| 期刊全稱 | Bayesian Modeling of Uncertainty in Low-Level Vision | | 影響因子2023 | Richard Szeliski | | 視頻video | http://file.papertrans.cn/182/181861/181861.mp4 | | 學(xué)科分類 | The Springer International Series in Engineering and Computer Science | | 圖書封面 |  | | 影響因子 | Vision has to deal with uncertainty. The sensors are noisy, the prior knowledge is uncertain or inaccurate, and the problems of recovering scene information from images are often ill-posed or underconstrained. This research monograph, which is based on Richard Szeliski‘s Ph.D. dissertation at Carnegie Mellon University, presents a Bayesian model for representing and processing uncertainty in low- level vision. Recently, probabilistic models have been proposed and used in vision. Sze- liski‘s method has a few distinguishing features that make this monograph im- portant and attractive. First, he presents a systematic Bayesian probabilistic estimation framework in which we can define and compute the prior model, the sensor model, and the posterior model. Second, his method represents and computes explicitly not only the best estimates but also the level of uncertainty of those estimates using second order statistics, i.e., the variance and covariance. Third, the algorithms developed are computationally tractable for dense fields, such as depth maps constructed from stereo or range finder data, rather than just sparse data sets. Finally, Szeliski demonstrates successful applications of | | Pindex | Book 1989 |
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