In this article, we develop a new algorithm for illumination invariant face recognition. We first transform the face images to the logarithm domain, which makes the dark regions brighter. We then use dual-tree complex wavelet transform to generate face images that are approximately invariant to illumination changes and use collaborative representation-based classifier to classify the unknown faces to one known class. We set the approximation sub-band and the highest two DTCWT coefficient sub-bands to zero values before the inverse DTCWT transform is performed. Experimental results demonstrate that our proposed method improves upon a few existing methods under both the noise-free and noisy environments for the Extended Yale Face Database B and the CMU-PIE face database.
By using.NET and SQL Server database management technology, anf this paper adopts B/S frame structure, innovatively designs the archives of Colleges and universities with database management system by C#.NET development technology. The association archives information data mining and improves the efficiency of archives management and inquiry statistics. The system uses MD5 encryption to encrypt user login system. The file can be printed information visualization, and the system can decently uses the internal operation of efficient. A detailed understanding of the different student user operation is convenient for the latter system, which can be used to update maintenance, accurate positioning system fault, which has high reliability, high security, scalability, easy expansibility and reusability.
Hyperspectral imagery can offer images with high spectral resolution and provide a unique ability to distinguish the subtle spectral signatures of different land covers. In this paper, we develop a new algorithm for hyperspectral image classification by using principal component analysis (PCA) and support vector machines (SVM). We use PCA to reduce the dimensionality of an HSI data cube, and then perform spatial convolution with three different filters on the PCA output cube. We feed all three convolved output cubes to SVM to classify every pixel. Finally, we perform fusion on the three output maps to determine the final classification map. We conduct experiments on three widely used hyperspectral image data cubes (ie indian pines, pavia university, and salinas). Our method can improve the classification accuracy significantly when compared to several existing methods. Our novel method is relatively fast in term of CPU computational time as well.
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