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Recently, the synthesis of 3D dynamic expressions has become an important concern in computer graphics, facial recognition, etc. In this study, we propose a regression based joint subspace learning method for the automatic synthesis of 3D dynamic expression images. This method synthesizes 3D dynamic expression images from a single 2D facial image. We use two subspaces (the view subspace and the frame...
Face Hallucination is, one of a learning-based super-resolution technique that can reconstruct a high-resolution image using only one low-resolution image. However, there are often some detailed high-frequency components of the reconstructed image that cannot be recovered using this method. In this study, we proposed a high-frequency compensated face hallucination method for enhancing reconstruction...
Incremental principal component analysis (IPCA) has been of great interest in computer vision and machine learning. In this paper, we introduce a new incremental learning procedure for principal component analysis (PCA). The proposed method can keep an accurate track of the mean of the data, and can deal with a set of new observed data in batch each time in subspace updating. Furthermore, a weighting...
This paper addresses the problems of feature selection and feature fusion. For the feature selection, the color SIFT descriptors in the independent components are ordered for image classification. To select distinctive and compact independent components (IC) of the color SIFT descriptors, we propose two ordering approaches based on variation: (1) Local ordering approaches (the localization-based ICs...
In this paper, we propose a visual tracking approach based on "bag of features" (BoF) algorithm. We randomly sample image patches within the object region in training frames for constructing two codebooks using RGB and LBP features, instead of only one codebook in traditional BoF. Tracking is accomplished by searching for the highest similarity between candidates and codebooks. Besides,...
We propose an incremental self-tuning particle filtering (ISPF) framework for visual tracking on the affine group. SIFT (Scale Invariant Feature Transform) like descriptors are used as basic features, and IPCA (Incremental Principle Component Analysis) is utilized to learn an adaptive appearance subspace for similarity measurement. ISPF tries to find the optimal target position in a step-by-step way:...
Glycosylation is one of the common post-translation modification of protein in eukaryotic cells. Conventional neural network methods have been applied to predict glycosylation sites in protein sequence and the prediction accuracy is dependent on the dimension of feature vector (length of protein sequence). Though the prediction accuracy can be improved by increasing the length of protein sequence,...
Statistical shape model (SSM) is to model the shape variation of an object. In this paper, we propose an efficient shape representation method and a new 2D-PCA based statistical shape modeling. In our proposed method, we used the radii of these surface points as shape feature instead of their coordinates, and the shape is represented by a 2D matrices. We then apply 2D-PCA to construct a statistical...
At present there are many methods that could deal well with frontal view face recognition when there is sufficient number of representative training samples. There into, subspace learning method such as principal component analysis (PCA), independent component analysis (ICA), linear discriminant analysis (LDA) are a very hot research topic in this field. However, in some face recognition system, the...
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