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Learning-Based Local Visual Representation and Indexing

Learning-Based Local Visual Representation and Indexing
A Book

by Rongrong Ji,Yue Gao,Ling-Yu Duan,Hongxun Yao,Qionghai Dai

  • Publisher : Morgan Kaufmann
  • Release : 2015-04-08
  • Pages : 128
  • ISBN : 0128026200
  • Language : En, Es, Fr & De
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Learning-Based Local Visual Representation and Indexing, reviews the state-of-the-art in visual content representation and indexing, introduces cutting-edge techniques in learning based visual representation, and discusses emerging topics in visual local representation, and introduces the most recent advances in content-based visual search techniques. Discusses state-of-the-art procedures in learning-based local visual representation. Shows how to master the basic techniques needed for building a large-scale visual search engine and indexing system Provides insight into how machine learning techniques can be leveraged to refine the visual recognition system, especially in the part of visual feature representation.

Encyclopedia of Multimedia

Encyclopedia of Multimedia
A Book

by Borko Furht

  • Publisher : Springer Science & Business Media
  • Release : 2008-11-26
  • Pages : 1001
  • ISBN : 0387747249
  • Language : En, Es, Fr & De
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This second edition provides easy access to important concepts, issues and technology trends in the field of multimedia technologies, systems, techniques, and applications. Over 1,100 heavily-illustrated pages — including 80 new entries — present concise overviews of all aspects of software, systems, web tools and hardware that enable video, audio and developing media to be shared and delivered electronically.

Artificial Intelligence and Computational Intelligence

Artificial Intelligence and Computational Intelligence
4th International Conference, AICI 2012, Chengdu, China, October 26-28, 2012, Proceedings

by Jingsheng Lei,Fu Lee Wang,Hepu Deng,Duoqian Miao

  • Publisher : Springer
  • Release : 2012-09-28
  • Pages : 788
  • ISBN : 3642334784
  • Language : En, Es, Fr & De
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This volume proceedings contains revised selected papers from the 4th International Conference on Artificial Intelligence and Computational Intelligence, AICI 2012, held in Chengdu, China, in October 2012. The total of 163 high-quality papers presented were carefully reviewed and selected from 724 submissions. The papers are organized into topical sections on applications of artificial intelligence, applications of computational intelligence, data mining and knowledge discovery, evolution strategy, expert and decision support systems, fuzzy computation, information security, intelligent control, intelligent image processing, intelligent information fusion, intelligent signal processing, machine learning, neural computation, neural networks, particle swarm optimization, and pattern recognition.

Encyclopedia of Image Processing

Encyclopedia of Image Processing
A Book

by Phillip A. Laplante

  • Publisher : CRC Press
  • Release : 2018-11-08
  • Pages : 856
  • ISBN : 1351032739
  • Language : En, Es, Fr & De
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The Encyclopedia of Image Processing presents a vast collection of well-written articles covering image processing fundamentals (e.g. color theory, fuzzy sets, cryptography) and applications (e.g. geographic information systems, traffic analysis, forgery detection). Image processing advances have enabled many applications in healthcare, avionics, robotics, natural resource discovery, and defense, which makes this text a key asset for both academic and industrial libraries and applied scientists and engineers working in any field that utilizes image processing. Written by experts from both academia and industry, it is structured using the ACM Computing Classification System (CCS) first published in 1988, but most recently updated in 2012.

ImageCLEF

ImageCLEF
Experimental Evaluation in Visual Information Retrieval

by Henning Müller,Paul Clough,Thomas Deselaers,Barbara Caputo

  • Publisher : Springer Science & Business Media
  • Release : 2010-08-20
  • Pages : 544
  • ISBN : 3642151817
  • Language : En, Es, Fr & De
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The pervasive creation and consumption of content, especially visual content, is ingrained into our modern world. We’re constantly consuming visual media content, in printed form and in digital form, in work and in leisure pursuits. Like our cave– man forefathers, we use pictures to record things which are of importance to us as memory cues for the future, but nowadays we also use pictures and images to document processes; we use them in engineering, in art, in science, in medicine, in entertainment and we also use images in advertising. Moreover, when images are in digital format, either scanned from an analogue format or more often than not born digital, we can use the power of our computing and networking to exploit images to great effect. Most of the technical problems associated with creating, compressing, storing, transmitting, rendering and protecting image data are already solved. We use - cepted standards and have tremendous infrastructure and the only outstanding ch- lenges, apart from managing the scale issues associated with growth, are to do with locating images. That involves analysing them to determine their content, clas- fying them into related groupings, and searching for images. To overcome these challenges we currently rely on image metadata, the description of the images, - ther captured automatically at creation time or manually added afterwards.

