Markov Random Fields for Vision and Image Processing (Hardcover)

This volume demonstrates the power of the Markov random field (MRF) in vision, treating the MRF both as a tool for modeling image data and, utilizing recently developed algorithms, as a means of making inferences about images. These inferences concern underlying image and scene structure as well as solutions to such problems as image reconstruction, image segmentation, 3D vision, and object labeling. It offers key findings and state-of-the-art research on both algorithms and applications. After an introduction to the fundamental concepts used in MRFs, the book reviews some of the main algorithms for performing inference with MRFs; presents successful applications of MRFs, including segmentation, super-resolution, and image restoration, along with a comparison of various optimization methods; discusses advanced algorithmic topics; addresses limitations of the strong locality assumptions in the MRFs discussed in earlier chapters; and showcases applications that use MRFs in more complex ways, as components in bigger systems or with multiterm energy functions. The book will be an essential guide to current research on these powerful mathematical tools.

本書展示了馬可夫隨機場（Markov random field，MRF）在視覺領域中的威力，將MRF既視為建模圖像數據的工具，又利用最近開發的算法對圖像進行推斷。這些推斷涉及底層圖像和場景結構，以及圖像重建、圖像分割、三維視覺和物體標記等問題的解決方案。本書提供了關鍵發現和最新研究，涵蓋了算法和應用領域。在介紹MRF基本概念後，本書回顧了一些用於進行MRF推斷的主要算法；介紹了MRF的成功應用，包括分割、超分辨率和圖像恢復，並比較了各種優化方法；討論了高級算法主題；解釋了早期章節中MRF強局部性假設的局限性；並展示了更複雜應用，將MRF作為更大系統或多項能量函數的組件。本書將成為這些強大數學工具當前研究的重要指南。