Optimization For Computer Vision

Author : Marco Alexander Treiber
Publisher : Springer Science & Business Media
Page : 257 pages
File Size : 40,9 Mb
Release : 2013-07-12
Category : Computers
ISBN : 9781447152835

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Optimization for Computer Vision by Marco Alexander Treiber Pdf

This practical and authoritative text/reference presents a broad introduction to the optimization methods used specifically in computer vision. In order to facilitate understanding, the presentation of the methods is supplemented by simple flow charts, followed by pseudocode implementations that reveal deeper insights into their mode of operation. These discussions are further supported by examples taken from important applications in computer vision. Topics and features: provides a comprehensive overview of computer vision-related optimization; covers a range of techniques from classical iterative multidimensional optimization to cutting-edge topics of graph cuts and GPU-suited total variation-based optimization; describes in detail the optimization methods employed in computer vision applications; illuminates key concepts with clearly written and step-by-step explanations; presents detailed information on implementation, including pseudocode for most methods.

Author : Mongi A. Abidi,Andrei V. Gribok,Joonki Paik
Publisher : Springer
Page : 293 pages
File Size : 46,8 Mb
Release : 2016-12-06
Category : Computers
ISBN : 9783319463643

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Optimization Techniques in Computer Vision by Mongi A. Abidi,Andrei V. Gribok,Joonki Paik Pdf

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.

Author : Luiz Velho,Paulo Carvalho,Jonas Gomes,Luiz de Figueiredo
Publisher : Morgan Kaufmann
Page : 304 pages
File Size : 54,8 Mb
Release : 2011-08-09
Category : Computers
ISBN : 008087858X

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Mathematical Optimization in Computer Graphics and Vision by Luiz Velho,Paulo Carvalho,Jonas Gomes,Luiz de Figueiredo Pdf

Mathematical optimization is used in nearly all computer graphics applications, from computer vision to animation. This book teaches readers the core set of techniques that every computer graphics professional should understand in order to envision and expand the boundaries of what is possible in their work. Study of this authoritative reference will help readers develop a very powerful tool- the ability to create and decipher mathematical models that can better realize solutions to even the toughest problems confronting computer graphics community today. *Distills down a vast and complex world of information on optimization into one short, self-contained volume especially for computer graphics *Helps CG professionals identify the best technique for solving particular problems quickly, by categorizing the most effective algorithms by application *Keeps readers current by supplementing the focus on key, classic methods with special end-of-chapter sections on cutting-edge developments

Author : Xue-Cheng Tai,Egil Bae,Marius Lysaker
Publisher : Springer
Page : 255 pages
File Size : 53,7 Mb
Release : 2018-11-19
Category : Computers
ISBN : 9783319912745

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Imaging, Vision and Learning Based on Optimization and PDEs by Xue-Cheng Tai,Egil Bae,Marius Lysaker Pdf

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.

Author : Andrés Bruhn,Thomas Pock,Xue-Cheng Tai
Publisher : Springer
Page : 175 pages
File Size : 44,5 Mb
Release : 2014-04-01
Category : Computers
ISBN : 9783642547744

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Efficient Algorithms for Global Optimization Methods in Computer Vision by Andrés Bruhn,Thomas Pock,Xue-Cheng Tai Pdf

This book constitutes the thoroughly refereed post-conference proceedings of the International Dagstuhl-Seminar on Efficient Algorithms for Global Optimization Methods in Computer Vision, held in Dagstuhl Castle, Germany, in November 2011. The 8 revised full papers presented were carefully reviewed and selected by 12 lectures given at the seminar. The seminar focused on the entire algorithmic development pipeline for global optimization problems in computer vision: modelling, mathematical analysis, numerical solvers and parallelization. In particular, the goal of the seminar was to bring together researchers from all four fields to analyze and discuss the connections between the different stages of the algorithmic design pipeline.

Author : Yuri Boykov,Andrew Delong,Olga Veksler,Vladimir Kolmogorov
Publisher : Morgan & Claypool
Page : 100 pages
File Size : 43,6 Mb
Release : 2010-03-01
Category : Computers
ISBN : 1608451097

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Optimization in Computer Vision by Yuri Boykov,Andrew Delong,Olga Veksler,Vladimir Kolmogorov Pdf

Author : Zhouchen Lin,Huan Li,Cong Fang
Publisher : Springer Nature
Page : 286 pages
File Size : 52,7 Mb
Release : 2020-05-29
Category : Computers
ISBN : 9789811529108

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Accelerated Optimization for Machine Learning by Zhouchen Lin,Huan Li,Cong Fang Pdf

This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.

Author : Suvrit Sra,Sebastian Nowozin,Stephen J. Wright
Publisher : MIT Press
Page : 509 pages
File Size : 53,9 Mb
Release : 2012
Category : Computers
ISBN : 9780262016469

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Optimization for Machine Learning by Suvrit Sra,Sebastian Nowozin,Stephen J. Wright Pdf

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

Author : Omar Oreifej,Mubarak Shah
Publisher : Springer Science & Business Media
Page : 114 pages
File Size : 49,9 Mb
Release : 2014-03-24
Category : Computers
ISBN : 9783319041841

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Robust Subspace Estimation Using Low-Rank Optimization by Omar Oreifej,Mubarak Shah Pdf

Various fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book, the authors discuss fundamental formulations and extensions for low-rank optimization-based subspace estimation and representation. By minimizing the rank of the matrix containing observations drawn from images, the authors demonstrate how to solve four fundamental computer vision problems, including video denosing, background subtraction, motion estimation, and activity recognition.