Content-Based Image Retrieval

Content-Based Image Retrieval
Ideas, Influences, and Current Trends

by Vipin Tyagi

  • Publisher : Springer
  • Release : 2018-01-15
  • Pages : 378
  • ISBN : 9811067597
  • Language : En, Es, Fr & De
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The book describes several techniques used to bridge the semantic gap and reflects on recent advancements in content-based image retrieval (CBIR). It presents insights into and the theoretical foundation of various essential concepts related to image searches, together with examples of natural and texture image types. The book discusses key challenges and research topics in the context of image retrieval, and provides descriptions of various image databases used in research studies. The area of image retrieval, and especially content-based image retrieval (CBIR), is a very exciting one, both for research and for commercial applications. The book explains the low-level features that can be extracted from an image (such as color, texture, shape) and several techniques used to successfully bridge the semantic gap in image retrieval, making it a valuable resource for students and researchers interested in the area of CBIR alike.

Semantic Mining Technologies for Multimedia Databases

Semantic Mining Technologies for Multimedia Databases
A Book

by Tao, Dacheng,Xu, Dong,Li, Xuelong

  • Publisher : IGI Global
  • Release : 2009-04-30
  • Pages : 550
  • ISBN : 1605661899
  • Language : En, Es, Fr & De
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Provides an introduction to recent techniques in multimedia semantic mining necessary to researchers new to the field.

Intelligent Control of Robotic Systems

Intelligent Control of Robotic Systems
A Book

by Laxmidhar Behera,Swagat Kumar,Prem Kumar Patchaikani,Ranjith Ravindranathan Nair,Samrat Dutta

  • Publisher : CRC Press
  • Release : 2020-04-07
  • Pages : 650
  • ISBN : 0429944004
  • Language : En, Es, Fr & De
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This book illustrates basic principles, along with the development of the advanced algorithms, to realize smart robotic systems. It speaks to strategies by which a robot (manipulators, mobile robot, quadrotor) can learn its own kinematics and dynamics from data. In this context, two major issues have been dealt with; namely, stability of the systems and experimental validations. Learning algorithms and techniques as covered in this book easily extend to other robotic systems as well. The book contains MATLAB- based examples and c-codes under robot operating systems (ROS) for experimental validation so that readers can replicate these algorithms in robotics platforms.

Computer Vision -- ACCV 2012

Computer Vision -- ACCV 2012
11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part III

by Kyoung Mu Lee,Yasuyuki Matsushita,James M. Rehg,Zhanyi Hu

  • Publisher : Springer
  • Release : 2013-03-27
  • Pages : 741
  • ISBN : 364237431X
  • Language : En, Es, Fr & De
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The four-volume set LNCS 7724--7727 constitutes the thoroughly refereed post-conference proceedings of the 11th Asian Conference on Computer Vision, ACCV 2012, held in Daejeon, Korea, in November 2012. The total of 226 contributions presented in these volumes was carefully reviewed and selected from 869 submissions. The papers are organized in topical sections on object detection, learning and matching; object recognition; feature, representation, and recognition; segmentation, grouping, and classification; image representation; image and video retrieval and medical image analysis; face and gesture analysis and recognition; optical flow and tracking; motion, tracking, and computational photography; video analysis and action recognition; shape reconstruction and optimization; shape from X and photometry; applications of computer vision; low-level vision and applications of computer vision.

Semantic-Based Visual Information Retrieval

Semantic-Based Visual Information Retrieval
A Book

by Zhang, Yu-Jin

  • Publisher : IGI Global
  • Release : 2006-11-30
  • Pages : 368
  • ISBN : 1599043726
  • Language : En, Es, Fr & De
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"This book presents state-of-the-art advancements and developments in the field, and also brings a selection of techniques and algorithms about semantic-based visual information retrieval. It covers many critical issues, such as: multi-level representation and description, scene understanding, semantic modeling, image and video annotation, human-computer interaction, and more"--Provided by publisher.

Machine Vision Inspection Systems, Machine Learning-Based Approaches

Machine Vision Inspection Systems, Machine Learning-Based Approaches
A Book

by Muthukumaran Malarvel,Soumya Ranjan Nayak,Prasant Kumar Pattnaik,Surya Narayan Panda

  • Publisher : John Wiley & Sons
  • Release : 2021-01-14
  • Pages : 352
  • ISBN : 111978610X
  • Language : En, Es, Fr & De
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Machine Vision Inspection Systems (MVIS) is a multidisciplinary research field that emphasizes image processing, machine vision and, pattern recognition for industrial applications. Inspection techniques are generally used in destructive and non-destructive evaluation industry. Now a day's the current research on machine inspection gained more popularity among various researchers, because the manual assessment of the inspection may fail and turn into false assessment due to a large number of examining while inspection process. This volume 2 covers machine learning-based approaches in MVIS applications and it can be employed to a wide diversity of problems particularly in Non-Destructive testing (NDT), presence/absence detection, defect/fault detection (weld, textile, tiles, wood, etc.), automated vision test & measurement, pattern matching, optical character recognition & verification (OCR/OCV), natural language processing, medical diagnosis, etc. This edited book is designed to address various aspects of recent methodologies, concepts, and research plan out to the readers for giving more depth insights for perusing research on machine vision using machine learning-based approaches.

