Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City

China

Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City

China
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Liu X.,Jiangsu University | Liu X.,Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City | Ma L.,Shanghai JiaoTong University | Liu Y.,Zhejiang Ocean University
Multimedia Tools and Applications | Year: 2016

We present a new illustrating algorithm of global tone, enlightened by the skill called artistic tone. It is structure-aware and computationally driven by “visibility”, which refers to quantitatively measuring how visible a point (or a region) on a mesh is within a virtual camera space. The feature lines are sketched by silhouettes and/or suggestive contours, which then are enhanced by colorful inks, mimicking tonal values. To overcome that computing tone throughout surfaces is prohibitive, the global shape descriptor of Gaussian visibility is proposed, which is fast and robust such that the rendering pipeline is finished in real-time. The line drawings are largely improved as the descriptor enables our approach to convey more shape cues beyond shades. We demonstrated the plausibility of global tone with various models, showing that our experimental results are comparable to or better than state-of-the-art. © 2016 Springer Science+Business Media New York


Yang L.,Xinjiang University | Yang L.,Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City | Yang L.,Jiangsu University | Tian S.,Xinjiang University | And 10 more authors.
International Journal of Innovative Computing, Information and Control | Year: 2015

There are regional limitations in traditional methods of water body extraction. For different terrain, all the methods rely heavily on c arefully hand-engineered feature selection and large amounts of prior knowledge. Due to the difficulty and high cost in acquiring, the labeled data of remote sensing is relatively small. Thus, there exist some challenges in the classification of huge amount of high dimension remote sensing data. Deep Learning has a good capacity of hierarchical feature learning from unlabeled data. Stacked sparse autoencoder (SSAE), one deep learning method, is widely investigated for image recognition. In this paper, a new water body extraction model based on SSAE is established. At first, current useful features (NDWI, NDVI, NDBI and so forth) are collected to construct unique feature matrix for each pixel. Next, a Feature Expansion Algorithm (FEA) is designed by taking account of the influence of neighboring pixels to expand feature matrixes. Setting the expansion features as inputs, SSAE is trained to extract water body. The experimental results showed that the proposed model outperformed Support Vector Machine (SVM) and traditional neural network (NN). Meanwhile, the proposed FEA explored more distinct features of water body so that the accuracy of water body extraction was improved to a great extent. © 2015.


Zhu H.,Jiangsu University | Fan H.,Jiangsu University | Ye F.,Jiangsu University | Ye F.,Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City | And 2 more authors.
Dyna (Spain) | Year: 2016

A novel method for moving vehicle tracking was proposed to improve the vehicle identification rate on the basis of local autocorrelation (LAC) and horizontal edge (HE) identification. Local autocorrelation images were generated as the pre-treatment for horizontal edge identification, so that the horizontal edge characteristics could be strengthened while the influence of weather conditions could be reduced. Robust background model could be obtained based on exponential forgetting method (EFM), the moving vehicle regions were detected by background subtraction. Stable horizontal edge of vehicle was detected for vehicle tracking, the length of horizontal edge was normalized in image sequence to improve vehicle detection rate. The distance of the barycentric coordinate of the horizontal edges was used to track vehicles in traffic videos. Barycentric coordinate was modified using correction coefficient to ensure the effect of tracking. The vehicle regions were marked using bounding box during vehicle tracking. Traffic videos of various complex conditions (foggy weather, strong sunlight, morning, and evening) were used as test images to verify the effectiveness of the proposed method. Experimental results show that a higher identification rate of moving vehicles is obtained via the proposed method. The proposed novel method can be used to improve the performance of the intelligent transportation systems.


Gu C.-S.,Jiangsu University | Gu C.-S.,Hefei University of Technology | Gu C.-S.,Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City
Tongxin Xuebao/Journal on Communications | Year: 2013

The signature schemes based on RSA and ECC do not seem suitable for special application area such as wireless sensor network, smart card and wireless RFID since they suffer from low computing efficiency. In order to design a scheme for small computing devices with limited computing capacity, Wang et al. proposed a novel lightweight digital signature scheme based on the hash authentication technology. A polynomial time algorithm, which found an equivalent signing secret key from the public key, was presented for this novel lightweight digital signature scheme. By using the equivalent secret key, adversary can forge signature for arbitrary messages. Hence, their digital signature scheme based on hash authentication is broken.


Gu C.-S.,Jiangsu University | Gu C.-S.,Hefei University of Technology | Gu C.-S.,Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City
Tongxin Xuebao/Journal on Communications | Year: 2013

To design post-quantum public key cryptography, Zhao, et al presented a novel public key scheme based on the BMQ problem. An equivalent secret key could directly be solved from the public key of their scheme by applying the property of the ergodic matrix over finite field. Thus, the HFEM public key scheme was broken.


Yijun L.,Jiangsu University | Yijun L.,Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City | Feiyue Y.,Jiangsu University | Feiyue Y.,Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Keys are very important for data management. Due to the hierarchical and flexible structure of XML, mining keys from XML data is a more complex and difficult task than from relational databases. In this paper, we study mining approximate keys from XML data, and define the support and confidence of a key expression based on the number of null values on key paths. In the mining process, inference rules are used to derive new keys. Through the two-phase reasoning, a target set of approximate keys and its reduced set are obtained. Our research conducted experiments over ten benchmark XML datasets from XMark and four files in the UW XML Repository. The results show that the approach is feasible and efficient, with which effective keys in various XML data can be discovered. © Springer-Verlag 2013.

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