Mathematical Methods And Applications For Artificial Intelligence And Computer Vision

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Mathematical Methods and Applications for Artificial Intelligence and Computer Vision

Ezequiel López-Rubio,Esteban J Palomo,Enrique Domínguez
Mathematical Methods and Applications for Artificial Intelligence and Computer Vision Book PDF

Author : Ezequiel López-Rubio,Esteban J Palomo,Enrique Domínguez
Publisher : Mdpi AG
Page : 0 pages
File Size : 46,8 Mb
Release : 2024-01-25
Category : Computers
ISBN : 3725800618

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Mathematical Methods and Applications for Artificial Intelligence and Computer Vision by Ezequiel López-Rubio,Esteban J Palomo,Enrique Domínguez Pdf

This Reprint comprises all of the accepted articles published as part of the Special Issue "Mathematical Methods and Applications for Artificial Intelligence and Computer Vision". The aim of this Special Issue was to publish recent theoretical and applied studies in computational intelligence and related fields, with a particular focus on computer vision. Our goal was to inspire researchers in this community to further their research in the field of artificial intelligence and computer vision while also encouraging the exploration of their valuable applications across various fields and disciplines. We hope that the included papers will stimulate further research and development in the domains of artificial intelligence and computer vision.

Simulation and Analysis of Mathematical Methods in Real-Time Engineering Applications

T. Ananth Kumar,E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
Simulation and Analysis of Mathematical Methods in Real Time Engineering Applications Book PDF

Author : T. Ananth Kumar,E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi
Publisher : John Wiley & Sons
Page : 370 pages
File Size : 51,8 Mb
Release : 2021-08-16
Category : Mathematics
ISBN : 9781119785507

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Simulation and Analysis of Mathematical Methods in Real-Time Engineering Applications by T. Ananth Kumar,E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi Pdf

SIMULATIONS AND ANALYSIS of Mathematical Methods Written and edited by a group of international experts in the field, this exciting new volume covers the state of the art of real-time applications of computer science using mathematics. This breakthrough edited volume highlights the security, privacy, artificial intelligence, and practical approaches needed by engineers and scientists in all fields of science and technology. It highlights the current research, which is intended to advance not only mathematics but all areas of science, research, and development, and where these disciplines intersect. As the book is focused on emerging concepts in machine learning and artificial intelligence algorithmic approaches and soft computing techniques, it is an invaluable tool for researchers, academicians, data scientists, and technology developers. The newest and most comprehensive volume in the area of mathematical methods for use in real-time engineering, this groundbreaking new work is a must-have for any engineer or scientist’s library. Also useful as a textbook for the student, it is a valuable contribution to the advancement of the science, both a working handbook for the new hire or student, and a reference for the veteran engineer.

Mathematical Methods in Artificial Intelligence

Edward A. Bender
Mathematical Methods in Artificial Intelligence Book PDF

Author : Edward A. Bender
Publisher : Wiley-IEEE Computer Society Press
Page : 0 pages
File Size : 54,8 Mb
Release : 1996-02-10
Category : Technology & Engineering
ISBN : 0818672005

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Mathematical Methods in Artificial Intelligence by Edward A. Bender Pdf

Mathematical Methods in Artificial Intelligence introduces the student to the important mathematical foundations and tools in AI and describes their applications to the design of AI algorithms. This useful text presents an introductory AI course based on the most important mathematics and its applications. It focuses on important topics that are proven useful in AI and involve the most broadly applicable mathematics. The book explores AI from three different viewpoints: goals, methods or tools, and achievements and failures. Its goals of reasoning, planning, learning, or language understanding and use are centered around the expert system idea. The tools of AI are presented in terms of what can be incorporated in the data structures. The book looks into the concepts and tools of limited structure, mathematical logic, logic-like representation, numerical information, and nonsymbolic structures. The text emphasizes the main mathematical tools for representing and manipulating knowledge symbolically. These are various forms of logic for qualitative knowledge, and probability and related concepts for quantitative knowledge. The main tools for manipulating knowledge nonsymbolically, as neural nets, are optimization methods and statistics. This material is covered in the text by topics such as trees and search, classical mathematical logic, and uncertainty and reasoning. A solutions diskette is available, please call for more information.