Computer Vision Using Local Binary Patterns

Computer Vision Using Local Binary Patterns
A Book

by Matti Pietikäinen,Abdenour Hadid,Guoying Zhao,Timo Ahonen

  • Publisher : Springer Science & Business Media
  • Release : 2011-06-21
  • Pages : 212
  • ISBN : 0857297481
  • Language : En, Es, Fr & De
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The recent emergence of Local Binary Patterns (LBP) has led to significant progress in applying texture methods to various computer vision problems and applications. The focus of this research has broadened from 2D textures to 3D textures and spatiotemporal (dynamic) textures. Also, where texture was once utilized for applications such as remote sensing, industrial inspection and biomedical image analysis, the introduction of LBP-based approaches have provided outstanding results in problems relating to face and activity analysis, with future scope for face and facial expression recognition, biometrics, visual surveillance and video analysis. Computer Vision Using Local Binary Patterns provides a detailed description of the LBP methods and their variants both in spatial and spatiotemporal domains. This comprehensive reference also provides an excellent overview as to how texture methods can be utilized for solving different kinds of computer vision and image analysis problems. Source codes of the basic LBP algorithms, demonstrations, some databases and a comprehensive LBP bibliography can be found from an accompanying web site. Topics include: local binary patterns and their variants in spatial and spatiotemporal domains, texture classification and segmentation, description of interest regions, applications in image retrieval and 3D recognition - Recognition and segmentation of dynamic textures, background subtraction, recognition of actions, face analysis using still images and image sequences, visual speech recognition and LBP in various applications. Written by pioneers of LBP, this book is an essential resource for researchers, professional engineers and graduate students in computer vision, image analysis and pattern recognition. The book will also be of interest to all those who work with specific applications of machine vision.

Deep Learning for Image Processing Applications

Deep Learning for Image Processing Applications
A Book

by D.J. Hemanth,V. Vieira Estrela

  • Publisher : IOS Press
  • Release : 2017-12
  • Pages : 284
  • ISBN : 1614998221
  • Language : En, Es, Fr & De
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Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.

Computer Vision for Microscopy Image Analysis

Computer Vision for Microscopy Image Analysis
A Book

by Mei Chen

  • Publisher : Academic Press
  • Release : 2020-12-01
  • Pages : 228
  • ISBN : 0128149736
  • Language : En, Es, Fr & De
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Are you a computer scientist working on image analysis? Are you a biologist seeking tools to process the microscopy data from image-based experiments? Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth discussion of modern computer vision techniques, in particular deep learning, for microscopy image analysis that will advance your efforts. Progress in imaging techniques has enabled the acquisition of large volumes of microscopy data and made it possible to conduct large-scale, image-based experiments for biomedical discovery. The main challenge and bottleneck in such experiments is the conversion of "big visual data" into interpretable information. Visual analysis of large-scale microscopy data is a daunting task. Computer vision has the potential to automate this task. One key advantage is that computers perform analysis more reproducibly and less subjectively than human annotators. Moreover, high-throughput microscopy calls for effective and efficient techniques as there are not enough human resources to advance science by manual annotation. This book articulates the strong need for biologists and computer vision experts to collaborate to overcome the limits of human visual perception, and devotes a chapter each to the major steps in analyzing microscopy images, such as detection and segmentation, classification, tracking, and event detection. Discover how computer vision can automate and enhance the human assessment of microscopy images for discovery Grasp the state-of-the-art approaches, especially deep neural networks Learn where to obtain open-source datasets and software to jumpstart his or her own investigation

Computer Vision – ECCV 2016

Computer Vision – ECCV 2016
14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VI

by Bastian Leibe,Jiri Matas,Nicu Sebe,Max Welling

  • Publisher : Springer
  • Release : 2016-09-16
  • Pages : 887
  • ISBN : 3319464663
  • Language : En, Es, Fr & De
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The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physics-based vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action, activity and tracking; 3D; and 9 poster sessions.

