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检索条件"丛书名=Synthesis Lectures on Computer Vision"

共 41 条记录，以下是31-40 订阅

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Boosting-Based Face Detection and Adaptation 1

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丛书名： synthesis lectures on computer vision

1000年

作者： Cha Zhang

ISBN: (数字)9783031018091

ISBN: (纸本)9783031006814

Face detection, because of its vast array of applications, is one of the most active research areas in computer vision. In this book, we review various approaches to face detection developed in the past decade, with more emphasis on boosting-based learning algorithms. We then present a series of algorithms that are empowered by the statistical view of boosting and the concept of multiple instance learning. We start by describing a boosting learning framework that is capable to handle billions of training examples. It differs from traditional bootstrapping schemes in that no intermediate thresholds need to be set during training, yet the total number of negative examples used for feature selection remains constant and focused (on the poor performing ones). A multiple instance pruning scheme is then adopted to set the intermediate thresholds after boosting learning. This algorithm generates detectors that are both fast and accurate. We then present two multiple instance learning schemesfor face detection, multiple instance learning boosting (MILBoost) and winner-take-all multiple category boosting (WTA-McBoost). MILBoost addresses the uncertainty in accurately pinpointing the location of the object being detected, while WTA-McBoost addresses the uncertainty in determining the most appropriate subcategory label for multiview object detection. Both schemes can resolve the ambiguity of the labeling process and reduce outliers during training, which leads to improved detector performances. In many applications, a detector trained with generic data sets may not perform optimally in a new environment. We propose detection adaption, which is a promising solution for this problem. We present an adaptation scheme based on the Taylor expansion of the boosting learning objective function, and we propose to store the second order statistics of the generic training data for future adaptation. We show that with a small amount of labeled data in the new environment, the detector'spe

关键词： computer Imaging, vision, Pattern Recognition and Graphics Image Processing and computer vision Pattern Recognition

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Structured Representation Learning 1

Structured Representation Learning

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丛书名： synthesis lectures on computer vision

1000年

作者： Yue Song Thomas Anderson Keller Nicu Sebe Max Welling

ISBN: (数字)9783031881114

ISBN: (纸本)9783031881107;9783031881138

This book introduces approaches to generalize the benefits of equivariant deep learning to a broader set of learned structures through learned homomorphisms. In the field of machine learning, the idea of incorporating knowledge of data symmetries into artificial neural networks is known as equivariant deep learning and has led to the development of cutting edge architectures for image and physical data processing. The power of these models originates from data-specific structures ingrained in them through careful engineering. To-date however, the ability for practitioners to build such a structure into models is limited to situations where the data must exactly obey specific mathematical symmetries. The authors discuss naturally inspired inductive biases, specifically those which may provide types of efficiency and generalization benefits through what are known as homomorphic representations, a new general type of structured representation inspired from techniques in physics and neuroscience. A review of some of the first attempts at building models with learned homomorphic representations are introduced. The authors demonstrate that these inductive biases improve the ability of models to represent natural transformations and ultimately pave the way to the future of efficient and effective artificial neural networks.

关键词： Machine Learning Artificial Intelligence Image Processing and computer vision computer Imaging, vision, Pattern Recognition and Graphics Mathematical Models of Cognitive Processes and Neural Networks computer Science, general

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Probabilistic and Biologically Inspired Feature Representations 1

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丛书名： synthesis lectures on computer vision

1000年

作者： Michael Felsberg

ISBN: (数字)9783031018220

ISBN: (纸本)9783031006944

Under the title;this text collects a substantial amount of work on the topic of channel representations. Channel representations are a biologically motivated, wavelet-like approach to visual feature descriptors: they are local and compact, they form a computational framework, and the represented information can be reconstructed. The first property is shared with many histogram- and signature-based descriptors, the latter property with the related concept of population codes. In their unique combination of properties, channel representations become a visual Swiss army knife—they can be used for image enhancement, visual object tracking, as 2D and 3D descriptors, and for pose estimation. In the chapters of this text, the framework of channel representations will be introduced and its attributes will be elaborated, as well as further insight into its probabilistic modeling and algorithmic implementation will be given. Channel representations are a useful toolbox to represent visual information for machine learning, as they establish a generic way to compute popular descriptors such as HOG, SIFT, and SHOT. Even in an age of deep learning, they provide a good compromise between hand-designed descriptors and a-priori structureless feature spaces as seen in the layers of deep networks.