Handbook of Mathematical Models in Computer Vision

Nikos Paragios,Yunmei Chen,Olivier D. Faugeras
Handbook of Mathematical Models in Computer Vision Book PDF

Author : Nikos Paragios,Yunmei Chen,Olivier D. Faugeras
Publisher : Springer Science & Business Media
Page : 612 pages
File Size : 48,8 Mb
Release : 2006-01-16
Category : Computers
ISBN : 9780387288314

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Handbook of Mathematical Models in Computer Vision by Nikos Paragios,Yunmei Chen,Olivier D. Faugeras Pdf

Abstract Biological vision is a rather fascinating domain of research. Scientists of various origins like biology, medicine, neurophysiology, engineering, math ematics, etc. aim to understand the processes leading to visual perception process and at reproducing such systems. Understanding the environment is most of the time done through visual perception which appears to be one of the most fundamental sensory abilities in humans and therefore a significant amount of research effort has been dedicated towards modelling and repro ducing human visual abilities. Mathematical methods play a central role in this endeavour. Introduction David Marr's theory v^as a pioneering step tov^ards understanding visual percep tion. In his view human vision was based on a complete surface reconstruction of the environment that was then used to address visual subtasks. This approach was proven to be insufficient by neuro-biologists and complementary ideas from statistical pattern recognition and artificial intelligence were introduced to bet ter address the visual perception problem. In this framework visual perception is represented by a set of actions and rules connecting these actions. The emerg ing concept of active vision consists of a selective visual perception paradigm that is basically equivalent to recovering from the environment the minimal piece information required to address a particular task of interest.

Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics

Jianping Gou,Weihua Ou,Shaoning Zeng
Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence Data Mining and Robotics Book PDF

Author : Jianping Gou,Weihua Ou,Shaoning Zeng
Publisher : Mdpi AG
Page : 0 pages
File Size : 55,6 Mb
Release : 2023-06-14
Category : Electronic
ISBN : 3036572627

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Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics by Jianping Gou,Weihua Ou,Shaoning Zeng Pdf

The present reprint contains 33 articles accepted and published in the Special Issue entitled "Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics, 2022" in the MDPI journal, Mathematics, which covers a wide range of topics connected to the theory and applications of feature representation learning for image processing, artificial intelligence, data mining and robotics. These topics include, among others, elements from image blurring, image aesthetic quality assessment, pedestrian detection, visual tracking, vehicle re-identification, face recognition, 3D reconstruction, the stability of switched systems, domain adaption, deep reinforcement, sentiment analysis, graph convolutional networks, knowledge graphs, geometric metric learning, etc. It is hoped that this reprint will be interesting and useful for those working in the area of image processing, computer vision, machine learning, natural language processing and robotics, as well as for those with backgrounds in machine learning who are willing to become familiar with recent advancements in artificial intelligence, which, today, is present in almost all aspects of human life and activities.

Image Processing III

Jonathan M. Blackledge,Martin J. Turner
Image Processing III Book PDF

Author : Jonathan M. Blackledge,Martin J. Turner
Publisher : ISBS
Page : 330 pages
File Size : 49,5 Mb
Release : 2001
Category : Computers
ISBN : 1898563721

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Image Processing III by Jonathan M. Blackledge,Martin J. Turner Pdf

International specialists report recent research and development, focusing on new applications: The book records proceedings of the IMA (Institution of Mathematics and Applications) conference co-sponsored with the Institute of Physics and the Institution of Electrical Engineers. Contents: Noise analysis: binary random images superposition: probabilistic image smoothing; Segmentation and pattern recognition; image segmentation; colour pattern recognition: Finger print identification; algorithms of 3-D Iso surfaces; mathematical model of image segmentation 3-D on parametric segmentation method: Artificial intelligence; Automatic satellite target detection; Analysis in light, confocal and electron microscopes; Compression Issues; Artificial neural networks; Coefficient video modelling; Progressive transmission: smoothing facsimile images; Human face identification; Fractals and wavelets; lacunarity; Wavelet processing of coloured images; Optical flow analysis; Computing optical fl

Mathematics for Machine Learning

Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
Mathematics for Machine Learning Book PDF

Author : Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
Publisher : Cambridge University Press
Page : 391 pages
File Size : 51,9 Mb
Release : 2020-04-23
Category : Computers
ISBN : 9781108470049

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Mathematics for Machine Learning by Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong Pdf

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Symbolic and Numerical Computation for Artificial Intelligence

Bruce R. Donald,Deepak Kapur,Joseph L. Mundy
Symbolic and Numerical Computation for Artificial Intelligence Book PDF

Author : Bruce R. Donald,Deepak Kapur,Joseph L. Mundy
Publisher : Unknown
Page : 392 pages
File Size : 48,5 Mb
Release : 1992
Category : Computers
ISBN : UOM:39015028453804

