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In this paper, we have investigated the use of principal component regression (PCR) combined with time domain filtering to predict the glucose concentration from NIR spectra of mixtures composed from glucose, urea and triacetin. The whole experiments were carried out in a non-controlled environment or sample conditions to show that the PCR coupled with digital bandpass filter can suppress effectively...
Linear Graph Embedding (LGE) is the linearization of graph embedding, and has been applied in many domains successfully. However, the high computational cost restricts these algorithms to be applied to large scale high dimensional data sets. One major limitation of such algorithms is that the generalized eigenvalue problem is computationally expensive to solve especially for large scale problems....
We present a method that automatically detects chewing events in surveillance video of a subject. Firstly, an Active Appearance Model (AAM) is used to track a subject's face across the video sequence. It is observed that the variations in the AAM parameters across chewing events demonstrate a distinct periodicity. We utilize this property to discriminate between chewing and non-chewing facial actions...
In this paper, we introduce an advanced age determination technique that combines a feature set derived from an image of the face using multi-factored Principal Components Analysis (PCA) on the shape of the face and its features and the skin of the face to produce a 30 × 1 linear encoding of the face. The linearly encoded features are combined with Spectral Regression (SR) to improve performance of...
Insect recognition is the basis of crop pest and disease control. Traditional insect recognition methods are time-consuming and hard-labor. Automatic machine insect recognition can solve the problem. In this paper, spectral regression LDA is used to reduce high dimension spaces of insects images, and get insect feature subspace. Then coefficient vector in the subspace is taken as the input of KNN...
In this paper, the physical analysis of planetary hyperspectral images is addressed. To deal with high dimensional spaces (image cubes present 256 bands), two methods are proposed. The first method is the support vectors machines regression (SVM-R) which applies the structural risk minimization to perform a non-linear regression. Several kernels are investigated in this work. The second method is...
It was developed that the method of spectral analysis was used to quantitatively analyze the rape moisture content. The method of region stepwise regression (RSR) was proposed to select the characteristic wavelengths for rape leaf moisture content prediction. The spectrum curve was segmented into several regions by the middle points of adjacent zeros of derivative spectrum data. Each region included...
Preventing accidents caused by drowsiness behind the steering wheel is highly desirable but requires techniques for continuously estimating driver's abilities of perception, recognition and vehicle control abilities. This paper proposes methods for drowsiness estimation that combine the electroencephalogram (EEG) log subband power spectrum, correlation analysis, principal component analysis, and linear...
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