关键词： computer Imaging, vision, Pattern Recognition and Graphics Image Processing and computer vision Pattern Recognition

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Multi-Modal Face Presentation Attack Detection 1

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丛书名： synthesis lectures on computer vision

1000年

作者： Jun Wan Guodong Guo Sergio Escalera Hugo Jair Escalante Stan Z. Li

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A Unifying Framework for Formal Theories of Novelty 1

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丛书名： synthesis lectures on computer vision

1000年

作者： Terrance Boult Walter Scheirer

ISBN: (数字)9783031330544

ISBN: (纸本)9783031330537;9783031330568

This book presents the first unified formalization for defining novelty across the span of machine learning, symbolic-reasoning, and control and planning-based systems. Dealing with novelty, things not previously seen by a system, is a critical issue for building vision-systems and general intelligent systems. The book presents examples of using this framework to define and evaluate in multiple domains including image recognition image-based open world learning, hand-writing and author analysis, CartPole Control, Image Captioning, and Monopoly. Chapters are written by well-known contributors to this new and emerging field. In addition, examples are provided from multiple areas, such as machine-learning based control problems, symbolic reasoning, and multi-player games.

关键词： Image Processing and computer vision Machine Learning computer Imaging, vision, Pattern Recognition and Graphics Pattern Recognition Signal, Image and Speech Processing Artificial Intelligence

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Extreme Value Theory-Based Methods for Visual Recognition 1

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丛书名： synthesis lectures on computer vision

1000年

作者： Walter J. Scheirer

ISBN: (数字)9783031018176

ISBN: (纸本)9783031006890

A common feature of many approaches to modeling sensory statistics is an emphasis on capturing the "average." From early representations in the brain, to highly abstracted class categories in machine learning for classification tasks, central-tendency models based on the Gaussian distribution are a seemingly natural and obvious choice for modeling sensory data. However, insights from neuroscience, psychology, and computer vision suggest an alternate strategy: preferentially focusing representational resources on the extremes of the distribution of sensory inputs. The notion of treating extrema near a decision boundary as features is not necessarily new, but a comprehensive statistical theory of recognition based on extrema is only now just emerging in the computer vision literature. This book begins by introducing the statistical Extreme Value Theory (EVT) for visual recognition. In contrast to central-tendency modeling, it is hypothesized that distributions near decision boundaries form a more powerful model for recognition tasks by focusing coding resources on data that are arguably the most diagnostic features. EVT has several important properties: strong statistical grounding, better modeling accuracy near decision boundaries than Gaussian modeling, the ability to model asymmetric decision boundaries, and accurate prediction of the probability of an event beyond our experience. The second part of the book uses the theory to describe a new class of machine learning algorithms for decision making that are a measurable advance beyond the state-of-the-art. This includes methods for post-recognition score analysis, information fusion, multi-attribute spaces, and calibration of supervised machine learning algorithms.

关键词： computer Imaging, vision, Pattern Recognition and Graphics Image Processing and computer vision Pattern Recognition

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Fine-Grained Image Analysis: Modern Approaches 1

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丛书名： synthesis lectures on computer vision

1000年

作者： Xiu-Shen Wei

ISBN: (数字)9783031313745

ISBN: (纸本)9783031313738;9783031313769

This book provides a comprehensive overview of the fine-grained image analysis research and modern approaches based on deep learning, spanning the full range of topics needed for designing operational fine-grained image systems. The author begins by providing detailed background information on FGIA, focusing on recognition and retrieval. The author also provides the fundamentals of convolutional neural networks to further make it easier for readers to understand the technical content in the book. The book introduces the main technical paradigms, technological developments, and representative approaches of fine-grained image recognition and fine-grained image retrieval. The author covers multiple popular research topics and includes cross-domain knowledge. The book also highlights advanced applications and topics for future research.

