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帮查几篇论文的ei号，谢谢！
1.   An Improved Non-local Means Image De-noising Algorithm Using Mahalanobis Distance        
2.  Feature Constrained Multi-example Based Image Super-resolution        
3.  Flower Solid Modeling Based on Sketches   
4.   Algorithm for Interactive Simulation of Sand Painting     
5.A Robust Higher Order Potential for Modeling the Label Consistency between Object Detection and Semantic Segmentation

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paperhunter: 金币+1, 鼓励交流 2016-09-15 22:18:03
An Improved Non-local Means Image De-noising Algorithm Using Mahalanobis Distance  

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An improved non-local means image de-noising algorithm using mahalanobis distance
Yin, Panqiang1 Email author yinpanqiang@live.com; Lu, Dongming1; Yuan, Yuan2
Source: Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, v 28, n 3, p 404-410, March 1, 2016; Language: Chinese;  ISSN: 10039775; Publisher: Institute of Computing Technology
Author affiliations:
1 School of Electronic and Optical Engineering, Nanjing University of Science & Technology, Nanjing, China
2 Science and Technology on Low-Light-Level Night Vision Laboratory, Xi'an, China
Abstract:
An improved non-local means (NLM) image denoising algorithm is proposed, which uses Mahalanobis distance to measure the similarity between the image pixels. Firstly, calculating the Mahalanobis distance between the image pixels in the eigenspace since the Mahalanobis distance is not robust in the sample space. Secondly, the image data is analyzed with the principal component analysis method, thus the Mahalanobis distance equation is simplified. Finally, the improved NLM image denoising algorithm is obtained with the Gaussian weighted kernel function which is composed of the simplified Mahalanobis distance. The experimental results on several typical images show that the improved NLM algorithm can achieve better denoising effect than the original NLM algorithm with a variety of image quality evaluation method. The filter parameter 'h' in the improved NLM denoising algorithm is analyzed in details and the equation between the filter parameter 'h' and the image noise variance is estimated. Based on the equation, the experimental results achieve nearly best denoising performance of the improved filtering algorithm. © 2016, Institute of Computing Technology. All right reserved.(20 refs)
Main heading: Image denoising
Controlled terms: Algorithms  -  Image analysis  -  Pixels  -  Principal component analysis  -  Quality control
Uncontrolled terms: De-noising algorithm  -  Filtering algorithm  -  Image denoising algorithm  -  Image quality evaluation  -  Mahalanobis distances  -  Non local means  -  Non local means (NLM)  -  Principal component analysis method
Classification Code: 716.1 Information Theory and Signal Processing -  913.3 Quality Assurance and Control -  922.2 Mathematical Statistics
Database: Compendex
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2.  Feature Constrained Multi-example Based Image Super-resolution        

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Feature constrained multi-example based image super-resolution
Zhang, Xin1; Zhang, Fan1; Li, Xuemei1 Email author xmli@sdu.edu.cn; Tang, Yuchun2; Zhang, Caiming1, 3
Source: Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, v 28, n 4, p 579-588, April 1, 2016; Language: Chinese;  ISSN: 10039775; Publisher: Institute of Computing Technology
Author affiliations:
1 Department of Computer Science and Technology, Shandong University, Ji'nan; 250101, China
2 Department of Medicine, Shandong University, Ji'nan; 250012, China
3 Shandong Provincial Key Laboratory of Digital Media Technology, Ji'nan; 250014, China
Abstract:
Example-based super-resolution algorithm predicts unknown high-resolution image information by the relationship model learnt from the known high-and low-resolution image pairs. This kind of algorithm can produce high-quality images, but relies on large extern image database. We propose a multi-example based image super-resolution method constrained by image features. First, our method initially high-resolves the low-resolution image by the proposed feature-constrained polynomial interpolation method. Second, we consider low-frequency versions of high-and low-resolution images as the example pair. Each patch in the high-resolution low-frequency image searches its similar patches from the low-resolution image by adaptive KNN search algorithm, and the regression model between similar patches are learnt. Finally, the learnt model is applied to low-resolution low-frequency image to complement high-resolution high-frequency information. Extensive experiments show that the proposed method produces high-quality high-resolution images with high PSNR and SSIM values. © 2016, Institute of Computing Technology. All right reserved.(23 refs)
Main heading: Optical resolving power
Controlled terms: Algorithms  -  Face recognition  -  Regression analysis
Uncontrolled terms: Example-based Super-resolution  -  Feature-constrained  -  High resolution image  -  High-frequency informations  -  Image super resolutions  -  Multi-example  -  Polynomial interpolation  -  Super resolution
Classification Code: 741.1 Light/Optics -  922.2 Mathematical Statistics
Database: Compendex
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3. Flower Solid Modeling Based on Sketches   

