Expectation-maximization algorithms for image processing using multiscale models and mean- field theory, with applications to laser radar range profiling and segmentation
Jun Zhang · Optical Engineering · 2001
We describe a new class of computationally efficient algo- rithms designed to solve incomplete-data problems frequently encoun- tered in image processing and computer vision. The basis of this frame- work 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 esti- mates 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 prob- lems. 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. © 2001 Society of Photo-Optical Instrumentation Engineers.