CORRELATION FILTERS FOR TARGET DETECTION IN A MARKOV MODEL BACKGROUND CLUTTER

作     者：KUMAR, BVKV CASASENT, DP MAHALANOBIS, A 

作者机构：The authors are with Carnegie Mellon University Department of Electrical & Computer Engineering Center for Excellence in Optical Data Processing Pittsburgh Pennsylvania 15213. 

出 版 物：《APPLIED OPTICS》 (美国光学会志，B辑：光物理学)

年 卷 期：1989年第28卷第15期

页      面：3112-3119页

核心收录：

学科分类：070207[理学-光学] 07[理学] 08[工学] 0803[工学-光学工程] 0702[理学-物理学] 

主　　题：Image processing Image registration Imaging noise Matched filtering Spatial filtering Synthetic discrimination functions 

摘      要：The performance of distortion-invariant correlation filters in the presence of background clutter is addressed. Background images are modeled as Markov noise processes, and a synthesis procedure for the optimal filter is described. It is shown that spatially filtering the training set images eliminates the need for the inversion of large noise covariance matrices, thus leading to a computationally efficient filter realization. The effect of errors (in the estimation of clutter correlation coefficient) on filter performance is theoretically analyzed, and a bound on the relative degradation of the SNR due to such errors is presented.