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Symbolic and Numerical Computation for Artificial Intelligence by Bruce R. Donald,Deepak Kapur,Joseph L. Mundy Pdf

Over the last decade, there has been considerable progress in investigating methods of symbolic mathematics in many application areas of computer science and artifical intelligence, such as engineering design, solid and geometric modelling, robotics and motion planning, and machine vision. This research has produced few applications within engineering and robotics because of the combinatorial cost of symbolic techniques. Therefore, it is essential to investigate approaches for systematic integration of symbolic with numerical techniques which are efficient for handling the huge amount of data that arises in practical applications, while at the same time maintain a logically consistent solution framework. Symbolic and Numerical Computation for Artificial Intelligence gives an overview of applications in machine vision, robotics and engineering design where there is a need for integrating symbolic and numerical methods. It also illustrates the case for an integrated symbolic and numerical environment to support the needs of these applications. This book will be essential reading for researchers in applied mathematics, symbolic and algebraic manipulation, and applied artificial intell

Mathematical Methods in Computer Vision

Peter J. Olver
Mathematical Methods in Computer Vision Book PDF

Author : Peter J. Olver
Publisher : Springer Science & Business Media
Page : 176 pages
File Size : 52,6 Mb
Release : 2003-10
Category : Business & Economics
ISBN : 0387004971

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Mathematical Methods in Computer Vision by Peter J. Olver Pdf

"Comprises some of the key work presented at two IMA Wokshops on Computer Vision during fall of 2000."--Pref.

Algorithmic Advances in Riemannian Geometry and Applications

Hà Quang Minh,Vittorio Murino
Algorithmic Advances in Riemannian Geometry and Applications Book PDF

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.

Modern Mathematics and Applications in Computer Graphics and Vision

Hongyu Guo
Modern Mathematics and Applications in Computer Graphics and Vision Book PDF

Author : Hongyu Guo
Publisher : World Scientific Publishing Company
Page : 0 pages
File Size : 49,8 Mb
Release : 2014
Category : Computer graphics
ISBN : 9814449334

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Modern Mathematics and Applications in Computer Graphics and Vision by Hongyu Guo Pdf

Mathematical Structures; Algebra: Linear Algebra; Tensor Algebra; Exterior Algebra; Geometric Algebra; Geometry: Projective Geometry; Differential Geometry; Non-Euclidean Geometry; Topology and More: General Topology; Manifolds; Hilbert Spaces; Measure Spaces and Probability Spaces; Applications: Color Spaces; Perspective Analysis of Images; Quaternions and 3-D Rotations; Support Vector Machines and Reproducing Kernel Hilbert Spaces; Manifold Learning in Machine Learning;

Variational, Geometric, and Level Set Methods in Computer Vision

Nikos Paragios,Olivier Faugeras,Tony Chan,Christoph Schnoerr
Variational Geometric and Level Set Methods in Computer Vision Book PDF

Author : Nikos Paragios,Olivier Faugeras,Tony Chan,Christoph Schnoerr
Publisher : Springer
Page : 0 pages
File Size : 55,6 Mb
Release : 2005-10-13
Category : Computers
ISBN : 3540321098

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Variational, Geometric, and Level Set Methods in Computer Vision by Nikos Paragios,Olivier Faugeras,Tony Chan,Christoph Schnoerr Pdf

Mathematical methods has been a dominant research path in computational vision leading to a number of areas like ?ltering, segmentation, motion analysis and stereo reconstruction. Within such a branch visual perception tasks can either be addressed through the introduction of application-driven geometric ?ows or through the minimization of problem-driven cost functions where their lowest potential corresponds to image understanding. The 3rd IEEE Workshop on Variational, Geometric and Level Set Methods focused on these novel mathematical techniques and their applications to c- puter vision problems. To this end, from a substantial number of submissions, 30 high-quality papers were selected after a fully blind review process covering a large spectrum of computer-aided visual understanding of the environment. The papers are organized into four thematic areas: (i) Image Filtering and Reconstruction, (ii) Segmentation and Grouping, (iii) Registration and Motion Analysis and (iiii) 3D and Reconstruction. In the ?rst area solutions to image enhancement, inpainting and compression are presented, while more advanced applications like model-free and model-based segmentation are presented in the segmentation area. Registration of curves and images as well as multi-frame segmentation and tracking are part of the motion understanding track, while - troducing computationalprocessesinmanifolds,shapefromshading,calibration and stereo reconstruction are part of the 3D track. We hope that the material presented in the proceedings exceeds your exp- tations and will in?uence your research directions in the future. We would like to acknowledge the support of the Imaging and Visualization Department of Siemens Corporate Research for sponsoring the Best Student Paper Award.

