Communications written by Daniel P. Palomar and published by . This book was released on 2010 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past two decades there have been significant advances in the field of optimization. In particular, convex optimization has emerged as a powerful signal processing tool, and the variety of applications continues to grow rapidly. This book, written by a team of leading experts, sets out the theoretical underpinnings of the subject and provides tutorials on a wide range of convex optimization applications. Emphasis throughout is on cutting-edge research and on formulating problems in convex form, making this an ideal textbook for advanced graduate courses and a useful self-study guide. Topics covered range from automatic code generation, graphical models, and gradient-based algorithms for signal recovery, to semidefinite programming (SDP) relaxation and radar waveform design via SDP. It also includes blind source separation for image processing, robust broadband beamforming, distributed multi-agent optimization for networked systems, cognitive radio systems via game theory, and the variational inequality approach for Nash equilibrium solutions.

Download or read book Forensic Digital Image Processing written by Brian Dalrymple and published by CRC Press. This book was released on 2018-03-20 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: The digital revolution over the past several decades has advanced every facet of evidence detection, photography, optimization, and interpretation. Forensic scientists and practitioners have benefited tremendously from the move from film to digital. With proper procedures in place, digital images and casework capabilities have increased tremendously in both complexity and range due to a vast array of tools to enhance evidence and photography. Forensic Digital Image Processing: Optimization of Impression Evidence provides the forensic investigator with the tools and understanding to extract, optimize, and interpret the maximum evidence possible from crime scenes to increase identifications. The book begins by examining the emergence of forensic digital image processing, and the gradual improvement and acceptance of the science over the past four decades. Coverage includes looking at the issues of image integrity and authentication including forensic image optimization and the manipulation of images. Chapters explore techniques exploiting color theory, modes, and channels to optimize signal-to-noise ratio in images. One of the greatest assets of digital image technology is the ability to combine multiple images of the same subject to create a final, blended image: one that displays the desired evidence and is especially useful for fingerprint or footwear impression. Later chapters demonstrate image subtraction, focus stacking, and high dynamic range, utilizing images in optimum focus and with substrate interference diminished or removed entirely. The authors look at fast Fourier transform as an optimal tool for noise removal, addressing basic theory and diagnosis of the noise signatures. The book discusses the history of digital imaging techniques and their treatment within the court system. Forensic Digital Image Processing: Optimization of Impression Evidence serves as an invaluable resource and tool for practicing professionals–as well as those new to the field—to look at best practices, the latest technology, and advances in utilizing the increasing array of tools of the trade.

Download or read book Imaging Vision and Learning Based on Optimization and PDEs written by Xue-Cheng Tai and published by Springer. This book was released on 2018-11-19 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents the peer-reviewed proceedings of the international conference Imaging, Vision and Learning Based on Optimization and PDEs (IVLOPDE), held in Bergen, Norway, in August/September 2016. The contributions cover state-of-the-art research on mathematical techniques for image processing, computer vision and machine learning based on optimization and partial differential equations (PDEs). It has become an established paradigm to formulate problems within image processing and computer vision as PDEs, variational problems or finite dimensional optimization problems. This compact yet expressive framework makes it possible to incorporate a range of desired properties of the solutions and to design algorithms based on well-founded mathematical theory. A growing body of research has also approached more general problems within data analysis and machine learning from the same perspective, and demonstrated the advantages over earlier, more established algorithms. This volume will appeal to all mathematicians and computer scientists interested in novel techniques and analytical results for optimization, variational models and PDEs, together with experimental results on applications ranging from early image formation to high-level image and data analysis.

Download or read book Bio Inspired Computation and Applications in Image Processing written by Xin-She Yang and published by Academic Press. This book was released on 2016-08-09 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bio-Inspired Computation and Applications in Image Processing summarizes the latest developments in bio-inspired computation in image processing, focusing on nature-inspired algorithms that are linked with deep learning, such as ant colony optimization, particle swarm optimization, and bat and firefly algorithms that have recently emerged in the field. In addition to documenting state-of-the-art developments, this book also discusses future research trends in bio-inspired computation, helping researchers establish new research avenues to pursue. Reviews the latest developments in bio-inspired computation in image processing Focuses on the introduction and analysis of the key bio-inspired methods and techniques Combines theory with real-world applications in image processing Helps solve complex problems in image and signal processing Contains a diverse range of self-contained case studies in real-world applications

Download or read book Large scale Optimization and Deep Learning Techniques for Data driven Signal Processing written by Omar DeGuchy and published by . This book was released on 2020 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: The collection of data has become an integral part of our everyday lives. The algorithms necessary to process this information become paramount to our ability to interpret this resource. This type of data is typically recorded in a variety of signals including images, sounds, time series, and bio-informatics. In this work, we develop a number of algorithms to recover these types of signals in a variety of modalities. This work is mainly presented in two parts. Initially, we apply and develop large-scale optimization techniques used for signal processing. This includes the use of quasi-Newton methods to approximate second derivative information in a trust-region setting to solve regularized sparse signal recovery problems. We also formulate the compact representation of a large family of quasi-Newton methods known as the Broyden class. This extension of the classic quasi-Newton compact representation allows different updates to be used at every iteration. We also develop the algorithm to perform efficient solves with these representations. Within the realm of sparse signal recovery, but particular to photon-limited imaging applications, we also propose three novel algorithms for signal recovery in a low-light regime. First, we recover the support and lifetime decay of a flourophore from time dependent measurements. This type of modality is useful in identifying different types of molecular structures in tissue samples. The second algorithm identifies and implements the Shannon entropy function as a regularization technique for the promotion of sparsity in reconstructed signals from noisy downsampled observations. Finally, we also present an algorithm which addresses the difficulty of choosing the optimal parameters when solving the sparse signal recovery problem. There are two parameters which effect the quality of the reconstruction, the norm being used, as well as the intensity of the penalization imposed by that norm. We present an algorithm which uses a parallel asynchronous search along with a metric in order to find the optimal pair. The second portion of the dissertation draws on our experience with large-scale optimization and looks towards deep learning as an alternative to solving signal recovery problems. We first look to improve the standard gradient based techniques used during the training of these deep neural networks by presenting two novel optimization algorithms for deep learning. The first algorithm takes advantage of the limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton algorithm in a trust-region setting in order to address the large scale minimization problem associated with deep learning. The second algorithm uses second derivative information in a trust region setting where the Hessian is not explicitly stored. We then use a conjugate based method in order to solve the trust-region subproblem. Finally, we apply deep learning techniques to a variety of applications in signal recovery. These applications include revisiting the photon-limited regime where we recover signals from noisy downsampled observations, image disambiguation which involves the recovery of two signals which have been superimposed, deep learning for synthetic aperture radar (SAR) where we recover information describing the imaging system as well as evaluate the impact of reconstruction on the ability to perform target detection, and signal variation detection in the human genome where we leverage the relationships between subjects to provide better detection.