Dictionary Learning in Visual Computing
Dictionary Learning in Visual Computing

Author: Qiang Zhang

Publisher: Morgan & Claypool Publishers

Published: 2015-05-01

Total Pages: 153

ISBN-13: 1627057781

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The last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniques employing manually defined dictionaries, such as Fourier Transform and Wavelet Transform, dictionary learning aims at obtaining a dictionary adaptively from the data so as to support optimal sparse representation of the data. In contrast to conventional clustering algorithms like K-means, where a data point is associated with only one cluster center, in a dictionary-based representation, a data point can be associated with a small set of dictionary atoms. Thus, dictionary learning provides a more flexible representation of data and may have the potential to capture more relevant features from the original feature space of the data. One of the early algorithms for dictionary learning is K-SVD. In recent years, many variations/extensions of K-SVD and other new algorithms have been proposed, with some aiming at adding discriminative capability to the dictionary, and some attempting to model the relationship of multiple dictionaries. One prominent application of dictionary learning is in the general field of visual computing, where long-standing challenges have seen promising new solutions based on sparse representation with learned dictionaries. With a timely review of recent advances of dictionary learning in visual computing, covering the most recent literature with an emphasis on papers after 2008, this book provides a systematic presentation of the general methodologies, specific algorithms, and examples of applications for those who wish to have a quick start on this subject.

Dictionary Learning in Visual Computing
Dictionary Learning in Visual Computing

Author: Qiang Zhang

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 133

ISBN-13: 303102253X

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The last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniques employing manually defined dictionaries, such as Fourier Transform and Wavelet Transform, dictionary learning aims at obtaining a dictionary adaptively from the data so as to support optimal sparse representation of the data. In contrast to conventional clustering algorithms like K-means, where a data point is associated with only one cluster center, in a dictionary-based representation, a data point can be associated with a small set of dictionary atoms. Thus, dictionary learning provides a more flexible representation of data and may have the potential to capture more relevant features from the original feature space of the data. One of the early algorithms for dictionary learning is K-SVD. In recent years, many variations/extensions of K-SVD and other new algorithms have been proposed, with some aiming at adding discriminative capability to the dictionary, and some attempting to model the relationship of multiple dictionaries. One prominent application of dictionary learning is in the general field of visual computing, where long-standing challenges have seen promising new solutions based on sparse representation with learned dictionaries. With a timely review of recent advances of dictionary learning in visual computing, covering the most recent literature with an emphasis on papers after 2008, this book provides a systematic presentation of the general methodologies, specific algorithms, and examples of applications for those who wish to have a quick start on this subject.

Dictionary Learning Algorithms and Applications
Dictionary Learning Algorithms and Applications

Author: Bogdan Dumitrescu

Publisher: Springer

Published: 2018-04-16

Total Pages: 289

ISBN-13: 3319786741

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This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that are crucial for a successful program. Besides MOD, K-SVD, and other standard algorithms, it provides the significant dictionary learning problem variations, such as regularization, incoherence enforcing, finding an economical size, or learning adapted to specific problems like classification. Several types of dictionary structures are treated, including shift invariant; orthogonal blocks or factored dictionaries; and separable dictionaries for multidimensional signals. Nonlinear extensions such as kernel dictionary learning can also be found in the book. The discussion of all these dictionary types and algorithms is enriched with a thorough numerical comparison on several classic problems, thus showing the strengths and weaknesses of each algorithm. A few selected applications, related to classification, denoising and compression, complete the view on the capabilities of the presented dictionary learning algorithms. The book is accompanied by code for all algorithms and for reproducing most tables and figures. Presents all relevant dictionary learning algorithms - for the standard problem and its main variations - in detail and ready for implementation; Covers all dictionary structures that are meaningful in applications; Examines the numerical properties of the algorithms and shows how to choose the appropriate dictionary learning algorithm.

Computer Vision – ECCV 2012
Computer Vision – ECCV 2012

Author: Andrew Fitzgibbon

Publisher: Springer

Published: 2012-09-26

Total Pages: 909

ISBN-13: 3642337090

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The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.

