State Key Laboratory of CAD and CG


State Key Laboratory of CAD and CG

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Chen H.,State Key Laboratory of CAD and CG | Chen W.,State Key Laboratory of CAD and CG | Chen W.,Zhejiang University | Mei H.,State Key Laboratory of CAD and CG | And 5 more authors.
IEEE Transactions on Visualization and Computer Graphics | Year: 2014

Scatterplots are widely used to visualize scatter dataset for exploring outliers, clusters, local trends, and correlations. Depicting multi-class scattered points within a single scatterplot view, however, may suffer from heavy overdraw, making it inefficient for data analysis. This paper presents a new visual abstraction scheme that employs a hierarchical multi-class sampling technique to show a feature-preserving simplification. To enhance the density contrast, the colors of multiple classes are optimized by taking the multi-class point distributions into account. We design a visual exploration system that supports visual inspection and quantitative analysis from different perspectives. We have applied our system to several challenging datasets, and the results demonstrate the efficiency of our approach. © 1995-2012 IEEE.

Chen F.,Zhejiang University | Chen F.,Jimei University | Yu H.,Zhejiang University | Yu H.,State Key Laboratory of CAD and CG | And 2 more authors.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2013

In this paper we introduce a new shape-driven approach for object segmentation. Given a training set of shapes, we first use deep Boltzmann machine to learn the hierarchical architecture of shape priors. This learned hierarchical architecture is then used to model shape variations of global and local structures in an energetic form. Finally, it is applied to data-driven variational methods to perform object extraction of corrupted data based on shape probabilistic representation. Experiments demonstrate that our model can be applied to dataset of arbitrary prior shapes, and can cope with image noise and clutter, as well as partial occlusions. © 2013 IEEE.

Chen F.,Zhejiang University | Chen F.,Jimei University | Yu H.,Zhejiang University | Yu H.,State Key Laboratory of CAD and CG | And 2 more authors.
Pattern Analysis and Applications | Year: 2013

In this paper, a robust sparse kernel density estimation based on the reduced set density estimator is proposed. The key idea is to induce randomness to the plug-in estimation of weighting coefficients. The random fluctuations can inhibit these small nonzero weighting coefficients to cluster in regions of space with greater probability mass. By sequential minimal optimization, these coefficients are merged into a few larger weighting coefficients. Experimental studies show that the proposed model is superior to several related methods both in sparsity and accuracy of the estimation. Moreover, the proposed density estimation is extensively validated on novelty detection and binary classification. © 2013 Springer-Verlag London.

Chen F.,Zhejiang University | Chen F.,Jimei University | Hu R.,Zhejiang University | Yu H.,Zhejiang University | And 2 more authors.
Journal of Visual Communication and Image Representation | Year: 2012

In this paper, a nonparametric statistical shape model based on shape probabilistic representation is proposed for object segmentation. Given a set of training shapes, Cremers et al.'s probabilistic method is adopted to represent the shape, and then principal components analysis (PCA) on shape probabilistic representation is computed to capture the variation of the training shapes. To encode complex shape variation in training set, reduced set density estimator is used to model nonlinear shape distributions in a finite-dimensional subspace. This statistical shape prior is integrated to convex segmentation functional to guide the evolving contour to the object of interest. In addition, in contrast to the commonly used signed distance functions, PCA on shape probabilistic representation needs less number of eigenmodes to capture certain details of the training shapes. Numerical experiments show promising results and the potential of the model for object segmentation. © 2012 Elsevier Inc. All rights reserved.

Chen F.,Fuzhou University | Zhang L.,Hong Kong Polytechnic University | Yu H.,Zhejiang University | Yu H.,State Key Laboratory of CAD and CG
Proceedings of the IEEE International Conference on Computer Vision | Year: 2016

Natural image modeling plays a key role in many vision problems such as image denoising. Image priors are widely used to regularize the denoising process, which is an illposed inverse problem. One category of denoising methods exploit the priors (e.g., TV, sparsity) learned from external clean images to reconstruct the given noisy image, while another category of methods exploit the internal prior (e.g., self-similarity) to reconstruct the latent image. Though the internal prior based methods have achieved impressive denoising results, the improvement of visual quality will become very difficult with the increase of noise level. In this paper, we propose to exploit image external patch prior and internal self-similarity prior jointly, and develop an external patch prior guided internal clustering algorithm for image denoising. It is known that natural image patches form multiple subspaces. By utilizing Gaussian mixture models (GMMs) learning, image similar patches can be clustered and the subspaces can be learned. The learned GMMs from clean images are then used to guide the clustering of noisypatches of the input noisy images, followed by a low-rank approximation process to estimate the latent subspace for image recovery. Numerical experiments show that the proposed method outperforms many state-of-the-art denoising algorithms such as BM3D and WNNM. © 2015 IEEE.

Zhu T.,State Key Laboratory of CAD and CG | Wang C.,Institute of Web Intelligence | Zhang M.,State Key Laboratory of CAD and CG | Pan Z.,Hangzhou Normal University
Proceedings - 2011 International Conference on Virtual Reality and Visualization, ICVRV 2011 | Year: 2011

Digital Mine has become the inevitable development trend of the future mines. Blasting is a essential process during the exploration and development of practical mining engineering. The blasting always plays a direct influence on the efficiency of each follow-up production process and if it were not handled correctly, it will even threaten the safety of miners. This paper presents a novel cross-platform tunnel blast design system used in Digital Mine to simulate the blasting process automatically, which shows how to construct the system modeling, design the blasting parameters, generate 3D holes pattern 3D blasting animation and parameters report. In order to generate blasting process, setting and calculating the parameters of inputs and outputs are grounded on the theory of minimum burden and experiences in blasting engineering, which tunnel smooth blasting is represented as an example in the paper. Results show that our system can reduce the time consuming of traditional manual blasting design and the most important is that our system does not rely much on the experiences of engineer. The experiments show that the results of our system are efficient, reasonable, and this system can be applied and referred in the production and research of tunnel blasting in Digital Mine. © 2011 IEEE.

Peng P.,State Key Laboratory of CAD and CG | Shou L.,State Key Laboratory of CAD and CG | Shou L.,Zhejiang University | Chen K.,State Key Laboratory of CAD and CG | And 2 more authors.
SIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval | Year: 2013

This paper presents a framework called Knowing Camera for real-time recognizing places-of-interest in smartphone photos, with the availability of online geotagged images of such places. We propose a probabilistic field-of-view model which captures the uncertainty in camera sensor data. This model can be used to retrieve a set of candidate images. The visual similarity computation of the candidate images relies on the sparse coding technique. We also propose an ANN filtering technique to speedup the sparse coding. The final ranking combines an uncertain geometric relevance with the visual similarity. Our preliminary experiments conducted in an urban area of a large city show promising results. The most distinguishing feature of our framework is its ability to perform well in contaminated, real-world online image database. Besides, our framework is highly scalable as it does not incur any complex data structure. Copyright © 2013 ACM.

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