Study Of Volume Holographic Correlation Recognition Technology Based On Near Stoichiometric Mg:Fe:LiNbO₃ Crystal

With the developing of information and computer technology, the new storage system is needed urgently. Volume holographic storage has become a potential storage means for the merits of high package density, high storage capacity, fast data transfer rates and short access time. Accordingly, the correlator based on volume holographic storage is a rising optical pattern recognition technology with inherent characteristics of parallelism and rapidity.The preparition of photorefractive materials with high performance and better quality is the key technology to promote the development of volume holographic correlation. So, near stoichiometric Mg:Fe:LiNbO₃ crystals, which is not only heavy gauge and uniform component but also high photodamage resistance ability, were grown by Top-seeded solution growth method, and the appropriate technological conditions (mixture ratio of raw materials, thermal field et al) were adoped. By analysis, [Li]/[Nb] ratio in crystals, attributed indirectly, is about 49.7⁴9.8%, the threshold concentration and location of Mg in doped near stoichiometric LiNbO₃ crystals are confirmed, and the location model is given. The defference of Curie temperature between crystals'head and tail is less than 2℃. Crystals'photodamage resistance ability is increased to 10⁴W·cm^-2, diffraction efficiency is up to 60%, response time is under submicrosecond. All of these can indicate crystals's component is uniform, and its ability of volume holographic storage and correlation is much higher than congruent Fe:LiNbO₃ crystals, which give a thick basis of volume holographic storage and correlation.Feature recognition vectors method is used to improve recognition accuracy under the circumstance of uneven diffraction efficiency, which must need the accurate position of correlation peaks. So, neighborhood variance increasing method based on neighborhood entropy is presented to allocate correlation peaks automatically, which can also solve the influence of background brightness. First two suitable neighborhoods are selected, then the value of variance increasing is calculated, other than entropy, to detect and locate target. It only takes less than 4 seconds to complete location, against correlation output image with size of 1024×768 pixels.Against large sample problems, an adaptive multi-scale edge extraction method is presented, which is used to improve the sharpness of correlation peaks. According the difference of Lipschitz exponent between noise and edge in wavelet transform domain, multi-scale edge correlation function is defined to check modulus extreme points, and then dual threshold, which is used to get binary image, is determined adaptively based on such criterion that within-class variance minimization. Volume holographic correlation systems with 1000 holograms from AR face database are realized. Recognition rate is 99.50% against stored image and all correct results are achieved towards 500 images not stored.Volume holographic correlation is not very effective under small sample size case. To solve this problem, a weighted two dimentional Fisherface algorithm is presented to extract features and reconstruct original information, which is used as a substitution of primary training samples and stored in crystals. First, two dimention principal component analysis is used to reduce training samples'dimension and obtain the most expressive features. Then, the most separative features is extracted by Fisher criterion, which is maximized the proportion of between-class dispersion and in-class one, and the impace of outline values is reduced by lighter weight. At last reconstructed image is rebuilt by such features. Correlation exprements based on ORL and Yale face database verify the effectivedness of the algorithm, and recognition rate is about 10% higher then that yielded by traditional volume holographic correlation.