Learning-Based Robot Vision

Learning-Based Robot Vision
Principles and Applications

by Josef Pauli

  • Publisher : Springer
  • Release : 2003-06-29
  • Pages : 292
  • ISBN : 3540451242
  • Language : En, Es, Fr & De
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Industrial robots carry out simple tasks in customized environments for which it is typical that nearly all e?ector movements can be planned during an - line phase. A continual control based on sensory feedback is at most necessary at e?ector positions near target locations utilizing torque or haptic sensors. It is desirable to develop new-generation robots showing higher degrees of autonomy for solving high-level deliberate tasks in natural and dynamic en- ronments. Obviously, camera-equipped robot systems, which take and process images and make use of the visual data, can solve more sophisticated robotic tasks. The development of a (semi-) autonomous camera-equipped robot must be grounded on an infrastructure, based on which the system can acquire and/or adapt task-relevant competences autonomously. This infrastructure consists of technical equipment to support the presentation of real world training samples, various learning mechanisms for automatically acquiring function approximations, and testing methods for evaluating the quality of the learned functions. Accordingly, to develop autonomous camera-equipped robot systems one must ?rst demonstrate relevant objects, critical situations, and purposive situation-action pairs in an experimental phase prior to the application phase. Secondly, the learning mechanisms are responsible for - quiring image operators and mechanisms of visual feedback control based on supervised experiences in the task-relevant, real environment. This paradigm of learning-based development leads to the concepts of compatibilities and manifolds. Compatibilities are general constraints on the process of image formation which hold more or less under task-relevant or accidental variations of the imaging conditions.

Artificial Intelligence for Maximizing Content Based Image Retrieval

Artificial Intelligence for Maximizing Content Based Image Retrieval
A Book

by Ma, Zongmin

  • Publisher : IGI Global
  • Release : 2009-01-31
  • Pages : 450
  • ISBN : 1605661759
  • Language : En, Es, Fr & De
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Discusses major aspects of content-based image retrieval (CBIR) using current technologies and applications within the artificial intelligence (AI) field.

Computer Analysis of Images and Patterns

Computer Analysis of Images and Patterns
18th International Conference, CAIP 2019, Salerno, Italy, September 3–5, 2019, Proceedings, Part II

by Mario Vento,Gennaro Percannella

  • Publisher : Springer Nature
  • Release : 2019-08-23
  • Pages : 596
  • ISBN : 3030298914
  • Language : En, Es, Fr & De
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The two volume set LNCS 11678 and 11679 constitutes the refereed proceedings of the 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019, held in Salerno, Italy, in September 2019. The 106 papers presented were carefully reviewed and selected from 176 submissions The papers are organized in the following topical sections: Intelligent Systems; Real-time and GPU Processing; Image Segmentation; Image and Texture Analysis; Machine Learning for Image and Pattern Analysis; Data Sets and Benchmarks; Structural and Computational Pattern Recognition; Posters.

Applied Cloud Deep Semantic Recognition

Applied Cloud Deep Semantic Recognition
Advanced Anomaly Detection

by Mehdi Roopaei,Peyman Najafirad (Paul Rad)

  • Publisher : CRC Press
  • Release : 2018-04-09
  • Pages : 188
  • ISBN : 135111901X
  • Language : En, Es, Fr & De
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This book provides a comprehensive overview of the research on anomaly detection with respect to context and situational awareness that aim to get a better understanding of how context information influences anomaly detection. In each chapter, it identifies advanced anomaly detection and key assumptions, which are used by the model to differentiate between normal and anomalous behavior. When applying a given model to a particular application, the assumptions can be used as guidelines to assess the effectiveness of the model in that domain. Each chapter provides an advanced deep content understanding and anomaly detection algorithm, and then shows how the proposed approach is deviating of the basic techniques. Further, for each chapter, it describes the advantages and disadvantages of the algorithm. The final chapters provide a discussion on the computational complexity of the models and graph computational frameworks such as Google Tensorflow and H2O because it is an important issue in real application domains. This book provides a better understanding of the different directions in which research has been done on deep semantic analysis and situational assessment using deep learning for anomalous detection, and how methods developed in one area can be applied in applications in other domains. This book seeks to provide both cyber analytics practitioners and researchers an up-to-date and advanced knowledge in cloud based frameworks for deep semantic analysis and advanced anomaly detection using cognitive and artificial intelligence (AI) models.

Deep Learning in Computer Vision

Deep Learning in Computer Vision
Principles and Applications

by Mahmoud Hassaballah,Ali Ismail Awad

  • Publisher : CRC Press
  • Release : 2020-03-23
  • Pages : 322
  • ISBN : 1351003801
  • Language : En, Es, Fr & De
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Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.