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幫查幾篇論文的ei號，謝謝！ 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 發(fā)自小木蟲IOS客戶端 |
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An Improved Non-local Means Image De-noising Algorithm Using Mahalanobis Distance Check record to add to Selected Records Add to selected records 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 Full-text and Local Holdings Links Full Text Links |
2樓2016-09-15 20:23:45
liouzhan654
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2. Feature Constrained Multi-example Based Image Super-resolution Check record to add to Selected Records Add to selected records 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 Full-text and Local Holdings Links Full Text Links |
3樓2016-09-15 20:24:52
liouzhan654
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3. Flower Solid Modeling Based on Sketches Check record to add to Selected Records Add to selected records 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 Full-text and Local Holdings Links Full Text Links |
4樓2016-09-15 20:26:17
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