3D visualization of large-scale virtual crowds is a very important and interesting problem in research fields of virtual reality. For reasons of efficiency and visual realism, it is very difficult to populate, animate...
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3D visualization of large-scale virtual crowds is a very important and interesting problem in research fields of virtual reality. For reasons of efficiency and visual realism, it is very difficult to populate, animate and render large-scale virtual crowds with hundreds of thousand individually animated virtual characters in real-time applications;especially for the scene which has more than ten thousands of individuals with different shapes and motions. In this paper, an efficient method to visualize large-scale virtual crowds is presented. Firstly, using model variation technique, many different models can be derived from a small number of model templates. Secondly, the crowds can be animated individually by deforming a small number of elements in motion database. Thirdly, using a developed point sample rendering algorithm, large-scale crowds can be displayed in real-time. This method can be used to visualize different dynamic crowds which require both real-time efficiency and large number of virtual individuals support. Based on this work, an efficient and readily usable 3D visualization system is presented. It can provide very high visual realism for large crowds' visualization in interactive frame rates on a regular PC. The 3D visualization of 60000 people evacuating from a building is also realized in real-time based on the system.
Topic models have been successfully used to information classification and retrieval. These models can capture word correlations in a collection of textual documents with a low-dimensional set of multinomial distribut...
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Topic models have been successfully used to information classification and retrieval. These models can capture word correlations in a collection of textual documents with a low-dimensional set of multinomial distribution, called "topics". It is important but difficult to select an appropriate number of topics for a specific dataset. This paper proposes a theorem that the model reaches optimum as the average similarity among topics reaches minimum, and based on this theorem, proposes a method of adaptively selecting the best LDA model based on density. Experiments show that the proposed method can achieve performance matching the best of LDA without manually tuning the number of topics.
Resource Space Model (RSM) is a semantic model to manage resources in the future interconnection environment. The query capability is an important aspect of RSM as a semantic resource management model. This paper repo...
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A novel and efficient speckle noise reduction algorithm based on Bayesian contourlet shrinkage using contourlet transform is ***,we show the sub-band decompositions of SAR images using contourle transforms,which provi...
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A novel and efficient speckle noise reduction algorithm based on Bayesian contourlet shrinkage using contourlet transform is ***,we show the sub-band decompositions of SAR images using contourle transforms,which provides sparse representation at both spatial and directional ***,a Bayesian contourlet shrinkage factor is applied to the decomposed data to estimate the best value for noise-free contourle *** results show that compared with conventional wavelet despeckling algorithm,the proposed algorithm can achieve an excellent balance between suppresses speckle effectively and preserve image details,and the significant information of origina image like textures and contour details is well ma intained.
The limit behaviors of computations have not been fully *** is necessary to consider such limit behaviors when we consider the properties of infinite objects in computer science,such as infinite logic programs,the sym...
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The limit behaviors of computations have not been fully *** is necessary to consider such limit behaviors when we consider the properties of infinite objects in computer science,such as infinite logic programs,the symbolic solutions of infinite polynomial ***,we can use finite objects to approximate infinite objects,and we should know what kinds of infinite objects are approximable and how to approximate them effectively.A sequence {Rκ:κω}of term rewriting systems has the well limit behavior if under the condition that the sequence has the Set-theoretic limit or the distance-based limit,the sequence {Th(Rκ):κ∈ω} of corresponding theoretic closures of Rκ has the set-theoretic or distance-based limit,and limκ→∞ Th(Rκ) is equal to the theoretic closure of the limit of {Rκ:κ∈ω).Two kinds of limits of term rewriting systems are considered:one is based on the set-theoretic limit,the other is on the distance-based *** is proved thatgiven a sequence {Rκ:κ∈ω) of term rewriting systems Rκ,if there is a well-founded ordering (-＜) on terms such that every Rκ is (-＜)-well-founded,and the set-theoretic limit of {Rκ:κ∈ω).exists,then {Rκ:κ∈ω).has the well limit behavior;and if (1) there is a well-founded ordering(-＜)on terms such that every Rκ is(-＜-well-founded,(2) there is a distance d on terms which is closed under substitutions and contexts and (3) {Rκ:κ∈ω).is Cauchy under d then {Rκ:κ∈ω).has the well limit *** results are used to approximate the least Herbrand models of infinite Horn logic programs and real Horn logic programs,and the solutions and Cr(o)bner bases of (infinite) sets of real polynomials by sequences of (finite) sets of rational polynomials.
This paper illustrates the advantages of using the Discrete Cosine Transform (DCT) as compared to the standard Discrete Fourier Transform (DFT) for the purpose of removing random noise embedded in seismic data. The pr...
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Adaptive multiple subtraction is a critical and challenging procedure for the widely-used surface-related multiple attenuation (SRMA) techniques. The problem encountered in this field is that a good result is usually ...
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According to support vector machines (SVMs), for those geometric approach based classification methods, examples close to the class boundary usually are more informative than others. Taking face detection as an exampl...
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According to support vector machines (SVMs), for those geometric approach based classification methods, examples close to the class boundary usually are more informative than others. Taking face detection as an example, this paper addresses the problem of enhancing given training set and presents a nonlinear method to tackle the problem effectively. Based on SVM and improved reduced set algorithm (IRS), the method generates new examples lying close to the face/non-face class boundary to enlarge the original dataset and hence improve its sample distribution. The new IRS algorithm has greatly improved the approximation performance of the original reduced set (RS) method by embedding a new distance metric called image Euclidean distance (IMED) into the kernel function. To verify the generalization capability of the proposed method, the enhanced dataset is used to train an AdaBoost-based face detector and test it on the MIT+CMU frontal face test set. The experimental results show that the original collected database can be enhanced effectively by the proposed method to learn a face detector with improved generalization performance.
In this paper, a fast global motion estimation method is proposed for video coding. This method can accommodate not only a translational motion model but also a polynomial motion model. It speeds up the procedure of t...
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ISBN:
(纸本)9780819469533
In this paper, a fast global motion estimation method is proposed for video coding. This method can accommodate not only a translational motion model but also a polynomial motion model. It speeds up the procedure of the global motion estimation (GME) by pre-analyzing the characteristics of the block. At the first stage, the smooth region blocks which contribute less to the GME are filtered by using a threshold method based on image intensity. Next, a threshold method based on the discrepancy of the motion vectors is used to exclude the foreground blocks from the GME. From the experimental results, we can conclude that the proposed fast global motion estimation method manages to speed up the processing of estimating the motion vector field while maintaining the coding performance.
This paper describes a novel model using dependency structures on the source side for syntax-based statistical machine translation: Dependency Treelet String Correspondence Model (DTSC). The DTSC model maps source dep...
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