Face recognition is a task that the human vision system seems to perform almost effortlessly, yet the goal of building computer-based systems with comparable capabilities has proven to be difficult. The task implicitly requires the ability to locate and track faces through often complex and dynamic scenes. Recognition is difficult because of variations in factors such as lighting conditions, viewpoint, body movement and facial expression. Although evidence from psychophysical and neurobiological experiments provides intriguing insights into how we might code and recognise faces, its bearings on computational and engineering solutions are far from clear. The study of face recognition has had an almost unique impact on computer vision and machine learning research at large. It raises many challenging issues and provides a good vehicle for examining some difficult problems in vision and learning. Many of the issues raised are relevant to object recognition in general.This book describes the latest models and algorithms that are capable of performing face recognition in a dynamic setting. The key question is how to design computer vision and machine learning algorithms that can operate robustly and quickly under poorly controlled and changing conditions. Consideration of face recognition as a problem in dynamic vision is perhaps both novel and important. The algorithms described have numerous potential applications in areas such as visual surveillance, verification, access control, video-conferencing, multimedia and visually mediated interaction.The book will be of special interest to researchers and academics involved in machine vision, visual recognition and machine learning. It should also be of interest to industrial research scientists and managers keen to exploit this emerging technology and develop automated face and human recognition systems. It is also useful to postgraduate students studying computer science, electronic engineering, information or systems engineering, and cognitive psychology.
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Charlotte y Peter Fiell son dos autoridades en historia, teoría y crítica del diseño y han escrito más de sesenta libros sobre la materia, muchos de los cuales se han convertido en éxitos de ventas. También han impartido conferencias y cursos como profesores invitados, han comisariado exposiciones y asesorado a fabricantes, museos, salas de subastas y grandes coleccionistas privados de todo el mundo. Los Fiell han escrito numerosos libros para TASCHEN, entre los que se incluyen 1000 Chairs, Diseño del siglo XX, El diseño industrial de la A a la Z, Scandinavian Design y Diseño del siglo XXI.
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Zustand: Muy bueno. : Este libro explora los últimos modelos y algoritmos capaces de realizar el reconocimiento facial en un entorno dinámico. La pregunta clave es cómo diseñar algoritmos de visión artificial y aprendizaje automático que puedan operar de forma robusta y rápida en condiciones mal controladas y cambiantes. El estudio del reconocimiento facial ha tenido un impacto casi único en la visión artificial y la investigación del aprendizaje automático en general. Plantea muchos problemas desafiantes y proporciona un buen vehículo para examinar algunos problemas difíciles en la visión y el aprendizaje. Muchos de los problemas planteados son relevantes para el reconocimiento de objetos en general. EAN: 9781860941818 Tipo: Libros Categoría: Tecnología|Ciencias Título: Dynamic Vision Autor: Shaogang Gong| Alexandra Psarrou| Stephen J. McKenna Editorial: Imperial College Press Idioma: en Páginas: 364 Formato: tapa dura. Artikel-Nr. Happ-2025-05-29-d6644cdd
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Gebunden. Zustand: New. A description of models and algorithms that are capable of performing face recognition in a dynamic setting. The key question is how to design computer-vision and machine-learning algorithms that can operate robustly and quickly under poorly-controlled and . Artikel-Nr. 597127186
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