 | | 系統號碼 | 716754 | | 書刊名 | (Hyper)-graphs inference through convex relaxations and move making algorithms : contributions and applications in artificial vision / | | 主要著者 | Komodakis, Nikos., author. | | 其他著者 | Kumar, M. Pawan.,;Paragios, Nikos., | | 出版項 | [Hanover, Massachusetts] : Now Publishers, 2016. | | 索書號 | RE986.K654 2016 | | ISBN | 9781680831399 | | 標題 | Artificial vision-Data processing.
Visual perception-Data processing.
Graphical modeling (Statistics)
Hypergraphs.
Markov random fields.
Graphical models | | 電子資源 | Abstract with links to full text
http://dx.doi.org/10.1561/0600000066 | | 叢書名 | Foundations and trends in computer graphics and vision,volume 10, issue 1, pages 1-1021572-2759 ; | | | |
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| | 資料類型 | 狀態 | 應還日期 | 預約人數 | 館藏地 | 索書號 | 條碼號 | | 找書 | 圖書 | 在架上 | | 0 | 總館
西文圖書區  | RE986 .K654 2016 | W108569 |
| 內容簡介 | Computational visual perception seeks to reproduce human vision through the combination of visual sensors, artificial intelligence and computing. To this end, computer vision tasks are often reformulated as mathematical inference problems where the objective is to determine the set of parameters corresponding to the lowest potential of a task-specific objective function. Graphical models have been the most popular formulation in the field over the past two decades where the problem is viewed as a discrete assignment labeling one. Modularity, scalability and portability are the main strengths of these methods which once combined with efficient inference algorithms they could lead to state of the art results. In this tutorial we focus on the inference component of the problem and in particular we discuss in a systematic manner the most commonly used optimization principles in the context of graphical models. Our study concerns inference over low rank models (interactions between variables are constrained to pairs) as well as higher order ones (arbitrary set of variables determine hyper-cliques on which constraints are introduced) and seeks a concise, self-contained presentation of prior art as well as the presentation of the current state of the art methods in the field. |
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