Advances in Visual Computing
Advances in Visual Computing

Author: George Bebis

Publisher: Springer Nature

Published: 2021-12-02

Total Pages: 555

ISBN-13: 3030904369

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This two-volume set of LNCS 13017 and 13018 constitutes the refereed proceedings of the 16th International Symposium on Visual Computing, ISVC 2021, which was held in October 2021. The symposium took place virtually instead due to the COVID-19 pandemic. The 48 papers presented in these volumes were carefully reviewed and selected from 135 submissions. The papers are organized into the following topical sections: Part I: deep learning; computer graphics; segmentation; visualization; applications; 3D vision; virtual reality; motion and tracking; object detection and recognition. Part II: ST: medical image analysis; pattern recognition; video analysis and event recognition; posters.

Convolutional Neural Networks in Visual Computing
Convolutional Neural Networks in Visual Computing

Author: Ragav Venkatesan

Publisher: CRC Press

Published: 2017-10-23

Total Pages: 204

ISBN-13: 1351650327

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This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics.

Signal Processing and Machine Learning Theory
Signal Processing and Machine Learning Theory

Author: Paulo S.R. Diniz

Publisher: Elsevier

Published: 2023-07-10

Total Pages: 1236

ISBN-13: 032397225X

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Signal Processing and Machine Learning Theory, authored by world-leading experts, reviews the principles, methods and techniques of essential and advanced signal processing theory. These theories and tools are the driving engines of many current and emerging research topics and technologies, such as machine learning, autonomous vehicles, the internet of things, future wireless communications, medical imaging, etc. Provides quick tutorial reviews of important and emerging topics of research in signal processing-based tools Presents core principles in signal processing theory and shows their applications Discusses some emerging signal processing tools applied in machine learning methods References content on core principles, technologies, algorithms and applications Includes references to journal articles and other literature on which to build further, more specific, and detailed knowledge

Deep Learning through Sparse and Low-Rank Modeling
Deep Learning through Sparse and Low-Rank Modeling

Author: Zhangyang Wang

Publisher: Academic Press

Published: 2019-04-11

Total Pages: 296

ISBN-13: 012813660X

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Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models—those that emphasize problem-specific Interpretability—with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics. Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models Provides tactics on how to build and apply customized deep learning models for various applications

Computer Vision
Computer Vision

Author: Hongbin Zha

Publisher: Springer

Published: 2015-09-18

Total Pages: 491

ISBN-13: 3662485702

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The two volumes CCIS 546 and 547 constitute the refereed proceedings of the CCF Chinese Conference on Computer Vision, CCCV 2015, held in Xi'an, China, in September 2015. The total of 89 revised full papers presented in both volumes were carefully reviewed and selected from 176 submissions. The papers address issues such as computer vision, machine learning, pattern recognition, target recognition, object detection, target tracking, image segmentation, image restoration, face recognition, image classification.

Leveraging Computer Vision to Biometric Applications
Leveraging Computer Vision to Biometric Applications

Author: Arvind Selwal

Publisher: CRC Press

Published: 2024-10-07

Total Pages: 358

ISBN-13: 1040120563

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Computer vision is an effective solution in a diverse range of real-life applications. With the advent of the machine and deep learning paradigms, this book adopts machine and deep learning algorithms to leverage digital image processing for designing accurate biometrical applications. In this aspect, it presents the advancements made in computer vision to biometric applications design approach using emerging technologies. It discusses the challenges of designing efficient and accurate biometric-based systems, which is a key issue that can be tackled via computer vision-based techniques. Key Features • Discusses real-life applications of emerging techniques in computer vision systems • Offers solutions on real-time computer vision and biometrics applications to cater to the needs of current industry • Presents case studies to offer ideas for developing new biometrics-based products • Offers problem-based solutions in the field computer vision and real-time biometric applications for secured human authentication • Works as a ready resource for professionals and scholars working on emerging topics of computer vision for biometrics. The book is for academic researchers, scholars and students in Computer Science, Information Technology, Electronics and Electrical Engineering, Mechanical Engineering, management, academicians, researchers, scientists and industry people working on computer vision and biometrics applications.