Skip to main content
Elsevier
View Cart

Books in Computer vision and pattern recognition

BooksJournals

    • Imaging Genetics

      • 1st Edition
      • September 22, 2017
      • Adrian Dalca + 3 more
      • English
      • Paperback
        9 7 8 0 1 2 8 1 3 9 6 8 4
      • eBook
        9 7 8 0 1 2 8 1 3 9 6 9 1
      Imaging Genetics presents the latest research in imaging genetics methodology for discovering new associations between imaging and genetic variables, providing an overview of the state-of the-art in the field. Edited and written by leading researchers, this book is a beneficial reference for students and researchers, both new and experienced, in this growing area. The field of imaging genetics studies the relationships between DNA variation and measurements derived from anatomical or functional imaging data, often in the context of a disorder. While traditional genetic analyses rely on classical phenotypes like clinical symptoms, imaging genetics can offer richer insights into underlying, complex biological mechanisms.

    • Biomedical Texture Analysis

      • 1st Edition
      • August 25, 2017
      • Adrien Depeursinge + 2 more
      • English
      • Paperback
        9 7 8 0 1 2 8 1 2 1 3 3 7
      • eBook
        9 7 8 0 1 2 8 1 2 3 2 1 8
      Biomedical Texture Analysis: Fundamentals, Applications, Tools and Challenges describes the fundamentals and applications of biomedical texture analysis (BTA) for precision medicine. It defines what biomedical textures (BTs) are and why they require specific image analysis design approaches when compared to more classical computer vision applications. The fundamental properties of BTs are given to highlight key aspects of texture operator design, providing a foundation for biomedical engineers to build the next generation of biomedical texture operators. Examples of novel texture operators are described and their ability to characterize BTs are demonstrated in a variety of applications in radiology and digital histopathology. Recent open-source software frameworks which enable the extraction, exploration and analysis of 2D and 3D texture-based imaging biomarkers are also presented. This book provides a thorough background on texture analysis for graduate students and biomedical engineers from both industry and academia who have basic image processing knowledge. Medical doctors and biologists with no background in image processing will also find available methods and software tools for analyzing textures in medical images.

    • Low-Rank Models in Visual Analysis

      • 1st Edition
      • June 5, 2017
      • Zhouchen Lin + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 8 1 2 7 3 1 5
      • eBook
        9 7 8 0 1 2 8 1 2 7 3 2 2
      Low-Rank Models in Visual Analysis: Theories, Algorithms, and Applications presents the state-of-the-art on low-rank models and their application to visual analysis. It provides insight into the ideas behind the models and their algorithms, giving details of their formulation and deduction. The main applications included are video denoising, background modeling, image alignment and rectification, motion segmentation, image segmentation and image saliency detection. Readers will learn which Low-rank models are highly useful in practice (both linear and nonlinear models), how to solve low-rank models efficiently, and how to apply low-rank models to real problems.

    • Group and Crowd Behavior for Computer Vision

      • 1st Edition
      • April 10, 2017
      • Vittorio Murino + 3 more
      • English
      • Hardback
        9 7 8 0 1 2 8 0 9 2 7 6 7
      • eBook
        9 7 8 0 1 2 8 0 9 2 8 0 4
      Group and Crowd Behavior for Computer Vision provides a multidisciplinary perspective on how to solve the problem of group and crowd analysis and modeling, combining insights from the social sciences with technological ideas in computer vision and pattern recognition. The book answers many unresolved issues in group and crowd behavior, with Part One providing an introduction to the problems of analyzing groups and crowds that stresses that they should not be considered as completely diverse entities, but as an aggregation of people. Part Two focuses on features and representations with the aim of recognizing the presence of groups and crowds in image and video data. It discusses low level processing methods to individuate when and where a group or crowd is placed in the scene, spanning from the use of people detectors toward more ad-hoc strategies to individuate group and crowd formations. Part Three discusses methods for analyzing the behavior of groups and the crowd once they have been detected, showing how to extract semantic information, predicting/tracking the movement of a group, the formation or disaggregation of a group/crowd and the identification of different kinds of groups/crowds depending on their behavior. The final section focuses on identifying and promoting datasets for group/crowd analysis and modeling, presenting and discussing metrics for evaluating the pros and cons of the various models and methods. This book gives computer vision researcher techniques for segmentation and grouping, tracking and reasoning for solving group and crowd modeling and analysis, as well as more general problems in computer vision and machine learning.

    • Discrete-Time Neural Observers

      • 1st Edition
      • February 6, 2017
      • Alma Y Alanis + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 8 1 0 5 4 3 6
      • eBook
        9 7 8 0 1 2 8 1 0 5 4 4 3
      Discrete-Time Neural Observers: Analysis and Applications presents recent advances in the theory of neural state estimation for discrete-time unknown nonlinear systems with multiple inputs and outputs. The book includes rigorous mathematical analyses, based on the Lyapunov approach, that guarantee their properties. In addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. In order to complete the treatment of these schemes, the authors also present simulation and experimental results related to their application in meaningful areas, such as electric three phase induction motors and anaerobic process, which show the applicability of such designs. The proposed schemes can be employed for different applications beyond those presented. The book presents solutions for the state estimation problem of unknown nonlinear systems based on two schemes. For the first one, a full state estimation problem is considered; the second one considers the reduced order case with, and without, the presence of unknown delays. Both schemes are developed in discrete-time using recurrent high order neural networks in order to design the neural observers, and the online training of the respective neural networks is performed by Kalman Filtering.

