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.
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.
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.
7092740
Representing Conditional Independence Using Decision Trees
While the representation of decision trees is fully expressive theoretically, it has been observed that traditional decision trees has the replication problem. This problem makes decision trees to be large and learnable only when sufficient training data are available. In this paper, we present a new representation model, conditional independence trees (CITrees), to tackle the replication problem from probability perspective. We propose a novel algorithm for learning CITrees. Our experiments show that CITrees outperform naive Bayes (Langley, Iba, & Thomas 1992), C4.5 (Quinlan 1993), TAN (Friedman, Geiger, & Goldszmidt 1997), and AODE (Webb, Boughton, & Wang 2005) significantly in classification accuracy.
17695045
A Multi-Agent Approach for Peer-to-Peer Based Information Retrieval System
This paper develops and analyzes distributed search techniques for use in a peer-to-peer (P2P) network-based Information Retrieval (IR) system. In the absence of a centralized mediator with global knowledge that directs requests to appropriate agents, agents must cooperate to forward the queries among themselves so as to find appropriate agents, and return and merge the results in order to fulfill the information retrieval task in a distributed environment. In our approach, the agent society is connected through an agent-view structure maintained by each agent. Initially, the agent-view structures are formed by agents connecting to each other randomly. However, we show that such an approach can be significantly enhanced by dynamically reorganizing the underlying agent-view topology and deploying contextsensitive distributed search algorithms. Experimental results indicate that appropriate organizational structures and distributed search mechanisms can have a positive influence on system performance.
14861274
Learning in Region-Based Image Retrieval
In this paper, several effective learning algorithms using global image representations are adjusted and introduced to regionbased image retrieval (RBIR). First, the query point movement technique is considered. By assembling all the segmented regions of positive examples together and resizing the regions to emphasize the latest positive examples, a composite image is formed as the new query. Second, the application of support vector machines (SVM) in relevance feedback for RBIR is investigated. Both the one class SVM as a class distribution estimator and two classes SVM as a classifier are taken into account. For the latter, two representative display strategies are studied. Last, a region re-weighting algorithm is proposed inspired by those feature re-weighting ones. Experimental results on a database of 10,000 general-purpose images demonstrate the effectiveness of the proposed learning algorithms.
2128098
A Fast Decision Tree Learning Algorithm
There is growing interest in scaling up the widely-used decision-tree learning algorithms to very large data sets. Although numerous diverse techniques have been proposed, a fast tree-growing algorithm without substantial decrease in accuracy and substantial increase in space complexity is essential. In this paper, we present a novel, fast decision-tree learning algorithm that is based on a conditional independence assumption. The new algorithm has a time complexity of O(m ċ n), where m is the size of the training data and n is the number of attributes. This is a significant asymptotic improvement over the time complexity O(m ċ n2) of the standard decision-tree learning algorithm C4.5, with an additional space increase of only O(n). Experiments show that our algorithm performs competitively with C4.5 in accuracy on a large number of UCI benchmark data sets, and performs even better and significantly faster than C4.5 on a large number of text classification data sets. The time complexity of our algorithm is as low as naive Bayes'. Indeed, it is as fast as naive Bayes but outperforms naive Bayes in accuracy according to our experiments. Our algorithm is a core tree-growing algorithm that can be combined with other scaling-up techniques to achieve further speedup.
14971297
Non-uniform Differential Mesh Deformation
In this paper, we propose a novel mesh deformation approach via manipulating differential properties non-uniformly. Guided by user-specified material properties, our method can deform the surface mesh in a non-uniform way while previous deformation techniques are mainly designed for uniform materials. The non-uniform deformation is achieved by material-dependent gradient field manipulation and Poisson-based reconstruction. Comparing with previous material-oblivious deformation techniques, our method supplies finer control of the deformation process and can generate more realistic results. We propose a novel detail representation that transforms geometric details between successive surface levels as a combination of dihedral angles and barycentric coordinates. This detail representation is similarity-invariant and fully compatible with material properties. Based on these two methods, we implement a multiresolution deformation tool, which allows the user to edit a mesh inside a hierarchy in a material-aware manner. We demonstrate the effectiveness and robustness of our methods by several examples with real-world data.