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.

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.

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 ability to see deeply affects how human beings perceive and interpret the world around them. For most people, eyesight is part of everyday communication, social activities, educational and professional pursuits, the care of others, and the maintenance of personal health, independence, and mobility. Functioning eyes and vision system can reduce an adult's risk of chronic health conditions, death, falls and injuries, social isolation, depression, and other psychological problems. In children, properly maintained eye and vision health contributes to a child's social development, academic achievement, and better health across the lifespan. The public generally recognizes its reliance on sight and fears its loss, but emphasis on eye and vision health, in general, has not been integrated into daily life to the same extent as other health promotion activities, such as teeth brushing; hand washing; physical and mental exercise; and various injury prevention behaviors. A larger population health approach is needed to engage a wide range of stakeholders in coordinated efforts that can sustain the scope of behavior change. The shaping of socioeconomic environments can eventually lead to new social norms that promote eye and vision health. Making Eye Health a Population Health Imperative: Vision for Tomorrow proposes a new population-centered framework to guide action and coordination among various, and sometimes competing, stakeholders in pursuit of improved eye and vision health and health equity in the United States. Building on the momentum of previous public health efforts, this report also introduces a model for action that highlights different levels of prevention activities across a range of stakeholders and provides specific examples of how population health strategies can be translated into cohesive areas for action at federal, state, and local levels.