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Flower solid modeling based on sketches
Ding, Zhan1 Email author dingzh@jit.edu.cn; Zhang, Sanyuan2
Source: Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, v 28, n 5, p 733-741, May 1, 2016; Language: Chinese;  ISSN: 10039775; Publisher: Institute of Computing Technology
Author affiliations:
1 School of Software, Jinling Institute of Technology, Nanjing; 211169, China
2 School of Computer Science & Technology, Zhejiang University, Hangzhou; 310027, China
Abstract:
The geometry of current flower modeling method is not waterproof. We propose a method to model flowers of solid shape. Our method separates individual flower modeling and inflorescence modeling procedures into structure and geometry modeling. We incorporate interactive editing gestures to allow user to edit structure parameters freely onto structure diagram. Furthermore, our method uses free-hand sketching techniques to allow users to create and edit 3D geometrical elements freely and easily. The final step is to automatically merge all independent 3D geometrical elements into a single waterproof mesh. Experiments show that this solid modeling approach is promising. Using our approach, novice users can create vivid flower models easily and freely. The generated flower model is waterproof. It can have applications in visualization, animation and toys and decorations if printed out on 3D rapid prototyping devices. © 2016, Beijing China Science Journal Publishing Co. Ltd. All right reserved.(21 refs)
Main heading: Three dimensional computer graphics
Controlled terms: Geometry  -  Vegetation  -  Waterproofing
Uncontrolled terms: Constrained Delaunay triangulation  -  Floral diagram  -  Freehand sketching  -  Gesture  -  Inflorescence
Classification Code: 723.2 Data Processing and Image Processing -  921 Mathematics
Database: Compendex
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4楼2016-09-15 20:26:17
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liouzhan654