Mathematical Methods in Computer Vision

Peter J. Olver,Allen Tannenbaum
Mathematical Methods in Computer Vision Book PDF

Author : Peter J. Olver,Allen Tannenbaum
Publisher : Springer
Page : 0 pages
File Size : 51,6 Mb
Release : 2010-11-16
Category : Business & Economics
ISBN : 1475741278

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Mathematical Methods in Computer Vision by Peter J. Olver,Allen Tannenbaum Pdf

This volume comprises some of the key work presented at two IMA Workshops on Computer Vision during fall of 2000. Recent years have seen significant advances in the application of sophisticated mathematical theories to the problems arising in image processing. Basic issues include image smoothing and denoising, image enhancement, morphology, image compression, and segmentation (determining boundaries of objects-including problems of camera distortion and partial occlusion). Several mathematical approaches have emerged, including methods based on nonlinear partial differential equations, stochastic and statistical methods, and signal processing techniques, including wavelets and other transform theories. Shape theory is of fundamental importance since it is the bottleneck between high and low level vision, and formed the bridge between the two workshops on vision. The recent geometric partial differential equation methods have been essential in throwing new light on this very difficult problem area. Further, stochastic processes, including Markov random fields, have been used in a Bayesian framework to incorporate prior constraints on smoothness and the regularities of discontinuities into algorithms for image restoration and reconstruction. A number of applications are considered including optical character and handwriting recognizers, printed-circuit board inspection systems and quality control devices, motion detection, robotic control by visual feedback, reconstruction of objects from stereoscopic view and/or motion, autonomous road vehicles, and many others.

Mathematical Aspects of Artificial Intelligence

Frederick Hoffman,American Mathematical Society
Mathematical Aspects of Artificial Intelligence Book PDF

Author : Frederick Hoffman,American Mathematical Society
Publisher : American Mathematical Soc.
Page : 290 pages
File Size : 42,5 Mb
Release : 1998
Category : Artificial intelligence
ISBN : 9780821806111

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Mathematical Aspects of Artificial Intelligence by Frederick Hoffman,American Mathematical Society Pdf

There exists a history of great expectations and large investments involving artificial intelligence (AI). There are also notable shortfalls and memorable disappointments. One major controversy regarding AI is just how mathematical a field it is or should be. This text includes contributions that examine the connections between AI and mathematics, demonstrating the potential for mathematical applications and exposing some of the more mathematical areas within AI. The goal is to stimulate interest in people who can contribute to the field or use its results. Included in the work by M. Newborn on the famous Deep BLue chess match. He discusses highly mathematical techniques involving graph theory, combinatorics and probability and statistics. G. Shafer offers his development of probability through probability trees with some of the results appearing here for the first time. M. Golumbic treats temporal reasoning with ties to the famous Frame Problem. His contribution involves logic, combinatorics and graph theory and leads to two chapters with logical themes. H. Kirchner explains how ordering techniques in automated reasoning systems make deduction more efficient. Constraint logic programming is discussed by C. Lassez, who shows its intimate ties to linear programming with crucial theorems going back to Fourier. V. Nalwa's work provides a brief tour of computer vision, tying it to mathematics - from combinatorics, probability and geometry to partial differential equations. All authors are gifted expositors and are current contributors to the field. The wide scope of the volume includes research problems, research tools and good motivational material for teaching.

Applications of Continuous Mathematics to Computer Science

Hung T. Nguyen,V. Kreinovich
Applications of Continuous Mathematics to Computer Science Book PDF

Author : Hung T. Nguyen,V. Kreinovich
Publisher : Springer Science & Business Media
Page : 440 pages
File Size : 41,8 Mb
Release : 1997-10-31
Category : Mathematics
ISBN : 0792347226

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Applications of Continuous Mathematics to Computer Science by Hung T. Nguyen,V. Kreinovich Pdf

This volume is intended to be used as a textbook for a special topic course in computer science. It addresses contemporary research topics of interest such as intelligent control, genetic algorithms, neural networks, optimization techniques, expert systems, fractals, and computer vision. The work incorporates many new research ideas, and focuses on the role of continuous mathematics. Audience: This book will be valuable to graduate students interested in theoretical computer topics, algorithms, expert systems, neural networks, and software engineering.