Junfeng Yao

Path Planning for Virtual Human Motion Using Improved A* Star Algorithm

Calculating and generating optimal motion path automatically is one of the key issues in virtual human motion path planning. To solve the point, the improved A* algorithm has been analyzed and realized in this paper, we modified the traditional A* algorithm by weighted processing of evaluation function, which made the searching steps reduced from 200 to 80 and searching time reduced from 4.359s to 2.823s in the feasible path planning. The artificial searching marker, which can escape from the barrier trap effectively and quickly, is also introduced to avoid searching the invalid region repeatedly, making the algorithm more effective and accurate in finding the feasible path in unknown environments. We solve the issue of virtual humans obstacle avoidance and navigation through optimizing the feasible path to get the shortest path.

Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition

Implicit discourse relation recognition remains a serious challenge due to the absence of discourse connectives. In this paper, we propose a Shallow Convolutional Neural Network (SCNN) for implicit discourse relation recognition, which contains only one hidden layer but is effective in relation recognition. The shallow structure alleviates the overfitting problem, while the convolution and nonlinear operations help preserve the recognition and generalization ability of our model. Experiments on the benchmark data set show that our model achieves comparable and even better performance when comparing against current state-of-the-art systems.

Cloud computing and its key techniques

Cloud computing is a new computing model; it is developed based on grid computing. The authors introduced the development history of cloud computing; took cloud computing of Google techniques as an example, summed up key techniques, such as data storage technology (Google File System), data management technology (Big Table), as well as programming model and task scheduling model (Map-Reduce), used in cloud computing. Finally the paper analyses the challenge of cloud computing and pointed out the broad development prospects of cloud computing.

Bilingual Correspondence Recursive Autoencoder for Statistical Machine Translation

Learning semantic representations and tree structures of bilingual phrases is beneficial for statistical machine translation. In this paper, we propose a new neural network model called Bilingual Correspondence Recursive Autoencoder (BCorrRAE) to model bilingual phrases in translation. We incorporate word alignments into BCorrRAE to allow it freely access bilingual constraints at different levels. BCorrRAE minimizes a joint objective on the combination of a recursive autoencoder reconstruction error, a structural alignment consistency error and a crosslingual reconstruction error so as to not only generate alignment-consistent phrase structures, but also capture different levels of semantic relations within bilingual phrases. In order to examine the effectiveness of BCorrRAE, we incorporate both semantic and structural similarity features built on bilingual phrase representations and tree structures learned by BCorrRAE into a state-of-the-art SMT system. Experiments on NIST Chinese-English test sets show that our model achieves a substantial improvement of up to 1.55 BLEU points over the baseline.

3D vasculature segmentation using localized hybrid level-set method

BackgroundIntensity inhomogeneity occurs in many medical images, especially in vessel images. Overcoming the difficulty due to image inhomogeneity is crucial for the segmentation of vessel image.MethodsThis paper proposes a localized hybrid level-set method for the segmentation of 3D vessel image. The proposed method integrates both local region information and boundary information for vessel segmentation, which is essential for the accurate extraction of tiny vessel structures. The local intensity information is firstly embedded into a region-based contour model, and then incorporated into the level-set formulation of the geodesic active contour model. Compared with the preset global threshold based method, the use of automatically calculated local thresholds enables the extraction of the local image information, which is essential for the segmentation of vessel images.ResultsExperiments carried out on the segmentation of 3D vessel images demonstrate the strengths of using locally specified dynamic thresholds in our level-set method. Furthermore, both qualitative comparison and quantitative validations have been performed to evaluate the effectiveness of our proposed model.ConclusionsExperimental results and validations demonstrate that our proposed model can achieve more promising segmentation results than the original hybrid method does.

Sparse Localized Decomposition of Deformation Gradients

Sparse localized decomposition is a useful technique to extract meaningful deformation components out of a training set of mesh data. However, existing methods cannot capture large rotational motion in the given mesh dataset. In this paper we present a new decomposition technique based on deformation gradients. Given a mesh dataset, the deformation gradient field is extracted, and decomposed into two groups: rotation field and stretching field, through polar decomposition. These two groups of deformation information are further processed through the sparse localized decomposition into the desired components. These sparse localized components can be linearly combined to form a meaningful deformation gradient field, and can be used to reconstruct the mesh through a least squares optimization step. Our experiments show that the proposed method addresses the rotation problem associated with traditional deformation decomposition techniques, making it suitable to handle not only stretched deformations, but also articulated motions that involve large rotations.

Objective Classification Using Advanced Adaboost Algorithm

Adaboost, a general method for improving the accuracy of any given learning algorithm, is usually used to solve the problem of object detection based on cascade structure. However it has some disadvantage. The paper proposes an advanced Adaboost algorithm for object detection. The algorithm adopts a new method to update weighted parameters of weak classifiers. The weights are affected not only by the error rates, but also by their capacity of positive recognition. It is more adaptive to the object detection by decreasing the false alarm rates in the low false rejection rate terminal. The experiment results show the improvement achieved by the new algorithm.

A Context-Aware Topic Model for Statistical Machine Translation

Lexical selection is crucial for statistical machine translation. Previous studies separately exploit sentence-level contexts and documentlevel topics for lexical selection, neglecting their correlations. In this paper, we propose a context-aware topic model for lexical selection, which not only models local contexts and global topics but also captures their correlations. The model uses target-side translations as hidden variables to connect document topics and source-side local contextual words. In order to learn hidden variables and distributions from data, we introduce a Gibbs sampling algorithm for statistical estimation and inference. A new translation probability based on distributions learned by the model is integrated into a translation system for lexical selection. Experiment results on NIST ChineseEnglish test sets demonstrate that 1) our model significantly outperforms previous lexical selection methods and 2) modeling correlations between local words and global topics can further improve translation quality.

A surgical training system for four medical punctures based on virtual reality and haptic feedback

This poster presents a surgical training system for four medical punctures based on virtual reality and haptic feedback, including a client program developed in the Unity3D game engine and a server program developed by PHP. This system provides the immersive surgery simulation for thoracentesis, lumbar puncture, bone marrow puncture and abdominal paracentesis that we call four medical punctures. Trainers or teachers can release training tasks in which trainees or students are able to learn surgery skills at a 3D visual scene. Furthermore, they will feel a sense of immediacy when putting on the head-mounted display and with the help of haptic feedback. The training records will be put into database for analysis.

An implicit skeleton-based method for the geometry reconstruction of vasculatures

Due to the high complexity of vascular system network, the geometry reconstruction of vasculatures from raw medical datasets remains a very challenging task. In this paper, we present a novel skeleton-based method for the geometry reconstruction of vascular structures from standard 3D medical datasets. With the proposed techniques, the geometry of vascular structures with high level of smoothness and accuracy can be reconstructed from the raw medical datasets. The experimental results and comparison with other techniques demonstrate that our method can achieve faithful and smooth vascular structures. In addition, quantitative validation has been conducted to evaluate the accuracy and smoothness of the reconstructed vessel geometry based on the proposed method.