8788311
Factoring Synchronous Grammars by Sorting
Synchronous Context-Free Grammars (SCFGs) have been successfully exploited as translation models in machine translation applications. When parsing with an SCFG, computational complexity grows exponentially with the length of the rules, in the worst case. In this paper we examine the problem of factorizing each rule of an input SCFG to a generatively equivalent set of rules, each having the smallest possible length. Our algorithm works in time O(n log n), for each rule of length n. This improves upon previous results and solves an open problem about recognizing permutations that can be factored.
2506060
Synchronous Binarization for Machine Translation
Systems based on synchronous grammars and tree transducers promise to improve the quality of statistical machine translation output, but are often very computationally intensive. The complexity is exponential in the size of individual grammar rules due to arbitrary re-orderings between the two languages, and rules extracted from parallel corpora can be quite large. We devise a linear-time algorithm for factoring syntactic re-orderings by binarizing synchronous rules when possible and show that the resulting rule set significantly improves the speed and accuracy of a state-of-the-art syntax-based machine translation system.
9323396
Inducing Word Alignments with Bilexical Synchronous Trees
This paper compares different bilexical tree-based models for bilingual alignment. EM training for the new model benefits from the dynamic programming "hook trick". The model produces improved dependency structure for both languages.
11755901
Interactive mesh deformation with pseudo material effects
This paper presents a novel interactive mesh deformation method that can achieve various dynamic material effects, including elastic membrane and cloth effects. In our framework, a mesh is encoded by some differential quantities based on edge length and dihedral angle; and the deformation is formulated as a least square problem for preserving the edge length and dihedral angle via the differential quantities. In order to obtain anisotropic material effects, we further propose an edge-weighting scheme based on a user-specified vector field. To avoid specifying the local transformations, we set up an iterative scheme for solving the deformation. At last, several examples are presented to show that our approach can interactively generate visually pleasing deformations. Copyright © 2006 John Wiley & Sons, Ltd.
18572663
Bernoulli Embedding Model and Its Application in Texture Mapping
A novel texture mapping technique is proposed based on nonlinear dimension reduction, called Bernoulli logistic embedding (BLE). Our probabilistic embedding model builds texture mapping with minimal shearing effects. A log-likelihood function, related to the Bregman distance, is used to measure the similarity between two related matrices defined over the spaces before and after embedding. Low-dimensional embeddings can then be obtained through minimizing this function by a fast block relaxation algorithm. To achieve better quality of texture mapping, the embedded results are adopted as initial values for mapping enhancement by stretch-minimizing. Our method can be applied to both complex mesh surfaces and dense point clouds.
8107037
Forward Shadow Mapping
Forward shadow mapping is a new approach to real-time shadow generation. The traditional shadow map algorithm maps the pixels in the eye’s view backward into the depth buffers of light sources (i.e. shadow maps), which is similiar to and often implemented as an extension to texture mapping. Our algorithm reverses this process by using 3-D image warping techniques to transform shadow map pixels forward into the eye’s view to directly indicate which pixels are lit; it does not interfere with normal texture mapping and easily supports antialiased shadow edges and projective textures. Access to shadow maps and projective textures is in pixel-sequential order. This algorithm has advantages when speed of texture mapping becomes the performance bottleneck, which is often the case in visual simulation and game applications.
34749580
Plane elementary bipartite graphs
A connected graph is called elementary if the union of all perfect matchings forms a connected subgraph. In this paper we mainly study various properties of plane elementary bipartite graphs so that many important results previously obtained for hexagonal systems are treated in a unified way. Firstly, we show that a plane bipartite graph G is elementary if and only if the boundary of each face (including the infinite face) is an alternating cycle with respect to some perfect matching of G. For a plane bipartite graph G all interior vertices of which are of the same degree, a stronger result is obtained; namely, G is elementary if and only if the boundary of the infinite face of G is an alternating cycle with respect to some perfect matching of G. Second, the concept of the Z-transformation graph Z(G) of a hexagonal system G (whose vertices represent the perfect matchings of G) is extended to a plane bipartite graph G and some results analogous to those for hexagonal systems are obtained. A peripheral face f of G is called reducible if the removal of the internal vertices and edges of the path that is the intersection of f and the exterior face of G results in a plane elementary bipartite graph. Thirdly, we obtain the reducible face decomposition for plane elementary bipartite graphs. Furthermore, sharp upper and lower bounds for the number of reducible faces are derived. Conversely, we can construct any plane elementary bipartite graphs by adding new peripheral faces one by one. As applications of this approach, we give simple construction methods for several types of plane elementary bipartite graphs G that contain a forcing edge (which belongs to exactly one perfect matching of G) and whose Z-transformation graphs Z(G) contain vertices of degree one.
1131614
Meta-Evaluation of Image Segmentation Using Machine Learning
Image segmentation is a fundamental step in many computer vision applications. Generally, the choice of a segmentation algorithm, or parameterization of a given algorithm, is selected at the application level and fixed for all images within that application. Our goal is to create a stand-alone method to evaluate segmentation quality. Stand-alone methods have the advantage that they do not require a manually-segmented reference image for comparison, and can therefore be used for real-time evaluation. Current stand-alone evaluation methods often work well for some types of images, but poorly for others. We propose a meta-evaluation method in which any set of base evaluation methods are combined by a machine learning algorithm that coalesces their evaluations based on a learned weighting function, which depends upon the image to be segmented. The training data used by the machine learning algorithm can be labeled by a human, based on similarity to a human-generated reference segmentation, or based upon system-level performance. Experimental results demonstrate that our method performs better than the existing stand-alone segmentation evaluation methods.