| 书目名称 | Stochastic Algorithms for Visual Tracking | | 副标题 | Probabilistic Modell | | 编辑 | John MacCormick | | 视频video | | | 概述 | Includes supplementary material: | | 丛书名称 | Distinguished Dissertations | | 图书封面 |  | | 描述 | A central problem in computer vision is to track objects as they move and deform in a video sequence. Stochastic algorithms -- in particular, particle filters and the Condensation algorithm -- have dramatically enhanced the state of the art for such visual tracking problems in recent years. This book presents a unified framework for visual tracking using particle filters, including the new technique of partitioned sampling which can alleviate the "curse of dimensionality" suffered by standard particle filters. The book also introduces the notion of contour likelihood: a collection of models for assessing object shape, colour and motion, which are derived from the statistical properties of image features. Because of their statistical nature, contour likelihoods are ideal for use in stochastic algorithms. A unifying theme of the book is the use of statistics and probability, which enable the final output of the algorithms presented to be interpreted as the computer‘s "belief" about the state of the world. The book will be of use and interest to students, researchers and practitioners in computer vision, and assumes only an elementary knowledge of probability theory. | | 出版日期 | Book 2002 | | 关键词 | Importance Sampling; Notation; Particle filters; Partitoned sampling; Stochastic Algorithms; algorithms; c | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4471-0679-1 | | isbn_softcover | 978-1-4471-1176-4 | | isbn_ebook | 978-1-4471-0679-1 | | copyright | Springer-Verlag London Limited 2002 |
The information of publication is updating
|
|
发表于 2025-3-21 19:23:13
