Xiaoqing Yu

Shanghai University, Shanghai, Shanghai Shi, China

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Publications (84)4.56 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we present a robust reconstruction framework on noisy and large point cloud data. Though Poisson reconstruction performs well in recovering the surface from noisy point cloud data, it's problematic to reconstruct underlying surface from large cloud data, especially on a general processor. An inaccurate estimation of point normal for noisy and large dataset would result in local distortion on the reconstructed mesh. We adopt a systematical combination of Poisson-disk sampling, normal estimation and Poisson reconstruction to avoid the inaccuracy of normal calculated from k-nearest neighbors. With the fewer dataset obtained by sampling on original points, the normal estimated is more reliable for subsequent Poisson reconstruction and the time spent in normal estimation and reconstruction is much less. We demonstrate the effectiveness of the framework in recovering topology and geometry information when dealing with point cloud data from real world. The experiment results indicate that the framework is superior to Poisson reconstruction directly on raw point dataset in the aspects of time consumption and visual fidelity.
    2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid); 05/2014
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    ABSTRACT: 3D visualization and real-time rendering of large-scale scenes is an important task of virtual reality. In this paper, different from local loading and roaming of the scenes, we focus on dynamic loading and real-time roaming, based on the remote servers and clients, to facilitate the efficient transmission and reduce the transmission time. More specifically, a novel dynamic scheduling algorithm in the client side is used to optimize the loading and real-time rendering performance, i.e., this method can dynamically load and unload the partitioned data blocks from the server side according to the roaming viewpoints so that we can realize the infinite roaming of a large scale scene. In order to accommodate different networking scenarios, we also design a multi-resolution scheme for those server-stored scene blocks according to different communication channel conditions, so that the most suitable resolution scheme can be automatically chosen. As shown in the experiments, our method only requires a small amount of memory overhead, while efficiently realizes infinite roaming of the large city scenes.
    Journal of Signal Processing Systems 04/2014; 75(1):15-21. · 0.56 Impact Factor
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    ABSTRACT: Particle systems' greatest advantage is well suited for modeling complex fuzzy phenomena, such as explosions, fountain, tornado and fireworks, etc. in 3D graphics. With the increasing requirements on the number of particles and particle-particle interactions, the computational complexity of simulation in particle systems has increased rapidly. Particle systems are traditionally implemented on a general-purpose CPU, and the computational complexity of particle systems limits the number of particles that can be computed at interactive rates. This paper focuses on real-time simulation of large-scale particle systems. We discuss optional integration algorithms based on CUDA (Compute Unified Device Architecture) for both graphic and scientific simulation. The speed of particle systems has been greatly improved, with parallel-core GPUs working in tandem with multi-core CPUs. In order to provide a scalable and portable API library, the object-oriented programming method is adopted to encapsulate the functions of parallel particle system. Results show that our proposed APIs are user-friendly and the parallel implementations are significantly efficient.
    2013 IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC); 12/2013
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    ABSTRACT: With the dramatic increase of the audio resources, how to achieve a multi-classifier of high performance for audio resources has become one of the research focuses. In this paper, we propose a novel classifier using SVM decision tree named as Dual Parallel SVM (DP-SVM). The key point of this method is that all the audio categories are divided into two local sub-classes each time till all nodes turns into leaf nodes. Rather than one branch is on behalf of one class, the other branch contains all of the remaining categories, and we can construct a new decision tree. Based on this SVM decision tree, Genetic Algorithm is adopted to optimize parameters. Better parameters are obtained with iterative selection, crossover and mutation operations on templates. Experimental results show that the new DP-SVM constructor based on Genetic Algorithm can effectively accelerate the speed of training and classification, and it can also improve the accuracy of classification.
    Journal of Computational and Theoretical Nanoscience 03/2013; 19(3):746-752. · 1.03 Impact Factor
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    ABSTRACT: This paper introduces the calculation method of degree centrality, closeness centrality and betwenness centrality based on the Sina weibo user data of Fudan University in Dec, 2012. The cumulative probability distribution of each centrality indicator is measured and analyzed. The relationship among three centrality metrics is also discussed. It is concluded that the combination of the three centrality measurements may effectively discover important nodes in the microblog network.
    IET International Conference on Smart and Sustainable City 2013 (ICSSC 2013); 01/2013
  • Liang Liu, Libing Lu, Xiaoqing Yu, Ran Liu
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    ABSTRACT: In this paper, first we introduce the principle and method of the point cloud registration. Then we study the registration method based on target ball, which mainly includes locating the center of target balls and the registration among multistation point cloud. Here, we proposed a new method that scan the same ball more time and then get the average of them to locate the centre. At last, we obtain multi-station scanning point cloud using Faro laser scanner and achieve the result of registration of multi-station point cloud using target ball method.
