Download or read book Sparse Coding and Its Applications in Computer Vision written by Zhaowen E. T. Al WANG and published by World Scientific. This book was released on 2015-10-28 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides a broader introduction to the theories and applications of sparse coding techniques in computer vision research. It introduces sparse coding in the context of representation learning, illustrates the fundamental concepts, and summarizes the most active research directions. A variety of applications of sparse coding are discussed, ranging from low-level image processing tasks such as super-resolution and de-blurring to high-level semantic understanding tasks such as image recognition, clustering and fusion. The book is suitable to be used as an introductory overview to this field, with its theoretical part being both easy and precious enough for quick understanding. It is also of great value to experienced researchers as it offers new perspective to the underlying mechanism of sparse coding, and points out potential future directions for different applications."--

Download or read book Interpretable Machine Learning and Sparse Coding for Computer Vision written by and published by . This book was released on 2014 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning offers many powerful tools for prediction. One of these tools, the binary classifier, is often considered a black box. Although its predictions may be accurate, we might never know why the classifier made a particular prediction. In the first half of this dissertation, I review the state of the art of interpretable methods (methods for explaining why); after noting where the existing methods fall short, I propose a new method for a particular type of black box called additive networks. I offer a proof of trustworthiness for this new method (meaning a proof that my method does not "make up" the logic of the black box when generating an explanation), and verify that its explanations are sound empirically. Sparse coding is part of a family of methods that are believed, by many researchers, to not be black boxes. In the second half of this dissertation, I review sparse coding and its application to the binary classifier. Despite the fact that the goal of sparse coding is to reconstruct data (an entirely different goal than classification), many researchers note that it improves classification accuracy. I investigate this phenomenon, challenging a common assumption in the literature. I show empirically that sparse reconstruction is not necessarily the right intermediate goal, when our ultimate goal is classification. Along the way, I introduce a new sparse coding algorithm that outperforms competing, state-of-the-art algorithms for a variety of important tasks.

Download or read book Sparse Modeling for Image and Vision Processing written by Julien Mairal and published by Now Publishers. This book was released on 2014-12-19 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sparse Modeling for Image and Vision Processing offers a self-contained view of sparse modeling for visual recognition and image processing. More specifically, it focuses on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.

Download or read book Computer Vision ACCV 2010 written by Ron Kimmel and published by Springer. This book was released on 2011-02-28 with total page 747 pages. Available in PDF, EPUB and Kindle. Book excerpt: The four-volume set LNCS 6492-6495 constitutes the thoroughly refereed post-proceedings of the 10th Asian Conference on Computer Vision, ACCV 2009, held in Queenstown, New Zealand in November 2010. All together the four volumes present 206 revised papers selected from a total of 739 Submissions. All current issues in computer vision are addressed ranging from algorithms that attempt to automatically understand the content of images, optical methods coupled with computational techniques that enhance and improve images, and capturing and analyzing the world's geometry while preparing the higher level image and shape understanding. Novel geometry techniques, statistical learning methods, and modern algebraic procedures are dealt with as well.

Download or read book Computer Vision ECCV 2010 written by Kostas Daniilidis and published by Springer Science & Business Media. This book was released on 2010-08-30 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six-volume set comprising LNCS volumes 6311 until 6313 constitutes the refereed proceedings of the 11th European Conference on Computer Vision, ECCV 2010, held in Heraklion, Crete, Greece, in September 2010. The 325 revised papers presented were carefully reviewed and selected from 1174 submissions. The papers are organized in topical sections on object and scene recognition; segmentation and grouping; face, gesture, biometrics; motion and tracking; statistical models and visual learning; matching, registration, alignment; computational imaging; multi-view geometry; image features; video and event characterization; shape representation and recognition; stereo; reflectance, illumination, color; medical image analysis.

Download or read book Computer Vision ECCV 2012 written by Andrew Fitzgibbon and published by Springer. This book was released on 2012-09-26 with total page 909 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Image Understanding using Sparse Representations written by Jayaraman J. Thiagarajan and published by Springer Nature. This book was released on 2022-06-01 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification. The primary goal of this book is to present the theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms for obtaining sparse representations and their performance guarantees are discussed in the initial chapters. Furthermore, approaches for designing overcomplete, data-adapted dictionaries to model natural images are described. The development of theory behind dictionary learning involves exploring its connection to unsupervised clustering and analyzing its generalization characteristics using principles from statistical learning theory. An exciting application area that has benefited extensively from the theory of sparse representations is compressed sensing of image and video data. Theory and algorithms pertinent to measurement design, recovery, and model-based compressed sensing are presented. The paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications such as object recognition, clustering, segmentation, and activity recognition. Frameworks that enhance the performance of sparse models in such applications by imposing constraints based on the prior discriminatory information and the underlying geometrical structure, and kernelizing the sparse coding and dictionary learning methods are presented. In addition to presenting theoretical fundamentals in sparse learning, this book provides a platform for interested readers to explore the vastly growing application domains of sparse representations.

