Sparse Modeling For Image And Vision Processing PDF Download
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Author | : Julien Mairal |
Publisher | : Now Publishers |
Total Pages | : 216 |
Release | : 2014-12-19 |
Genre | : Computers |
ISBN | : 9781680830088 |
Download Sparse Modeling for Image and Vision Processing Book in PDF, ePub and Kindle
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.
Author | : Jayaraman J. Thiagarajan |
Publisher | : Springer Nature |
Total Pages | : 115 |
Release | : 2022-06-01 |
Genre | : Technology & Engineering |
ISBN | : 3031022505 |
Download Image Understanding using Sparse Representations Book in PDF, ePub and Kindle
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.
Author | : Yun Fu |
Publisher | : Springer |
Total Pages | : 240 |
Release | : 2014-10-30 |
Genre | : Computers |
ISBN | : 331912000X |
Download Low-Rank and Sparse Modeling for Visual Analysis Book in PDF, ePub and Kindle
This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applications.
Author | : Jayaraman Jayaraman Thiagarajan |
Publisher | : |
Total Pages | : 244 |
Release | : 2013 |
Genre | : Computer vision |
ISBN | : |
Download Sparse Methods in Image Understanding and Computer Vision Book in PDF, ePub and Kindle
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.
Author | : Zhangyang Wang |
Publisher | : Academic Press |
Total Pages | : 296 |
Release | : 2019-04-11 |
Genre | : Computers |
ISBN | : 012813660X |
Download Deep Learning through Sparse and Low-Rank Modeling Book in PDF, ePub and Kindle
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
Author | : Yue Zhang |
Publisher | : |
Total Pages | : 154 |
Release | : 2018 |
Genre | : Image processing |
ISBN | : |
Download Sparsity in Image Processing and Machine Learning Book in PDF, ePub and Kindle
Natural images are usually either sparse by themselves or sparse after a transform. For instance, images of stars from telescope are sparse by themselves and images of humans are sparse under the wavelet transform. Sparsity plays an important role in image processing and machine learning. How to build appropriate sparse models, how to numerically find solutions from the sparse models and how to derive theoretical guarantees of the correctness of the solutions are essential for the success of sparsity in applications. In this thesis, we study sparse modeling in image processing and machine learning. We propose a novel two-stage (projection followed by correction) modeling framework for image reconstruction from nonuniform Fourier measurements. We design domain adaptive sparsity regularization schemes that are able to maintain image smoothness as well as edge details. Various numerical results in both one-dimensional signals and two-dimensional images are also presented to demonstrate the superior performance of the proposed model compared with other methods. We study the problem of learning data adaptive over complete bases so that each data example can be linearly represented by few basis vectors. In the specific case when the input data size is large, we design a novel distributed dictionary learning algorithm which builds on the classical consensus approach. Most importantly, the proposed algorithm imposes a structured constraint to ensure that the derived dictionary has low coherence, that is, the columns of the dictionaries are nearly orthogonal. We analyze the solution obtained from the well known Least Absolute Shrinkage and Selection Operator (Lasso) model in the feature selection scenario. A substantial challenge in feature selection to specify the relation between the factors/features and the observations (e.g., linear or nonlinear). The relation is however often unknown. We prove that under certain conditions, Lasso is able to find the correct features (asymptotically) even though the model is misspecified. The contributions of this thesis are summarized below: 1. We propose a new two-stage model with domain adaptive sparsity constraints for image reconstruction from nonuniform Fourier measurements. 2. We design a new distributed dictionary learning algorithm. 3. We prove a new theorem on the correctness of Lasso model in sparse feature selection when the relationship between observations and features is misspecified.
Author | : Hong Cheng |
Publisher | : Springer |
Total Pages | : 259 |
Release | : 2015-05-25 |
Genre | : Computers |
ISBN | : 1447167147 |
Download Sparse Representation, Modeling and Learning in Visual Recognition Book in PDF, ePub and Kindle
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.
Author | : Michael Elad |
Publisher | : Springer Science & Business Media |
Total Pages | : 376 |
Release | : 2010-08-12 |
Genre | : Mathematics |
ISBN | : 1441970118 |
Download Sparse and Redundant Representations Book in PDF, ePub and Kindle
A long long time ago, echoing philosophical and aesthetic principles that existed since antiquity, William of Ockham enounced the principle of parsimony, better known today as Ockham’s razor: “Entities should not be multiplied without neces sity. ” This principle enabled scientists to select the ”best” physical laws and theories to explain the workings of the Universe and continued to guide scienti?c research, leadingtobeautifulresultsliketheminimaldescriptionlength approachtostatistical inference and the related Kolmogorov complexity approach to pattern recognition. However, notions of complexity and description length are subjective concepts anddependonthelanguage“spoken”whenpresentingideasandresults. The?eldof sparse representations, that recently underwent a Big Bang like expansion, explic itly deals with the Yin Yang interplay between the parsimony of descriptions and the “language” or “dictionary” used in them, and it became an extremely exciting area of investigation. It already yielded a rich crop of mathematically pleasing, deep and beautiful results that quickly translated into a wealth of practical engineering applications. You are holding in your hands the ?rst guide book to Sparseland, and I am sure you’ll ?nd in it both familiar and new landscapes to see and admire, as well as ex cellent pointers that will help you ?nd further valuable treasures. Enjoy the journey to Sparseland! Haifa, Israel, December 2009 Alfred M. Bruckstein vii Preface This book was originally written to serve as the material for an advanced one semester (fourteen 2 hour lectures) graduate course for engineering students at the Technion, Israel.
Author | : Ron Rubinstein |
Publisher | : |
Total Pages | : 208 |
Release | : 2011 |
Genre | : |
ISBN | : |
Download Analysis and Synthesis Sparse Modeling Methods Image Processing Book in PDF, ePub and Kindle
Author | : Jean-Daniel Boissonnat |
Publisher | : Springer |
Total Pages | : 758 |
Release | : 2012-01-06 |
Genre | : Computers |
ISBN | : 3642274137 |
Download Curves and Surfaces Book in PDF, ePub and Kindle
This volume constitutes the thoroughly refereed post-conference proceedings of the 7th International Conference on Curves and Surfaces, held in Avignon, in June 2010. The conference had the overall theme: "Representation and Approximation of Curves and Surfaces and Applications". The 39 revised full papers presented together with 9 invited talks were carefully reviewed and selected from 114 talks presented at the conference. The topics addressed by the papers range from mathematical foundations to practical implementation on modern graphics processing units and address a wide area of topics such as computer-aided geometric design, computer graphics and visualisation, computational geometry and topology, geometry processing, image and signal processing, interpolation and smoothing, scattered data processing and learning theory and subdivision, wavelets and multi-resolution methods.