15559062
Adaptive Geometry Compression Based on 4-Point Interpolatory Subdivision Schemes
We propose an adaptive geometry compression method based on 4-point interpolatory subdivision schemes. It can work on digital curves of arbitrary dimensions. With the geometry compression method, a digital curve is adaptively compressed into several segments with different compression levels. Each segment is a 4-point subdivision curve with a subdivision step. In the meantime, we provide high-speed 4-point interpolatory subdivision curve generation methods for efficiently decompressing the compressed data. In the decompression methods, we consider both the open curve case and the closed curve case. For an arbitrary positive integer k, formulae of the number of the resultant control points of an open or closed 4-point subdivision curve after k subdivision steps are provided. The time complexity of the new approaches are O(n), where n is the number of the points in the given digital curve. Examples are provided as well to illustrate the efficiency of the proposed approaches.
3748150
Mesh blending
A new method for smoothly connecting different patches on triangle meshes with arbitrary connectivity, called mesh blending, is presented. A major feature of mesh blending is to move vertices of the blending region to a virtual blending surface by choosing an appropriate parameterization of those vertices. Once blending is completed, the parameterization optimization is performed to perfect the final meshes. Combining mesh blending with multiresolution techniques, an effective blending technique for meshes is obtained. Our method has several advantages: (1) the user can intuitively control the blending result using different blending radii, (2) the shape of cross-section curves can be adjusted to flexibly design complex models, and (3) the resulting mesh has the same connectivity as the original mesh. In this paper, some examples about smoothing, sharpening, and mesh editing show the efficiency of the method.
18976777
Up to Face Extrusion Algorithm for Generating B-Rep Solid
Up to face extrusion (UTFE) is an effective operation to extrude the profile from a sketch plane to a selected face. An up to face extrusion algorithm is presented in this paper. The algorithm first generates the trimmed extrusion through a simple body-surface Boolean operation method, and then generates the resultant solid through a regularized Boolean operation between the original solid and the trimmed extrusion. The algorithm has been implemented in a commercial geometric modeling software system TiGems. Examples are given to illustrate the algorithm.
16104127
Bilateral Filter for Meshes Using New Predictor
A new predictor of bilateral filter for smoothing meshes is presented. It prevents shrinkage of corners. A major feature of mesh smoothing is to move every vertex along the direction determined by the mean curvature normal with speed defined by the predictor. It prevents unnatural deformation for irregular meshes. In order to remove thenormal noise, we use adaptive Gaussian filter to smooth triangle normals.
18261046
Shelling Algorithm in Solid Modeling
Shelling is a powerful modeling operation in current CAD /CAM systems. In this paper, we present a new shelling algorithm for regular B-Rep solids. We first generate an initial offset solid, then correct the self-intersecting offset solid if necessary. We obtain the resultant shelling solid through a regularized Boolean operation finally. The algorithm has been implemented in a solid modeling system, and some examples are given to illustrate it.
6781663
Parallel Image Matrix Compression for Face Recognition
The canonical face recognition algorithm Eigenface and Fisherface are both based on one dimensional vector representation. However, with the high feature dimensions and the small training data, face recognition often suffers from the curse of dimension and the small sample problem. Recent research [4] shows that face recognition based on direct 2D matrix representation, i.e. 2DPCA, obtains better performance than that based on traditional vector representation. However, there are three questions left unresolved in the 2DPCA algorithm: I ) what is the meaning of the eigenvalue and eigenvector of the covariance matrix in 2DPCA; 2) why 2DPCA can outperform Eigenface; and 3) how to reduce the dimension after 2DPCA directly. In this paper, we analyze 2DPCA in a different view and proof that is 2DPCA actually a "localized" PCA with each row vector of an image as object. With this explanation, we discover the intrinsic reason that 2DPCA can outperform Eigenface is because fewer feature dimensions and more samples are used in 2DPCA when compared with Eigenface. To further reduce the dimension after 2DPCA, a two-stage strategy, namely parallel image matrix compression (PIMC), is proposed to compress the image matrix redundancy, which exists among row vectors and column vectors. The exhaustive experiment results demonstrate that PIMC is superior to 2DPCA and Eigenface, and PIMC+LDA outperforms 2DPC+LDA and Fisherface.
7400694
A Parallel BDI Agent Architecture
8279644
Z-transformation graphs of maximum matchings of plane bipartite graphs
Let G be a plane bipartite graph. The Z-transformation graph Z(G) and its orientation Z(G) on the maximum matchings of G are defined. If G has a perfect matching, Z(G) and Z(G) are the usual Z-transformation graph and digraph. If G has neither isolated vertices nor perfect matching, then Z(G) is not connected. This paper mainly shows that some basic results for Z-transformation graph (digraph) of a plane elementary bipartite graph still hold for every nontrivial component of Z(G) (Z(G)). In particular, by obtaining a result that every shortest path of Z(G) from a source of Z(G) corresponds to a directed path of Z(G), we show that every nontrivial component of Z(G) has exactly one source and one sink. Accordingly, it follows that the block graph of every nontrivial component of Z(G) is a path. In addition, we give a simple characterization for a maximum matching of G being a cut-vertex of Z(G).
140083992
Boosting image classification with LDA-based feature combination for digital photograph management
Image classification is of great importance for digital photograph management. In this paper we propose a general statistical learning method based on boosting algorithm to perform image classification for photograph annotation and management. The proposed method employs both features extracted from image content (i.e., color moment and edge direction histogram) and features from the EXIF metadata recorded by digital cameras. To fully utilize potential feature correlations and improve the classification accuracy, feature combination is needed. We incorporate linear discriminant analysis (LDA) algorithm to implement linear combinations between selected features and generate new combined features. The combined features are used along with the original features in boosting algorithm for improving classification performance. To make the proposed learning algorithm more efficient, we present two heuristics for selective feature combinations, which can significantly reduce training computation without losing performance. The proposed image classification method has several advantages: small model size, computational efficiency and improved classification performance based on LDA feature combination.
36919902
Motion planning with uncertainty
We present a general framework for motion planning of robots in the presence of obstacles and other robots. We use variational calculus and optimization to find optimal open loop and closed loop plans in the presence of uncertainty. The plans are based on world models with set-valued uncertainty associated with the positions and shape of the obstacles. The open loop plans are generated by an efficient method that allows successive refinements of a nominal motion plan and accommodates finer levels of granularity as additional information becomes available. The closed loop plans are control policies that are based on given sensor models. The optimal plan is a control policy that performs the best in the worst-case situation. We discuss how the open loop and closed loop plans can be viewed as optimal strategies in the framework of two-person, zero-sum, non-cooperative games.