This work is motivated by ship identification from a sequence of ISAR images. Maximum likelihood classification, based on point matching, is formulated when the observed images are subject to missing points and phantoms. The 3-D to 2-D transformation is assumed to be known only in a certain parametric form. Proper weights, based on the noise levels for all images, are derived for the classification formula. The new formulation simplifies the computation of matching and makes its extension to object identification from multiple images feasible. Moreover, some theoretical properties of the identification procedure can now be investigated. Guidelines on which groups of objects are easier to distinguish are found from statistical theory followed by intuitive explanation. This method is then applied to ship identification with simulated ISAR images.>

A filter induced by the autoregressive pattern from an image is used to enhance the image itself. The enhanced image usually reveals the wave pattern and nonstationary structures more clearly than the original. Unlike the Fourier analysis, local structures in the image are still retained. The merits have been demonstrated on simulated data and synthetic-aperture radar (SAR) ocean imagery.>

Two situations in which the image gray-level histogram cannot be used for threshold determination are: (1) the situation in which the background noise by itself has a multimodal distribution; and (2) the situation in which the object is so small that its contribution to the histogram is overwhelmed by the noise portion even if the noise distribution is unimodal. To alleviate these two undesirable conditions, local average, the central limit theorem, and a statistical theory for truncated data analysis are used to: (1) make the noise part of the histogram appear unimodal; and (2) cut off a large portion of the background so that the object portion in the histogram becomes more prominent. The gray-level distributions for the background and the object are then estimated and used to find an optimum threshold.>

This paper presents a detailed analysis of a sampling approach used in the partitioning of a data file for the parallel balanced tree sort in a local area network or a multiprocessor environment. The average overall time complexity for sorting N data on a k processor system is derived. The performance of the parallel sorting rests upon how evenly the file can be partitioned into k ordered subfiles. A data partition scheme by sampling is proposed and analyzed. Formulas for computing the optimal sampling size are obtained. The results also show the computational improvement of the sorting as a function of k and the sampling overhead. The performance of the sampling method is studied and found to be approaching the absolute optimal in some cases.