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Temperature Sensitive Paint (TSP) is a kind of special temperature sensor. TSP test is an essential approach to measure the surface temperature distribution, especially for high temperature, large area aero-engine components. The manual method of TSP test has a weak repeatability because it's heavily rely on the operator's personal experience and proficiency. Based on image processing algorithms,...
In this paper, we propose a novel feature extraction method called sparse local Fisher discriminant analysis (SLFDA), which is an extension of the local Fisher discriminant analysis (LFDA) algorithm. The proposed method projects the training samples into the range space of local total scatter matrix. Then, it gives the explicit characterization for all solutions of the LFDA. To obtain the sparse projection...
In this paper, we propose a novel feature extraction method called double sparse local Fisher discriminant analysis (DSLFDA), which is an extension of the local Fisher discriminant analysis (LFDA) algorithm. The proposed method combines the idea of sparse representation to construct an adaptive graph to describe the structure information of the samples. Meanwhile, to obtain the sparse projection vectors,...
For a large number of experimental data, the BRDF surface fitting method based on B-spline function and least squares theory, the ill-conditioned normal equations, the low accuracy of the results and long CPU time may be appeared. Thereby, in this paper by using the BP learning method, combined with the training process of L-M algorithm, an improved method is presented. And the method is applied to...
In recent years, feature extraction method make an achievement in pattern recognition. It extracts not only useful feature for classification, but also reduces the dimension of pattern sample. Linear discriminant analysis is an important method for image recognition, it achieve significant development both in theory and applications. Local fisher discriminant analysis redefines the between-class and...
In recent years, feature extraction methods make an achievement in pattern recognition and computer vision. It extracts not only useful feature for classification, but also reduces the dimension of pattern samples. In this paper, we propose orthogonal supervised spectral discriminant analysis (OSSDA) which motivated by marginal fisher analysis (MFA) and spectral clustering. It put different weights...
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