The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The PCA dimensionality reduction algorithm for 2D data with the Laplacian noise model, i.e., L1-2DPCA, not only preserves the structural relation among 2D data, but also is robust for data outliers. The algorithm relies on the EM algorithm with great computational cost. In order to learn intrinsic information more consistently, this paper takes a view of manifold optimization for the model based on...
Restricted Boltzmann Machine (RBM) is a particular type of random neural network models modeling vector data based on the assumption of Bernoulli distribution. For multidimensional and non-binary data, it is necessary to vectorize and discretize the information in order to apply the conventional RBM. It is well-known that vectorization would destroy internal structure of data, and the binary units...
Restricted Boltzmann Machine (RBM) is an important generative model modeling vectorial data. While applying an RBM in practice to images, the data have to be vectorized. This results in high-dimensional data and valuable spatial information has got lost in vectorization. In this paper, a Matrix-Variate Restricted Boltzmann Machine (MVRBM) model is proposed by generalizing the classic RBM to explicitly...
The probabilistic principal component analysis (PPCA) is built upon a global linear mapping, with which it is insufficient to model complex data variation. This paper proposes a mixture of bilateral-projection probabilistic principal component analysis model (mixB2DPPCA) on 2D data. With multi-components in the mixture, this model can be seen as a 'soft' cluster algorithm and has capability of modeling...
The marine environment, such as wind, wave and current, can have a significant effect in subsea production tree installation process and has brought difficulties and challenges. In this paper, in order to analyze riser stress and displacement in tis installation with different marine environments, the loads and boundary conditions at different installation stages are elected, and the installation...
With the semi-structured data rapidly growing, it is crucial to obtain valuable information for different applications. So many data mining methods are proposed and the frequent sub trees mining is an important and typical method. The current mining methods demand substantial computational time and space, and return a huge number of patterns, but some important sub trees are often missed and some...
In recent years, Sparse Representation based classification (SRC) has made great progress in Face Recognition. However, SRC is only efficient and effective when the noise is sparse. The recognition rate of SRC decreases when the noise is non-Gaussian, for example, the light on the face is quite various or the face is covered in part by a mask. In this paper, we propose a robust l2,1-norm Sparse Representation...
As the energy of nodes in Wireless Sensor Networks (WSNs) is generally constrained, it is urgent to develop an efficient data gathering algorithm. Recently, Low Rank approximation is deeply studied and successfully applied in WSNs data recovery, in which a subset of nodes is randomly selected for sensing the environmental data, such as temperature and humidity. But this random sampling solution generally...
Recently, techniques based on dictionary learning for sparse representation have demonstrated promising results for depth or disparity maps restoration. However, we show that these methods are not robust due to the fact that depth or disparity maps are not only slightly contaminated by additive Gaussian noise but also seriously corrupted with outliers, occlusions, or even variable uncertainties. These...
Non-rigid registration of 3D facial surfaces is a crucial step in a variety of computer vision tasks. In this paper, we present a fully automatic 3D face registration method based on the thin plate spline (TPS) and deformable model. To model the non-rigid modality of complex 3D facial surfaces, the thin plate spline is adopted to represent the transformation between 3D faces. The farthest point sampling...
In this paper, we present an automatic point matching method to overcome the dense point alignment of scanned 3D faces. The thin plate spline (TPS) transformation is adopted to model the deformation of 3D faces, and to get fully automatic point matching method, a random point selecting method is proposed to get the controlling points for TPS transformation. Integrating this point generating method...
In this paper, we present an automatic point matching method to overcome the dense point alignment of 3D face scans. We adopt TPS (thin plate spline) transformation to model the deformation of different 3D faces, because TPS is a type of non-rigid transformation with good smooth property and suitable for formulating the complexities of human facial morphology. Generally, TPS is derived from the interpolation...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.