Author : Jean H Gallier,Jocelyn Quaintance
Publisher : World Scientific
Page : 823 pages
File Size : 54,9 Mb
Release : 2020-01-22
Category : Mathematics
ISBN : 9789811206412

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Linear Algebra And Optimization With Applications To Machine Learning - Volume I: Linear Algebra For Computer Vision, Robotics, And Machine Learning by Jean H Gallier,Jocelyn Quaintance Pdf

This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.

Author : Giovanni Maria Farinella,Sebastiano Battiato,Roberto Cipolla
Publisher : Springer Science & Business Media
Page : 433 pages
File Size : 54,7 Mb
Release : 2013-09-24
Category : Computers
ISBN : 9781447155201

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Advanced Topics in Computer Vision by Giovanni Maria Farinella,Sebastiano Battiato,Roberto Cipolla Pdf

This book presents a broad selection of cutting-edge research, covering both theoretical and practical aspects of reconstruction, registration, and recognition. The text provides an overview of challenging areas and descriptions of novel algorithms. Features: investigates visual features, trajectory features, and stereo matching; reviews the main challenges of semi-supervised object recognition, and a novel method for human action categorization; presents a framework for the visual localization of MAVs, and for the use of moment constraints in convex shape optimization; examines solutions to the co-recognition problem, and distance-based classifiers for large-scale image classification; describes how the four-color theorem can be used for solving MRF problems; introduces a Bayesian generative model for understanding indoor environments, and a boosting approach for generalizing the k-NN rule; discusses the issue of scene-specific object detection, and an approach for making temporal super resolution video.

Author : Gustavo Olague
Publisher : Springer
Page : 411 pages
File Size : 55,8 Mb
Release : 2016-09-28
Category : Computers
ISBN : 9783662436936

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Evolutionary Computer Vision by Gustavo Olague Pdf

This book explains the theory and application of evolutionary computer vision, a new paradigm where challenging vision problems can be approached using the techniques of evolutionary computing. This methodology achieves excellent results for defining fitness functions and representations for problems by merging evolutionary computation with mathematical optimization to produce automatic creation of emerging visual behaviors. In the first part of the book the author surveys the literature in concise form, defines the relevant terminology, and offers historical and philosophical motivations for the key research problems in the field. For researchers from the computer vision community, he offers a simple introduction to the evolutionary computing paradigm. The second part of the book focuses on implementing evolutionary algorithms that solve given problems using working programs in the major fields of low-, intermediate- and high-level computer vision. This book will be of value to researchers, engineers, and students in the fields of computer vision, evolutionary computing, robotics, biologically inspired mechatronics, electronics engineering, control, and artificial intelligence.

Author : Hà Quang Minh,Vittorio Murino
Publisher : Springer
Page : 208 pages
File Size : 54,8 Mb
Release : 2016-10-05
Category : Computers
ISBN : 9783319450261

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Algorithmic Advances in Riemannian Geometry and Applications by Hà Quang Minh,Vittorio Murino Pdf

This book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using the mathematical machinery of Riemannian geometry. As demonstrated by all the chapters in the book, when the data is intrinsically non-Euclidean, the utilization of this geometrical information can lead to better algorithms that can capture more accurately the structures inherent in the data, leading ultimately to better empirical performance. This book is not intended to be an encyclopedic compilation of the applications of Riemannian geometry. Instead, it focuses on several important research directions that are currently actively pursued by researchers in the field. These include statistical modeling and analysis on manifolds,optimization on manifolds, Riemannian manifolds and kernel methods, and dictionary learning and sparse coding on manifolds. Examples of applications include novel algorithms for Monte Carlo sampling and Gaussian Mixture Model fitting, 3D brain image analysis,image classification, action recognition, and motion tracking.

Author : Kapil Joshi,Shubham Mahajan,Amit Kant Pandit,Nitish Pathak
Publisher : Wiley
Page : 0 pages
File Size : 46,6 Mb
Release : 2024-09-04
Category : Computers
ISBN : 1394230923

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Integrating Metaheuristics in Computer Vision for Real-World Optimization Problems by Kapil Joshi,Shubham Mahajan,Amit Kant Pandit,Nitish Pathak Pdf

Author : Fouad Sabry
Publisher : One Billion Knowledgeable
Page : 133 pages
File Size : 43,7 Mb
Release : 2024-05-11
Category : Computers
ISBN : PKEY:6610000567294

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Least Squares by Fouad Sabry Pdf

What is Least Squares The method of least squares is a parameter estimation method in regression analysis based on minimizing the sum of the squares of the residuals made in the results of each individual equation. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Least squares Chapter 2: Gauss-Markov theorem Chapter 3: Regression analysis Chapter 4: Ridge regression Chapter 5: Total least squares Chapter 6: Ordinary least squares Chapter 7: Weighted least squares Chapter 8: Simple linear regression Chapter 9: Generalized least squares Chapter 10: Linear least squares (II) Answering the public top questions about least squares. (III) Real world examples for the usage of least squares in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Least Squares.