We have developed an optical multiple correlation system for pattern recognition. It has superior ability of pattern discrimination even though an input image contains an unknown and complicated background or noise, which often disturbs the pattern recognition in the real world application. A design method for the multiple correlation filters has been developed, in which correlation filters are synthesized with component images extracted from training images by the principal component analysis. It is shown the correlation filters designed by this method has better efficiency on the noise tolerance and the detection ability than the filters synthesized directly with all training images. An optical multiple-correlation system has been developed and it is applied to a vision system of the robot named 'Gazing Tiger', in which the optical multiple correlation system detects locations of human faces in crowds and the robot looks a human face producing largest signal of correlation. The developed optical system can operate well even in the crowd and in the complicated background. It denotes the superior ability of the multiple correlation system we have developed.
A diffractive phase element (DPE) reducing the diameter of main-lobe of a focal light spot has been developed. A light spot focused by an optical system spreads due to the light diffraction from a limited aperture of lens. The developed DPE reduces the main-lobe diameter by modulating the incident wave-front of the focusing lens. Although the DPE increases the side-lobe intensity of the focal spot, appropriate design can reduce its magnitude enough not to affect the photoresist in lithography process. An iterative method based on the Gerchberg-Saxton algorithm combined with new constraints was applied to design a DPE. A rotation symmetrical binary DPE was designed, which reduces the main- lobe diameter to 74% and makes the side-lobe intensity under 2.7% of the main-lobe. The designed DPE was fabricated with the laser beam lithography system developed by the authors, and its performance was measured by mounting it on this system. The minimum line width obtained with the DPE becomes 1.0 micrometers while it is 1.2 micrometers without the DPE. It is also shown by a computer simulation that the focal depth of the focusing system with the DPE becomes wider than that without the DPE when both systems produce the same focal spot size.
We propose a new technique of correlation filter design for optical correlator. For detection and classification of complex patterns, ability of single optical correlator is not enough. To achieve high performance of classification, a multiple correlator is suitable in respect of flexibility in correlation filter design. We attempt to design a set of correlation filters for use in multiple optical correlators. As the target, we select road signs. In real scene, the scale and aspect ratio of road sign are dependent on the distance and angle of observation. In addition, many kinds of signs are used. Therefore the correlation filter set has to be designed as to have distortion invariance to adapt to the change of aspect and the variation of road signs. We apply the technique of multiple-object correlation filter, such as the synthetic discriminant function, to the design of correlation filter set, in order to obtain the necessary invariance. We calculate 180 filters in order to detect and classify 15 kinds of Japanese road signs in real scene. Computer simulation result shows that the combination of multiple optical correlator with the correlation filter set can indicate high performance of pattern detection and classification.
We applied the spectral imaging technique to the remote sensing of physiological responses on the human body. Blood, sweat and thermal distributions and their fluctuation are important and useful information to estimate the physiological state or thermal comfort of a person. Such information can be obtained as images by using cameras which can detect different wavelength regions. The blood distribution can be observed over a 430 nanometer wavelength region by the absorption pattern of oxidized hemoglobin contained in blood. Also information of sweat distribution can be obtained over a 1.9 micrometer region by the absorption pattern of water. Thermal cameras can acquire a thermal distribution of the human body without contact. We therefore attempt to observe simultaneously blood, sweat and thermal distributions and their fluctuations by spectral imaging. Some experimental results are shown. The sensitivity of this technique is discussed.
Study of pattern recognition technique using optical correlation has a long history. However, the technique has not been put to practical use yet. The main reason is that the amount of information included in temporally and spatially changeable images from the real world is too large to be processed by a single optical correlator. Another reason was a lackoffunctional optical or optoelectronic devices, such as spatial light modulators, micro lens arrays and smart pixel devices. However, functional optical devices have been developed andbecome available in optical systems. Nowadays it becomes possible to realize an optical computing system surpassing electronic system in processing speed, by applying the speed and parallelism of light. We think it is possible to realize a machine vision system which shows an adequate ability ofpattern recognition au! processing speed by a multiple optical correlator' using a set of optimized correlation filters and functional optoelectronic devices. In this paper, we designedsets ofoptical correlation filters fordetection andclassification of road signs in a image of real world scene, in order to evaluate the ability of machine vision system using multiple optical correlator. The correlation filter set is designedas to have partial invariance fordistortion to adaptto the change of aspect of road signs. Computer simulation result shows that the combination of multiple optical correlator and partial invariant correlation filter can indicate high performance of pattern recognition.
We propose a real-time optical pattern recognition system using an optical correlator. The system consists of two high speed spatial light modulators. To achieve high performance of pattern classification and to obtain bright correlation signals, the binary phase only modulation of both input patterns and correlation filters is applied. In order to suppress sidelobes in the correlation plane and to achieve the multiple-object pattern classification, optimized correlation filters are utilized. The experimental results shows that bright correlation peaks can be obtained without false peaks or large sidelobes.
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