As the transmission of 3D shape models through Internet becomes more important, the compression issue of shape models gets more critical. While the compressions of topology and geometry have been explored significantly, the same issue for normal vectors has not yet been studied as much as it deserves.
Presented in this paper is an approach to compress the normal vectors of a 3D mesh model using the concept of clustering and relative indexing. The model is assumed to be manifold triangular mesh model with normal vectors associated with vertices. The proposed scheme clusters the normal vectors of given model and the representative normal vector of each cluster is referred to via a mixed use of relative as well as absolute indexing concepts. It turns out that the proposed approach achieves a significant compression ratio (less than 10% of the original VRML model files) without a serious sacrifice of the visual quality. 相似文献
The aim of this paper is to propose new regularization and filtering techniques for dense and sparse vector fields, and to focus on their application to non-rigid registration. Indeed, most of the regularization energies used in non-rigid registration operate independently on each coordinate of the transformation. The only common exception is the linear elastic energy, which enables cross-effects between coordinates. Cross-effects are yet essential to give realistic deformations in the uniform parts of the image, where displacements are interpolated.In this paper, we propose to find isotropic quadratic differential forms operating on a vector field, using a known theorem on isotropic tensors, and we give results for differentials of order 1 and 2. The quadratic approximation induced by these energies yields a new class of vectorial filters, applied numerically in the Fourier domain. We also propose a class of separable isotropic filters generalizing Gaussian filtering to vector fields, which enables fast smoothing in the spatial domain. Then we deduce splines in the context of interpolation or approximation of sparse displacements. These splines generalize scalar Laplacian splines, such as thin-plate splines, to vector interpolation. Finally, we propose to solve the problem of approximating a dense and a sparse displacement field at the same time. This last formulation enables us to introduce sparse geometrical constraints in intensity based non-rigid registration algorithms, illustrated here on intersubject brain registration. 相似文献
Accurately and automatically detecting image orientation is of great importance in intelligent image processing. In this paper, we present automatic image orientation detection algorithms based on both the luminance (structural) and chrominance (color) low-level content features. The statistical learning support vector machines (SVMs) are used in our approach as the classifiers. The different sources of the extracted image features, as well as the binary classification nature of SVM, require our system to be able to integrate the outputs from multiple classifiers. Both static combiner (averaging) and trainable combiner (also based on SVMs) are proposed and evaluated in this work. Furthermore, two rejection options (regular and re-enforced ambiguity rejections) are employed to improve orientation detection accuracy by sieving out images with low confidence values during the classification. Large amounts of experiments have been conducted on a database of more than 14,000 images to validate our approaches. Discussions and future directions for this work are also addressed at the end of the paper. 相似文献
In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LS-SVM sparse approximation procedure can be successfully applied. 相似文献