    IET International Conference on Smart and Sustainable City 2013 (ICSSC 2013); 01/2013
  • Wei Xiong, Xiaoqing Yu, Jianhua Shi
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    ABSTRACT: An audio fingerprint is a content-based compact feature that summarizes an audio clip. As same as we using human fingerprint to recognize people, audio fingerprint can be used to retrieval unknown audio clips. In this paper, we propose an algorithm which extracts audio fingerprint based on spectral bark-band energy and PCA (Principle Components Analysis). By using this algorithm, search a huge audio database efficient and highly robust in the presence of noise be possible. Especially, the algorithm enhances the efficiency to a higher level. The preliminary result of our experiments shows prominent performance.
    IET International Conference on Smart and Sustainable City 2013 (ICSSC 2013); 01/2013
  • Xiaoqing Yu, Jing Lu, Huanhuan Liu
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    ABSTRACT: Opinion leaders have an important influence on message propagation, so how to find opinion leaders efficiently is significance for further studying. In this paper, we proposed synthesize centrality as a new method to find opinion leaders. PageRank and HITS are presented to ranking influential, experimental results show that large overlap exists between SC and PageRank and HITS. If a user is an opinion leader with SC, it is likely to be opinion leaders with PageRank and HITS. And SC has a higher accuracy in finding opinion leaders than PageRank and HITS.
    IET International Conference on Smart and Sustainable City 2013 (ICSSC 2013); 01/2013
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    ABSTRACT: VOIP is widely used in modern communication. Based on the VOIP theory, we design the conference mixer system. It combines communication protocol, voice compression, mixer processor and network transmission. This paper shows how to realize the whole conference mixer system and tests performance of it.
    IET International Conference on Smart and Sustainable City 2013 (ICSSC 2013); 01/2013
  • Xiaoqing Yu, Wei Xiong, Jianhua Shi
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    ABSTRACT: Chroma-based feature is one of the most useful information in the area of music retrieval. In this paper, a novel music retrieval system based on chroma feature been proposed. The core of this system is the music fingerprint that be extracted from the chroma feature. Beyond that, notes detection technique also has been implemented in our system to assist the retrieval process. The preliminary result of experiments shows the proposed system occupy both speed and accuracy.
    IET International Conference on Smart and Sustainable City 2013 (ICSSC 2013); 01/2013
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    ABSTRACT: With the rapid development of digital media technology, audio resource has become one important sources of information, but people may be confused because they do not know how to handle these resources. In this paper, we proposed a new audio fingerprinting system based on Shazam's algorithm to help people manage and find out the wanted audio resources. Salient points, not only the maxima but also the points have the largest increases in energy, have been extracted in the chromagram to construct the audio fingerprints. The idea which is similar to Constant Q Transform is adopted to optimize the salient points before the fingerprinting construction. The experiment results show that our system can recognize the wanted audio clips at the accuracy about 90%.
    IET International Conference on Smart and Sustainable City 2013 (ICSSC 2013); 01/2013
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    ABSTRACT: This paper proposes a novel algorithm for high-dimensional unsupervised reduction from intrinsic Bayesian model. The proposed algorithm is to assume that the pixel reflectance results from nonlinear combinations of pure component spectra contaminated by additive noise. The constraints are naturally expressed in intrinsic Bayesian literature by using appropriate abundance prior distributions. The posterior distributions of the unknown model parameters are then derived. The proposed algorithm consists of intrinsic Bayesian inductive cognition part and hierarchical reduction algorithm model part. The algorithm has several advantages over traditional distance based on Bayesian reduction algorithms. The proposed reduction algorithm from intrinsic Bayesian inductive cognitive model is used to decide which dimensions are advantageous and to output the recommended dimensions of the hyperspectral image. The algorithm can be interpreted as a novel fast reduction inference method for intrinsic Bayesian inductive cognitive model. We describe procedures for learning the model hyperparameters, computing the dimensions distribution, and extensions to the intrinsic Bayesian inductive cognition model. Experimental results on hyperspectral data demonstrate robust and useful properties of the proposed reduction algorithm.
    Neurocomputing 12/2012; 98:143–150. · 2.01 Impact Factor
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    ABSTRACT: This paper proposed an improved objective audio quality estimation system compatible with PEAQ (Perceptual Evaluation of Audio Quality). Based on the computational auditory model, we used a novel psychoacoustic model to assess the quality of highly impaired audio. We also applied the robust linear MOA (Least-squares Weight Vector algorithm) and MinmaxMOA (Minmax-Optimized MOV Selection algorithm) to cognitive model of the estimation system. Compared to the PEAQ advanced version, the proposed estimation system has a considerable improvement in performance both in terms of the correlation and MSE (Mean Square Error). By combining the computational auditory model and PEAQ, our estimation system can be applied to the quality assessment of highly impaired audio.
    Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV; 11/2012
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    ABSTRACT: To dynamically load the large scaled 3D city model based on OSG (OpenSceneGraph), the model is firstly separated small blocks so as to improve the loading speed and save the memory, here we directly separate the scene along the x-axis and y-axis, then we design the algorithm to control the numbers and the positions of loading blocks, thirdly we optimize the algorithm. In the end we give the experimental result to show the virtues of our methods.
    2012 International Conference on Audio, Language and Image Processing (ICALIP); 07/2012
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    ABSTRACT: Audio fingerprint is a content-based compact feature that summarizes an audio clip. As same as we using human fingerprint to recognize people, audio fingerprint can be used to retrieval unknown audio clips. In this paper, we propose an algorithm which extracts audio fingerprint based on dynamic subband locating and normalized SSC (Spectral Subband Centroid). SSC has been found to be related the human sensation of the brightness of an audio signal. By using this algorithm, recognize an unknown audio clip efficient and highly robust in the presence of noise be possible. The preliminary result of our experiments shows prominent performance.
    Audio, Language and Image Processing (ICALIP), 2012 International Conference on; 01/2012
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    ABSTRACT: A theoretical framework of compressed sensing are represented, for real, no-spare especial audio signals, the short-term stability of these signals is analyzed, these signals are compressed using compressed sensing method, the performance of different reconstruction algorithms are studied for original audio signals based on the number of measurements and the length of frame. The experiment results show that the reconstruction performance associated with the number of measurements and the length of frames. The best reconstruction performance in all algorithms we used is OMP, the speed calculation of OMP also is good.
    Audio, Language and Image Processing (ICALIP), 2012 International Conference on; 01/2012
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    ABSTRACT: In this paper, we realize the roaming of large scale city scene based on OSG. For large scale city, we separate it to blocks and put the blocks to the server, in order to realize rendering in client, we need to transmit the needed block to client and then render them. But the loading from server to client is a time-consuming course. General ways can't guarantee fluent roaming, here we adopt dual threads to fulfill this attitude, one thread is responsible for data transmission through net, and the other is responsible for the block's rendering. Through the coordinating work of the two threads, we realize the fluent rendering of the large city scene.
    Audio, Language and Image Processing (ICALIP), 2012 International Conference on; 01/2012
  • Jie Hu, Wang gen Wan, Xiaoqing Yu
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    ABSTRACT: Pathfinding in the context of video games concerns the way in which a moving entity finds a path around obstacles; the most frequent context is real-time strategy games (in which the player directs units around a play area containing obstacles), but forms of this are found in most modern video games. It is probably the most popular but frustrating game artificial intelligence (AI) problem in game industry. This article introduces A-Star algorithm first, then describes the details of A-Star algorithm. Finally, a real example of how the A-Star pathfinding technique is used in a famous 3D game engine: Unity3D.
    Audio, Language and Image Processing (ICALIP), 2012 International Conference on; 01/2012
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    ABSTRACT: The progressive mesh compression algorithm on the blocks of model based on edge-collapse mesh simplification is proposed to solve some practical problems. At first, we adopt K-means to cluster the mesh triangles. Secondly, we compress each model block. The detail information of different groups obtained by compressing each of blocks are independent of each other. If some details are lost in transmission, other details can also decode independently. We only need to request server to resend the lost part of the groups data. At the same time, it can be decoded regional details depend on our viewpoint when we need to load the complex models. Because our vision can only focus on part region of model, and the area in which we isn't interested can be instead by coarse model.
    Audio, Language and Image Processing (ICALIP), 2012 International Conference on; 01/2012
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    ABSTRACT: In this paper, we provide a new method for rendering of large-scale 3D terrain point cloud based on bounding volume hierarchy (BVH), level of detail (LOD) and out-of-core algorithm. First, large-scale 3D terrain point clouds are partitioned into several same size small blocks according to its size. Then small blocks are reconstructed to mesh models and added texture mappings. Thirdly, we use BVH to organize scene data and create two levels to handle node loading and computer graphics rendering separately. Fourthly, we use view-dependent out-of-core method to determine which part of meshes should be rendering or deleted from the memory. In the last of the paper, we use terrain models reconstructed from point clouds to test that our method has higher usage efficiency of computer memory and rendering rates.
    Audio, Language and Image Processing (ICALIP), 2012 International Conference on; 01/2012

Publication Stats

36 Citations
4.56 Total Impact Points

Institutions

  • 1998–2014
    • Shanghai University
      • School of Communication and Information Engineering (SCIE)
      Shanghai, Shanghai Shi, China
  • 2001
    • The Hong Kong Polytechnic University
      • Department of Electronic and Information Engineering
      Hong Kong, Hong Kong