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As the necessity of automatized system has been increased, an adaptive target detection algorithm is widely demanded in the development of industrial and military technologies. Accordingly, this paper proposes a novel approach to detect and segment the moving targets from a sequence of images which are detected by a detector embedded on a moving vehicle such as car, airplane, ship, and so on. The BPEJTC (binary phase extraction joint transform correlator) that can produce phase type correlation peaks in real time by optical way, is introduced for compensating the camera movement between two successive frames.

In this paper, a rotation invariant fingerprint identification system is implemented by using the circular harmonic filter and the binary phase extraction joint transform correlator. We present that this system has the shift and rotation invariant properties and can recognize the fingerprint in real-time. The complex circular harmonic filter which is used to obtain the rotation invariance, is converted into the real-valued filter for real-time implementation. Through computer simulation, we also show that this system has a good performance in the rotated fingerprints.

To give a real-time adaptive self-organizing capability to the automatic target recognition (ATR) system suppressing the over clustering, the modified adaptive resonance theory (mART) neural networks are proposed which include the vigilance test method of self-organizing map (SOM) and the real-time adaptive clustering algorithm of ART. This neural networks effectively cluster the arbitrary feature maps which are mostly invariant to two dimensional distortion, so as to solve the three dimensional distortion problem. As the extraction of features which are invariant to two dimensional distortion, five alternative methods are tested in this paper. And for the purpose of proving the performance of the proposed neural networks, some experiments with the database composed of 9 fighters and 5 tanks are carried out. Under the condition that the system occupies the same size of memory, the mART produces 19% higher recognition rate than that of the SOM neural networks. Consequently, it is proved that the proposed approaches can give a great attribution in realizing the three dimensional distortion invariant target recognition system.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A novel BPEJTC-based method of extracting a moving target's signature residing in a background input is proposed. The efficiency of this method depends to a great extent on the degree of a JTC performance. A BPEJTC is shown to have a improved peak-to-sidelobe ratio than that of a conventional JTC. An optoelectronic system implementing this method is realized. In this system, time-consuming computations, like FFT are conducted optically while arithmetic operations are executed digitally such that the proposed hybrid system can operate in real-time. Some experimental results obtained with real input images indicate that the proposed system can effectively work even in a nonstationary background.

In this paper, we present an OptoNeural approach to the problem of multitarget tracking. The proposed hybrid OptoNeural system uses a new optical binary phase extraction joint transform correlator (BPEJTC) to reduce the massive input target data into a few correlation peak signals. A parallel computational neural network is then used for effective target tracking data association based on these correlation signals. For real-time operation, the BPEJTC is optically implemented using the high resolution LCD spatial light modulators and CCD detectors. Some experimental results on simultaneous tracking of multitargets are also provided.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.