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Algorithm for interactive simulation of sand painting
Yang, Meng1, 2 Email author yangmeng@bjfu.edu.cn; He, Xiaoyu1; Hu, Cheng1; Wang, Tianxue1; Yang, Gang1
Source: Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, v 28, n 7, p 1084-1093, July 1, 2016; Language: Chinese;  ISSN: 10039775; Publisher: Institute of Computing Technology
Author affiliations:
1 School of Information Science and Technology, Beijing Forestry University, Beijing; 100083, China
2 The State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing; 100190, China
Abstract:
In order to provide a simple and surreal burgeoning approach of interactive sand painting to ordinary users and artists, this paper presented an interactive algorithm to realistically simulate sand painting based on Kinect in real-time. The simulation algorithm consists of three subsystems: a preprocessing subsystem, a user interaction information acquisition subsystem, and a processing and rendering subsystem in real-time. The preprocessing subsystem includes the collection, analysis and statistics for real sand painting image and the definition for various brush styles. Both of these two operations need to be implemented only once. The acquisition subsystem as the starting point of our simulation algorithm could interactively recognize user action information, such as user gestures, by using Kinect. Based on the action information, the acquisition subsystem could know the function selection of users, and could recognize the painting style and the stroke paths in users' painting. The processing and rendering subsystem adopted height-filed-based sand accumulation algorithm and sandpile collapse algorithm to simulate various painting styles, such as hand/fingertip sweeping, sand pouring and sand leakage. Furthermore, the height field of sand canvas would be converted back into a RGB image for the final effects rendering. The experimental results and the user experience feedbacks reveal that the proposed system and algorithm in this paper can generate sand painting artistic creations realistically, effectively, interactively in real-time. © 2016, Beijing China Science Journal Publishing Co. Ltd. All right reserved.(22 refs)
Main heading: Sand
Controlled terms: Algorithms  -  Painting  -  Rendering (computer graphics)  -  User interfaces
Uncontrolled terms: Acquisition subsystems  -  Interactive algorithms  -  Interactive recognition  -  Interactive simulations  -  Kinect  -  Preprocessing subsystems  -  Sand accumulation  -  Sandpile collapse
Classification Code: 483.1 Soils and Soil Mechanics -  722.2 Computer Peripheral Equipment -  723.2 Data Processing and Image Processing -  813.1 Coating Techniques
Database: Compendex
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A robust higher order potential for modeling the label consistency between object detection and semantic segmentation
Yu, Miao1, 2; Hu, Zhanyi1 Email author huzy@nlpr.ia.ac.cn
Source: Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, v 28, n 8, p 1201-1214, August 1, 2016; Language: Chinese;  ISSN: 10039775; Publisher: Institute of Computing Technology
Author affiliations:
1 National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing; 100190, China
2 School of Electric and Information Engineering, Zhongyuan University of Technology, Zhengzhou; 450007, China
Abstract:
Jointly solving the object detection and semantic segmentation under a unified energy minimization framework is a promising way towards a holistic scene understanding, in which how to design powerful expressive higher order potentials and how to construct the corresponding efficient inference algorithms are two key issues. In this work, we at first introduce three design criteria for suitable higher order potential to appropriately model label consistency between object detection and semantic segmentation, then based on these three criteria, a robust higher order potential and its corresponding efficient inference algorithm are proposed. Our proposed higher order potential separately models the label consistency of the pixels within the bounding boxes for true, false and inaccurate detectors, and can be represented as the lower envelope of three linear functions. By introducing only two auxiliary binary variables, it is proved the higher order α-expansion move function can be transformed to submodular pairwise energy, which in turn can be efficiently minimized via graph cuts. The comparative experiments on PASCAL VOC 2010 dataset with the state-of-the-art algorithms showed that our proposed robust higher order potential could effectively model the label consistency of object detection and semantic segmentation for both accepted and rejected detectors, while keeping robust to the false detectors resulting from inaccurate localization. © 2016, Beijing China Science Journal Publishing Co. Ltd. All right reserved.(37 refs)
Main heading: Object detection
Controlled terms: Graphic methods  -  Image segmentation  -  Inference engines  -  Object recognition  -  Semantics
Uncontrolled terms: Comparative experiments  -  Conditional random field  -  Energy minimization  -  Higher Order Potentials  -  Inference algorithm  -  Scene understanding  -  Semantic segmentation  -  State-of-the-art algorithms
Classification Code: 723.2 Data Processing and Image Processing -  723.4.1 Expert Systems
Database: Compendex
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6楼2016-09-15 20:27:41
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heye0601

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6楼: Originally posted by liouzhan654 at 2016-09-15 20:27:41
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A robust higher order potential for modeling the label consistency between object detection and semantic segmentation
Yu, Miao1, 2; ...

谢谢！怎么没有ei号呀，只要ei号就行…

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7楼2016-09-15 20:30:56
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liouzhan654

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7楼: Originally posted by heye0601 at 2016-09-15 20:30:56
谢谢！怎么没有ei号呀，只要ei号就行…
...

检索出来就是这样的，我也纳闷怎么没有检索号
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8楼2016-09-15 20:33:29
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heye0601

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8楼: Originally posted by liouzhan654 at 2016-09-15 20:33:29
检索出来就是这样的，我也纳闷怎么没有检索号...

点那个details，里面有个access number就是了，万分感谢！

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robust_HF

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已发送，谢谢！

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