    • Human Recognition in Unconstrained Environments

      • 1st Edition
      • January 9, 2017
      • Maria De Marsico + 2 more
      • English
      • Hardback
        9 7 8 0 0 8 1 0 0 7 0 5 1
      • eBook
        9 7 8 0 0 8 1 0 0 7 1 2 9
      Human Recognition in Unconstrained Environments provides a unique picture of the complete ‘in-the-wild’ biometric recognition processing chain; from data acquisition through to detection, segmentation, encoding, and matching reactions against security incidents. Coverage includes: Data hardware architecture fundamentals Background subtraction of humans in outdoor scenes Camera synchronization Biometric traits: Real-time detection and data segmentation Biometric traits: Feature encoding / matching Fusion at different levels Reaction against security incidents Ethical issues in non-cooperative biometric recognition in public spaces With this book readers will learn how to: Use computer vision, pattern recognition and machine learning methods for biometric recognition in real-world, real-time settings, especially those related to forensics and security Choose the most suited biometric traits and recognition methods for uncontrolled settings Evaluate the performance of a biometric system on real world data

    • Computing and Visualization for Intravascular Imaging and Computer-Assisted Stenting

      • 1st Edition
      • December 5, 2016
      • Simone Balocco + 4 more
      • English
      • Hardback
        9 7 8 0 1 2 8 1 1 0 1 8 8
      • eBook
        9 7 8 0 1 2 8 1 1 0 1 9 5
      Computing and Visualization for Intravascular Imaging and Computer-Assisted Stenting presents imaging, treatment, and computed assisted technological techniques for diagnostic and intraoperative vascular imaging and stenting. These techniques offer increasingly useful information on vascular anatomy and function, and are poised to have a dramatic impact on the diagnosis, analysis, modeling, and treatment of vascular diseases. After setting out the technical and clinical challenges of vascular imaging and stenting, the book gives a concise overview of the basics before presenting state-of-the-art methods for solving these challenges. Readers will learn about the main challenges in endovascular procedures, along with new applications of intravascular imaging and the latest advances in computer assisted stenting.

    • Multi-Dimensional Summarization in Cyber-Physical Society

      • 1st Edition
      • November 14, 2016
      • Hai Zhuge
      • English
      • Paperback
        9 7 8 0 1 2 8 0 3 4 5 5 2
      • eBook
        9 7 8 0 1 2 8 0 3 4 6 4 4
      Text summarization has been studied for over a half century, but traditional methods process texts empirically and neglect the fundamental characteristics and principles of language use and understanding. Automatic summarization is a desirable technique for processing big data. This reference summarizes previous text summarization approaches in a multi-dimensional category space, introduces a multi-dimensional methodology for research and development, unveils the basic characteristics and principles of language use and understanding, investigates some fundamental mechanisms of summarization, studies dimensions on representations, and proposes a multi-dimensional evaluation mechanism. Investigation extends to incorporating pictures into summary and to the summarization of videos, graphs and pictures, and converges to a general summarization method. Further, some basic behaviors of summarization are studied in the complex cyber-physical-socia... space. Finally, a creative summarization mechanism is proposed as an effort toward the creative summarization of things, which is an open process of interactions among physical objects, data, people, and systems in cyber-physical-socia... space through a multi-dimensional lens of semantic computing. The author’s insights can inspire research and development of many computing areas.

    • Learning-Based Local Visual Representation and Indexing

      • 1st Edition
      • March 23, 2015
      • Rongrong Ji + 4 more
      • English
      • Paperback
        9 7 8 0 1 2 8 0 2 4 0 9 6
      • eBook
        9 7 8 0 1 2 8 0 2 6 2 0 5
      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.

    • Emerging Trends in Image Processing, Computer Vision and Pattern Recognition

      • 1st Edition
      • December 9, 2014
      • Leonidas Deligiannidis + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 8 0 2 0 4 5 6
      • eBook
        9 7 8 0 1 2 8 0 2 0 9 2 0
      Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition discusses the latest in trends in imaging science which at its core consists of three intertwined computer science fields, namely: Image Processing, Computer Vision, and Pattern Recognition. There is significant renewed interest in each of these three fields fueled by Big Data and Data Analytic initiatives including but not limited to; applications as diverse as computational biology, biometrics, biomedical imaging, robotics, security, and knowledge engineering. These three core topics discussed here provide a solid introduction to image processing along with low-level processing techniques, computer vision fundamentals along with examples of applied applications and pattern recognition algorithms and methodologies that will be of value to the image processing and computer vision research communities. Drawing upon the knowledge of recognized experts with years of practical experience and discussing new and novel applications Editors’ Leonidas Deligiannidis and Hamid Arabnia cover; Many perspectives of image processing spanning from fundamental mathematical theory and sampling, to image representation and reconstruction, filtering in spatial and frequency domain, geometrical transformations, and image restoration and segmentation Key application techniques in computer vision some of which are camera networks and vision, image feature extraction, face and gesture recognition and biometric authentication Pattern recognition algorithms including but not limited to; Supervised and unsupervised classification algorithms, Ensemble learning algorithms, and parsing algorithms. How to use image processing and visualization to analyze big data.

Related subjects

Document and text processing