In a variety of applications (including automatic target recognition) image classification algorithms operate on compressed image data. This paper explores the design of optimal transform coders and scalar quantizers using Chernoff bounds on probability of misclassification as the measure of classification accuracy. This design improves classification performance but the mean square error (as w...

Feature extraction algorithms for vehicle classification techniques represent a large branch of Automatic Target Recognition (ATR) efforts. Traditionally, vehicle ATR techniques have assumed time series vibration data collected from multiple accelerometers are a function of direct path, engine driven signal energy. If data, however, is highly dependent on measurement location these pre-establis...

In this paper we propose a system that detects independently moving objects (IMOs) in forward looking infra-red (FLIR) image sequences taken from an airborne, moving platform. Ego-motion effects are removed through a robust multi-scale affine image registration process. Consequently, areas with residual motion indicate object activity. These areas are detected, refined and selected using a Baye...

Optical correlator systems due to their inherent parallel processing capabilities have the potential of processing large data bank to enable rapid identification of targets within an input signal. Purely optical correlation systems are not practical for real time applications. In this paper, we discuss and implement the hybrid correlator system. In this system, the input scene is Fourier transf...

According to literature, a significant and up to date research direction to increase the performance level of automatic target recognition (ATR) systems is focused on the use of information coming from an appropriate set of EM sensors and high-quality decision fusion techniques, respectively. Consequently, in this paper a genetic optimized version of Sugeno’s fuzzy integral is discussed. In add...

In this paper, we investigate the performance of the fuzzy entropy approach when it is applied to the segmentation of infrared objects. Through a number of examples, the performance is compared with those using existing entropy-based object segmentation approaches and the superiority of the fuzzy entropy method is demonstrated. In addition, the ant colony optimization (ACO) is used to obtain th...

This paper discusses several current attempts to use acoustic and electromagnetic wave propagation for modeling physical phenomena and the role that wavelet analysis is playing in these eeorts. The rst problem involves recent application of wavelets to computational uid dynamics. The second problem involves geophysical modeling of the ocean oor, using acoustic waves, and wavelets have recently ...

This paper describes a new methodology for Automatic Target Recognition in visual images and second generation Forward Looking Infrared (FLIR) images. A system that uses information from multiple features or sensors can employ redundancy, diversity and complementarity to overcome the shortcomings of single-sensor systems and improve performance. In this paper, a general multifeature/multisensor...

A strategy is introduced for achieving high accuracy in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. Initially, a novel pose rectification process and an image normalization process are sequentially introduced to produce images with less variations prior to the feature processing stage. Then, feature sets that have a wealth of texture and edge information are extract...

We present two stochastic filters for an FM-band passive air surveillance radar. The first system uses an extended Kalman filter and delay-Doppler measurements to track targets. The second system uses a particle filter to simultaneously track and classify targets. Automatic target recognition is made possible by the inclusion of radar cross section (RCS) in the measurement vector. The extended ...