Download or read book Sparse Modeling for Image and Vision Processing written by Julien Mairal and published by . This book was released on 2014 with total page 3 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection -- that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.

Download or read book Sparse Representation Modeling and Learning in Visual Recognition written by Hong Cheng and published by Springer. This book was released on 2015-05-25 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: describes sparse recovery approaches, robust and efficient sparse representation, and large-scale visual recognition; covers feature representation and learning, sparsity induced similarity, and sparse representation and learning-based classifiers; discusses low-rank matrix approximation, graphical models in compressed sensing, collaborative representation-based classification, and high-dimensional nonlinear learning; includes appendices outlining additional computer programming resources, and explaining the essential mathematics required to understand the book.

Download or read book Emerging Topics in Computer Vision and Its Applications written by C. H. Chen and published by World Scientific. This book was released on 2012 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives a comprehensive overview of the most advanced theories, methodologies and applications in computer vision. Particularly, it gives an extensive coverage of 3D and robotic vision problems. Example chapters featured are Fourier methods for 3D surface modeling and analysis, use of constraints for calibration-free 3D Euclidean reconstruction, novel photogeometric methods for capturing static and dynamic objects, performance evaluation of robot localization methods in outdoor terrains, integrating 3D vision with force/tactile sensors, tracking via in-floor sensing, self-calibration of camera networks, etc. Some unique applications of computer vision in marine fishery, biomedical issues, driver assistance, are also highlighted.

Download or read book Handbook of Pattern Recognition and Computer Vision written by C. H. Chen and published by World Scientific. This book was released on 1999 with total page 1045 pages. Available in PDF, EPUB and Kindle. Book excerpt: The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference.

Download or read book Sparse Methods in Image Understanding and Computer Vision written by Jayaraman Jayaraman Thiagarajan and published by . This book was released on 2013 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire data space and not just the samples considered. By exploiting the relation of dictionary learning to 1-D subspace clustering, a multilevel dictionary learning algorithm is developed, and it is shown to outperform conventional sparse models in compressed recovery, and image denoising. Theoretical aspects of learning such as algorithmic stability and generalization are considered, and ensemble learning is incorporated for effective large scale learning. In addition to building strategies for efficiently implementing 1-D subspace clustering, a discriminative clustering approach is designed to estimate the unknown mixing process in blind source separation. By exploiting the non-linear relation between the image descriptors, and allowing the use of multiple features, sparse methods can be made more effective in recognition problems. The idea of multiple kernel sparse representations is developed, and algorithms for learning dictionaries in the feature space are presented. Using object recognition experiments on standard datasets it is shown that the proposed approaches outperform other sparse coding-based recognition frameworks. Furthermore, a segmentation technique based on multiple kernel sparse representations is developed, and successfully applied for automated brain tumor identification. Using sparse codes to define the relation between data samples can lead to a more robust graph embedding for unsupervised clustering. By performing discriminative embedding using sparse coding-based graphs, an algorithm for measuring the glomerular number in kidney MRI images is developed. Finally, approaches to build dictionaries for local sparse coding of image descriptors are presented, and applied to object recognition and image retrieval.

Download or read book Compressed Sensing and its Applications written by Holger Boche and published by Birkhäuser. This book was released on 2018-01-17 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: This contributed volume contains articles written by the plenary and invited speakers from the second international MATHEON Workshop 2015 that focus on applications of compressed sensing. Article authors address their techniques for solving the problems of compressed sensing, as well as connections to related areas like detecting community-like structures in graphs, curbatures on Grassmanians, and randomized tensor train singular value decompositions. Some of the novel applications covered include dimensionality reduction, information theory, random matrices, sparse approximation, and sparse recovery. This book is aimed at both graduate students and researchers in the areas of applied mathematics, computer science, and engineering, as well as other applied scientists exploring the potential applications for the novel methodology of compressed sensing. An introduction to the subject of compressed sensing is also provided for researchers interested in the field who are not as familiar with it.

