Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more. Rather than providing ‘how-to’ source code examples and shortcuts, this book provides a counterpoint discussion to the many fine opencv community source code resources available for hands-on practitioners.

Person-Centred Healthcare Research Person-Centred Healthcare Research provides an innovative and novel approach to exploring a range of research designs and methodological approaches aimed at investigating person-centred healthcare practice within and across healthcare disciplines. With contributions from internationally renowned experts in the field, this engaging resource challenges existing research and development methodologies and their relevance to advancing person-centred knowledge generation, dissemination, translation, implementation and use. It also explores new developments in research methods and practices that open up new avenues for advancing the field of person-centred practice. Person-Centred Healthcare Research: Enables students, practitioners, managers and researchers to gain a solid understanding of the complexity of person-centred thinking in research designs and methods Explores the theories and practices underpinning a topical subject within current healthcare practice Is edited by an internationally recognised team who are at the forefront of person-centred healthcare research For more information on the complete range of Wiley nursing publishing, please visit: www.wileynursing.com To receive automatic updates on Wiley books and journals, join our email list. Sign up today at www.wiley.com/email This new title is also available as an e-book. For more details, please see www.wiley.com/buy/9781119099604

This third volume, with three supporting editors, broadens its focus on genetic eye research from the Asian to the global scale. New efforts and a new awareness have sparked important discussions on genetic eye research, and new plans are being implemented to identify the genes responsible for numerous eye diseases. The book introduces the latest findings on genetics in eye diseases, gene therapy, and genome-wide association analysis, and the efforts of the Global Eye Genetic Consortium (GEGC). The book’s editors have been instrumental in developing strategies for discovering the new genes involved in many eye diseases. All chapters were written by leading researchers working on eye genetics from the fields of Human Genetics, Ophthalmology, Molecular Biology, Biochemistry, Sensory Sciences, and Clinical Research. Advances in Vision Research, Volume III is a major resource for all researchers, clinicians, clinical researchers, and allied eye health professionals with an interest in eye diseases around the globe.

The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.