Expectation-maximization algorithms for image processing using multiscale models and mean-field theory, with applications to laser radar range profiling and segmentation

作     者：Tsai, A Zhang, J Willsky, AS 

作者机构：MIT Informat & Decis Syst Lab Cambridge MA 02139 USA Univ Wisconsin Dept Elect Engn & Comp Sci Milwaukee WI 53201 USA 

出 版 物：《OPTICAL ENGINEERING》 (光学工程)

年 卷 期：2001年第40卷第7期

页      面：1287-1301页

核心收录：

学科分类：08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 081102[工学-检测技术与自动化装置] 0811[工学-控制科学与工程] 0702[理学-物理学] 

基　　金：Air Force Office of Scientific Research, AFOSR, (F49620-98-1-0349) Boston University, BU 

主　　题：EM algorithm mean-field theory multiscale models laser radar image processing 

摘      要：We describe a new class of computationally efficient algorithms designed to solve incomplete-data problems frequently encountered in image processing and computer vision. The basis of this framework is the marriage of the expectation-maximization (EM) procedure with two powerful methodologies. In particular, we have incorporated optimal multiscale estimators into the EM procedure to compute estimates and error statistics efficiently. In addition, mean-field theory (MFT) from statistical mechanics is incorporated into the EM procedure to help solve the computational problems that arise from our use of Markov random-field (MRF) modeling of the hidden data in the EM formulation. We have applied this, algorithmic framework and shown that it is effective in solving a wide variety of image-processing and computer-vision problems. We demonstrate the application of our algorithmic framework to solve the problem of simultaneous anomaly detection, segmentation, and object profile estimation for noisy and speckled laser radar range images. (C) 2001 Society of Photo-Optical Instrumentation Engineers.