Download or read book Biologically Inspired Computer Vision written by Gabriel Cristobal and published by John Wiley & Sons. This book was released on 2015-08-20 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the state-of-the-art imaging technologies became more and more advanced, yielding scientific data at unprecedented detail and volume, the need to process and interpret all the data has made image processing and computer vision increasingly important. Sources of data that have to be routinely dealt with today's applications include video transmission, wireless communication, automatic fingerprint processing, massive databanks, non-weary and accurate automatic airport screening, robust night vision, just to name a few. Multidisciplinary inputs from other disciplines such as physics, computational neuroscience, cognitive science, mathematics, and biology will have a fundamental impact in the progress of imaging and vision sciences. One of the advantages of the study of biological organisms is to devise very different type of computational paradigms by implementing a neural network with a high degree of local connectivity. This is a comprehensive and rigorous reference in the area of biologically motivated vision sensors. The study of biologically visual systems can be considered as a two way avenue. On the one hand, biological organisms can provide a source of inspiration for new computational efficient and robust vision models and on the other hand machine vision approaches can provide new insights for understanding biological visual systems. Along the different chapters, this book covers a wide range of topics from fundamental to more specialized topics, including visual analysis based on a computational level, hardware implementation, and the design of new more advanced vision sensors. The last two sections of the book provide an overview of a few representative applications and current state of the art of the research in this area. This makes it a valuable book for graduate, Master, PhD students and also researchers in the field.

Download or read book Sparse Representations and Compressive Sensing for Imaging and Vision written by Vishal M. Patel and published by Springer Science & Business Media. This book was released on 2013-02-11 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: Compressed sensing or compressive sensing is a new concept in signal processing where one measures a small number of non-adaptive linear combinations of the signal. These measurements are usually much smaller than the number of samples that define the signal. From these small numbers of measurements, the signal is then reconstructed by non-linear procedure. Compressed sensing has recently emerged as a powerful tool for efficiently processing data in non-traditional ways. In this book, we highlight some of the key mathematical insights underlying sparse representation and compressed sensing and illustrate the role of these theories in classical vision, imaging and biometrics problems.

Download or read book Computer Vision ACCV 2022 written by Lei Wang and published by Springer Nature. This book was released on 2023-03-03 with total page 687 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 7-volume set of LNCS 13841-13847 constitutes the proceedings of the 16th Asian Conference on Computer Vision, ACCV 2022, held in Macao, China, December 2022. The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; optimization methods; Part II: applications of computer vision, vision for X; computational photography, sensing, and display; Part III: low-level vision, image processing; Part IV: face and gesture; pose and action; video analysis and event recognition; vision and language; biometrics; Part V: recognition: feature detection, indexing, matching, and shape representation; datasets and performance analysis; Part VI: biomedical image analysis; deep learning for computer vision; Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods.

Download or read book Artificial Intelligence Evolutionary Computing and Metaheuristics written by Xin-She Yang and published by Springer. This book was released on 2012-07-27 with total page 797 pages. Available in PDF, EPUB and Kindle. Book excerpt: Alan Turing pioneered many research areas such as artificial intelligence, computability, heuristics and pattern formation. Nowadays at the information age, it is hard to imagine how the world would be without computers and the Internet. Without Turing's work, especially the core concept of Turing Machine at the heart of every computer, mobile phone and microchip today, so many things on which we are so dependent would be impossible. 2012 is the Alan Turing year -- a centenary celebration of the life and work of Alan Turing. To celebrate Turing's legacy and follow the footsteps of this brilliant mind, we take this golden opportunity to review the latest developments in areas of artificial intelligence, evolutionary computation and metaheuristics, and all these areas can be traced back to Turing's pioneer work. Topics include Turing test, Turing machine, artificial intelligence, cryptography, software testing, image processing, neural networks, nature-inspired algorithms such as bat algorithm and cuckoo search, and multiobjective optimization and many applications. These reviews and chapters not only provide a timely snapshot of the state-of-art developments, but also provide inspiration for young researchers to carry out potentially ground-breaking research in the active, diverse research areas in artificial intelligence, cryptography, machine learning, evolutionary computation, and nature-inspired metaheuristics. This edited book can serve as a timely reference for graduates, researchers and engineers in artificial intelligence, computer sciences, computational intelligence, soft computing, optimization, and applied sciences.