关键词： Image Processing and computer vision computer Imaging, vision, Pattern Recognition and Graphics Signal, Image and Speech Processing Pattern Recognition Machine Learning Artificial Intelligence

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Image-Based Modeling of Plants and Trees 1

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丛书名： synthesis lectures on computer vision

1000年

作者： Sing Bing Kang Long Quan

ISBN: (数字)9783031018084

ISBN: (纸本)9783031006807

Plants and trees are among the most complex natural objects. Much work has been done attempting to model them, with varying degrees of success. In this book, we review the various approaches in computer graphics, which we categorize as rule-based, image-based, and sketch-based methods. We describe our approaches for modeling plants and trees using images. Image-based approaches have the distinct advantage that the resulting model inherits the realistic shape and complexity of a real plant or tree. We use different techniques for modeling plants (with relatively large leaves) and trees (with relatively small leaves).With plants, we model each leaf from images, while for trees, the leaves are only approximated due to their small size and large number. Both techniques start with the same initial step of structure from motion on multiple images of the plant or tree that is to be modeled. For our plant modeling system, because we need to model the individual leaves, these leaves need to besegmented out from the images. We designed our plant modeling system to be interactive, automating the process of shape recovery while relying on the user to provide simple hints on segmentation. Segmentation is performed in both image and 3D spaces, allowing the user to easily visualize its effect immediately. Using the segmented image and 3D data, the geometry of each leaf is then automatically recovered from the multiple views by fitting a deformable leaf model. Our system also allows the user to easily reconstruct branches in a similar manner. To model trees, because of the large leaf count, small image footprint, and widespread occlusions, we do not model the leaves exactly as we do for plants. Instead, we populate the tree with leaf replicas from segmented source images to reconstruct the overall tree shape. In addition, we use the shape patterns of visible branches to predict those of obscured branches. As a result, we are able to design our tree modeling system so as to minimize

关键词： computer Imaging, vision, Pattern Recognition and Graphics Image Processing and computer vision Pattern Recognition

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The Maximum Consensus Problem 1

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丛书名： synthesis lectures on computer vision

1000年

作者： Tat-Jun Chin David Suter

ISBN: (数字)9783031018183

ISBN: (纸本)9783031006906

Outlier-contaminated data is a fact of life in computer vision. For computer vision applications to perform reliably and accurately in practical settings, the processing of the input data must be conducted in a robust manner. In this context, the maximum consensus robust criterion plays a critical role by allowing the quantity of interest to be estimated from noisy and outlier-prone visual measurements. The;refers to the problem of optimizing the quantity of interest according to the maximum consensus criterion. This book provides an overview of the algorithms for performing this optimization. The emphasis is on the basic operation or "inner workings" of the algorithms, and on their mathematical characteristics in terms of optimality and efficiency. The applicability of the techniques to common computer vision tasks is also highlighted. By collecting existing techniques in a single article, this book aims to trigger further developments in this theoretically interesting and practically important area.

关键词： computer Imaging, vision, Pattern Recognition and Graphics Image Processing and computer vision Pattern Recognition

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Computational Texture and Patterns 1

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丛书名： synthesis lectures on computer vision

1000年

作者： Kristin J. Dana

ISBN: (数字)9783031018237

ISBN: (纸本)9783031006951

Visual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn about the physical world. Our ability to capture visual imagery with cameras and sensors has resulted in vast amounts of raw data, but using this information effectively in a task-specific manner requires sophisticated computational representations. We enumerate specific desirable traits for these representations: (1) intraclass invariance—to support recognition; (2) illumination and geometric invariance for robustness to imaging conditions; (3) support for prediction and synthesis to use the model to infer continuation of the pattern; (4) support for change detection to detect anomalies and perturbations; and (5) support for physics-based interpretation to infer system properties from appearance. In recent years, computer vision has undergone a metamorphosis with classic algorithms adaptingto new trends in deep learning. This text provides a tour of algorithm evolution including pattern recognition, segmentation and synthesis. We consider the general relevance and prominence of visual pattern analysis and applications that rely on computational models.

关键词： computer Imaging, vision, Pattern Recognition and Graphics Image Processing and computer vision Pattern Recognition

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