2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)最新文献_第2页

Recursive Binary Particle Swarm Optimization based Face Localization 基于递归二元粒子群算法的人脸定位

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776227

N. Sanket, K. Manikantan, S. Ramachandran

引用次数: 2

Kernel estimation from blurred edge profiles using Radon Transform for shaken images 基于Radon变换的抖动图像模糊边缘轮廓核估计

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776254

C. Fasil, C. Jiji

引用次数: 0

Lip tracking under varying expressions utilizing domain knowledge 基于领域知识的唇形跟踪

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776201

Swapna Agarwal, D. Mukherjee

引用次数: 1

Search based Video Recommendations 基于搜索的视频推荐

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776190

Abhranil Chatterjee, Bijoy Sarkar, Prateeksha Chandraghatgi, K. Seal, Girish Ananthakrishnan

引用次数: 2

Shape recognition based on shape-signature identification and condensibility: Application to underwater imagery 基于形状特征识别和可压缩性的形状识别:在水下图像中的应用

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776224

J. Banerjee, R. Ray, S. R. K. Vadali, R. Layek, S. N. Shome

{"title":"Shape recognition based on shape-signature identification and condensibility: Application to underwater imagery","authors":"J. Banerjee, R. Ray, S. R. K. Vadali, R. Layek, S. N. Shome","doi":"10.1109/NCVPRIPG.2013.6776224","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776224","url":null,"abstract":"In this paper, a shape recognition method is proposed for a few common geometrical shapes including straight line, circle, ellipse, triangle, quadrilateral, pentagon and hexagon. In the present work, two indices namely Unique Shape Signature (USS) and Condensibility (C) are employed for shape recognition of an object. Using the USS index, all the above mentioned non-circular shapes are neatly recognized, whereas, the C index recognized the circular objects. An added advantage of the proposed method is that it can further differentiate triangles, quadrilaterals and both symmetric and non-symmetric shapes of pentagon and hexagon using distance variance (V ar(dsi)) parameter calculated from USS. Applying the proposed method on above mentioned shapes, an overall recognition rate of 98.80% is achieved on several simulated and real objects of different shapes. Proposed method has also been compared with two existing methods, presents better result. Performance of the proposed method is illustrated by applying it on underwater images and it is observed to perform satisfactory on all the images under test.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129773964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Dual Objective Feature Selection and Scaled Euclidean Classification for face recognition 面向人脸识别的双目标特征选择与尺度欧氏分类

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776153

Siddharth Srivatsa, Prajwal Shanthakumar, K. Manikantan, S. Ramachandran

引用次数: 2

Psychovisual saliency in color images 彩色图像的心理视觉显著性

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776158

Soumyajit Gupta, Rahul Agrawal, R. Layek, J. Mukhopadhyay

引用次数: 1

CHILD: A robust Computationally-Efficient Histogram-based Image Local Descriptor CHILD:基于直方图的鲁棒高效图像局部描述符

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776154

Sai Hareesh Anamandra, V. Chandrasekaran

引用次数: 2

Mesh denoising by improved 3D geometric bilateral filter 改进的三维几何双边滤波器对网格进行去噪

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776193

Somnath Dutta, Sumandeep Banerjee, P. Biswas, Partha Bhowmick

引用次数: 2

Outdoor scene classification using invariant features 基于不变特征的户外场景分类

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776188

R. Raja, S. Roomi, D. Dharmalakshmi

{"title":"Outdoor scene classification using invariant features","authors":"R. Raja, S. Roomi, D. Dharmalakshmi","doi":"10.1109/NCVPRIPG.2013.6776188","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776188","url":null,"abstract":"Scene classification using semantic description has gained much attention towards automatic image retrieval. In many cases, visual appearance of images get affected by environmental conditions such as low lighting and viewing conditions. Such problems in semantic scenes pose difficult challenges during the classification of sceneries. To address this issue, a new outdoor scene classification method for using low level feature has been proposed in this work. To support automatic scene classification at the concept level an efficient illumination and rotation invariant low level features such as color, texture and edge like features have been used in conjunction with multiclass Support Vector Machine (SVM). In this work, we have taken scene categories like mountains, forests, highways, rivers, buildings etc., from the outdoor scenes for classification experimentation. From the experimental results, we demonstrate that the proposed method provides better classification in the large scale image databases like Eight scene category, upright scene and COREL dataset and gives better performance in terms of classification accuracy.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121468437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7