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In this letter, a novel semantic constant-false-alarm-rate (CFAR) method for the detection of cars from a very high resolution synthetic-aperture-radar (SAR) image is presented. The method not only employs the strong scattering features of the target which is used in CFAR but also employs the shadow features of the target. Furthermore, the semantic relationship between the strong scattering features...
As most electronic system structure is complex and uncertain, this paper presents a new efficiency method for spacecraft electrical characteristics identification. PCA (Principal Component Analysis) feature extraction, offline FCM (Fuzzy C-means) clustering and online SVM (Support Vector Machine) classifier is introduced into the registration model. At first step of the algorithm, get an expert training...
As most electronic system structure is complex and uncertain, this paper presents a new efficiency method for spacecraft electrical characteristics identification. Offline FCM clustering and online SVM classifier is introduced into the registration model. At first step of the algorithm, using FCM clustering method to get an expert training set. By get expert training set for SVM classifier make this...
In this paper, we propose a novel algorithm for face feature extraction, namely the cascade two-dimensional locality preserving projections (C2DLPP), which directly extracts the proper features from image matrices based on locality preserving criterion. Experiments on ORL and PIE face database are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of proposed...
A novel face feature extraction method based on Bilateral Two-dimensional Principal Component Analysis (B2DPCA) and Kernel Discriminant Analysis (KDA) was presented in this paper. In this method, B2DPCA method directly extracts the proper features from image matrices at first, then the KDA was performed on the features to enhance discriminant power. As opposed to PCA, B2DPCA is based on 2D image matrices...
In this paper, we propose a novel weighted complete linear discriminant analysis (WCLDA) method for feature extraction and recognition. The WCLDA first introduces a weighting function to restrain the dominant role of the classes with larger distance and then searches the optimal discriminant vectors under the conjugative orthogonal constrains in the null space of the within-class scatter matrix and...
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