| Optical information processing is a newly subject developed 1870's. The invention of holography in 1948, the establishment of optical transfer function in 1955 and the nativity of laser-a new style strong coherent lamp in 1960 are not only three great invents in optics' developing history, but also the foundation of optical information processing. Moreover the Fourier transform effect of lens constitutes the theoretical frame of optical information processing.Optical image recognition is an important field of optical information processing. It can be widely used in the field such as the recognition and tracking of military object in the sky or on the ground, industry auto-adaptation, robot vision, and so on. At present there are two realization ways of optical image recognition: optical correlation and optical nerve network. Optical correlation has become a most important way for its 4-f system structure is simple and its output is apparent. Because of the great potential application of it in optical self-accommodation, target tracking, pattern recognition and robot vision, optical correlation was regarded more and more important. Early researches centered in the study of non-realtime matching-filter optical correlation using films. Recently, a magnitude development aspect is the study of real-time correlation which can be divided into two parts: one is making use of EA-SLM as joint apparatus and realizing real-time correlation using joint transform; the other is the study of real-time correlation using MSF. It is proposed that optics and electrics should be combined to optimize the system.The basic task of optical correlation is to achieve a judge that the reference signal and the input one are same or not. Traditional correlator transfers the measurement of comparability between reference signal and input signal into the measurement of intensity of a point. Correlation peak is the sign of consistency of them. To a certain reference signal, correlator present a strong and sharp peak when input signal is the same as the reference and present a relative flat peak when input image is different from the reference. When there is little difference between complex images in such field as military, medicine and biology, traditional method is inefficient. Traditional correlator is unable to recognize similar images. To overcome the defect oftraditional correlator, difference correlation that can recognize similar images efficiently was proposed in this paper.The work of this thesis has four parts:In the first part, we will describe the basic theory of optical image recognition, incoherent optical correlator, Vander Lugt correlator(VLC) and joint Fourier transform correlator(JTC). Both VLC and JTC realized optical correlation via processing inverse Fourier transform on the product of two Fourier transforms. The contrast of incoherent optical correlator output is too small to put into practical use. Compared with VLC, JTC is characterized by easily processing real-time recognition and self-adaptation. The capability of JTC declined rapidly if there are many signals on the input plane. After MSF got great success, the number of input object did not affect the output. Fractional Fourier transform and fractional correlation are also introduced in this part. Fractional correlation is also a way of optical pattern recognition.In the second part of this thesis, difference correlation is proposed to overcome the defect of traditional correlator. We give the academic deduce of difference correlation, construct the photoelectric processing system of this scheme and present the numerical simulative results. If input image is the same as the reference, we will get zero output. When they are not identical with each other, the output will present obvious correlation peak. This system can recognize similar images efficiently and the distribution of the output-peak can indicate the relative offset quantity of diversity unit.In the third part of this thesis, we put forward the difference correlation based on the frac...
|
PDF Full Text Request