2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)最新文献_第3页

An optimized derivative projection warping approach for moving platform video stabilization 一种用于移动平台视频稳定的导数投影翘曲优化方法

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776218

Deepika Shukla, R. K. Jha

{"title":"An optimized derivative projection warping approach for moving platform video stabilization","authors":"Deepika Shukla, R. K. Jha","doi":"10.1109/NCVPRIPG.2013.6776218","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776218","url":null,"abstract":"This paper presents an optimized and efficient video stabilization technique based on projection curve warping. In most of the recorded videos, the relative displacement between two consecutive frames goes from 3-4 pixel for hand-held and 25-30 for moving platform applications. Based on this experimental data, the use of Sakoe-Chiba band with fixed window size has been proposed for constraining distance matrix estimation, in the dynamic time warping algorithm. In the existing projection based stabilization techniques, intensity values are matched for motion estimation. Any change in the local intensity values either induced due to intensity variation, moving objects or scene variation, causes error in the estimated motion. To overcome this problem, a higher level feature i.e. shape of the projection curve has been incorporated by matching the local derivative of curve instead of the intensity values itself. Robustness and time efficiency of the proposed technique is measured in terms of interframe transformation fidelity and processing time respectively.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"27 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":"125182837","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: 4

Tracking based depth-guided video inpainting 基于跟踪的深度引导视频绘画

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776217

Saroj Hatheele, M. Zaveri

引用次数: 2

A new approach for terrain analysis of lunar surface by Chandrayaan-1 data using open source libraries 基于开源库的月船1号数据月球表面地形分析新方法

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776166

Hardik Acharya, Amitabh, T. Srinivasan, B. Gopalakrishna

引用次数: 2

Near real-time face parsing 近实时人脸分析

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776192

A. Minocha, Digvijay Singh, Nataraj Jammalamadaka, C. V. Jawahar

引用次数: 1

Time-frequency analysis based motion detection in perfusion weighted MRI 灌注加权MRI中基于时频分析的运动检测

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776215

M. Sushma, Anubha Gupta, J. Sivaswamy

{"title":"Time-frequency analysis based motion detection in perfusion weighted MRI","authors":"M. Sushma, Anubha Gupta, J. Sivaswamy","doi":"10.1109/NCVPRIPG.2013.6776215","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776215","url":null,"abstract":"In this paper, we present a novel automated method to detect motion in perfusion weighted images (PWI), which is a type of magnetic resonance imaging (MRI). In PWI, blood perfusion is measured by injecting an exogenous tracer called bolus into the blood flow of a patient and then tracking it in the brain. PWI requires a long data acquisition time to form a time series of volumes. Hence, motion occurs due to patient's unavoidable movements during a scan, which in turn results into motion corrupted data. There is a necessity of detection of these motion artifacts on captured data for correct disease diagnosis. In PWI, intensity profile gets disturbed due to occurrence of motion and/or bolus passage through the blood vessels. There is no way to distinguish between motion occurrence and bolus passage. In this paper, we propose an efficient time-frequency analysis based motion detection method. We show that proposed method is computationally inexpensive and fast. This method is evaluated on a DSC-MRI sequence with simulated motion of different degrees. We show that our approach detects motion in a few seconds.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"21 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":"133928361","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: 0

Correlation based object-specific attentional mechanism for target localization in high resolution satellite images 基于相关性的高分辨率卫星图像目标定位注意机制

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776221

Phool Preet, P. Chowdhury, G. S. Malik

引用次数: 0

Medical image analysis an attempt for mammogram classification using texture based association rule mining 医学图像分析:基于纹理的关联规则挖掘对乳房x光片分类的尝试

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776208

D. Deshpande, A. Rajurkar, R. Manthalkar

{"title":"Medical image analysis an attempt for mammogram classification using texture based association rule mining","authors":"D. Deshpande, A. Rajurkar, R. Manthalkar","doi":"10.1109/NCVPRIPG.2013.6776208","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776208","url":null,"abstract":"Breast cancer, the most common type of cancer in women is one of the leading causes of cancer deaths. Due to this, early detection of cancer is the major concern for cancer treatment. The most common screening test called mammography is useful for early detection of cancer. It has been proven that there is potential raise in the cancers detected due to consecutive reading of mammograms. But this approach is not monetarily viable. Therefore there is a significant need of computer aided detection systems which can produce intended results and assist medical staff for accurate diagnosis. In this research we made an attempt to build classification system for mammograms using association rule mining based on texture features. The proposed system uses most relevant GLCM based texture features of mammograms. New method is proposed to form associations among different texture features by judging the importance of different features. Resultant associations can be used for classification of mammograms. Experiments are carried out using MIAS Image Database. The performance of the proposed method is compared with standard Apriori algorithm. It is found that performance of proposed method is better due to reduction in multiple times scanning of database which results in less computation time. We also investigated the use of association rules in the field of medical image analysis for the problem of mammogram classification.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"82 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":"131806844","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: 12

Face and facial expression recognition using Extended Locality Preserving Projection 基于扩展局域保持投影的人脸和面部表情识别

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776156

Deshna Jain, G. Shikkenawis, S. Mitra, S. K. Parulkar

{"title":"Face and facial expression recognition using Extended Locality Preserving Projection","authors":"Deshna Jain, G. Shikkenawis, S. Mitra, S. K. Parulkar","doi":"10.1109/NCVPRIPG.2013.6776156","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776156","url":null,"abstract":"Face images of a person taken in varying expressions, orientations, lighting conditions are expected to be close to each other even under any mathematical transformation. These high dimensional face images are difficult to be recognized as faces of same person by machines in contrast to the humans. Many of the existing face recognition systems thus explicitly reduce the dimensions before performing recognition task. However, it is not guaranteed that varying faces of a single person could still be close in the lower dimensional space. Dimensionality reduction technique such as Extended Locality Preserving Projection (ELPP) not only reduces the dimension of the input data remarkably but also preserves the locality using neighbourhood information in the projected space. This paper deals with a face recognition system where ELPP is used to reduce the dimension of face images and hence uses ELPP coefficients as features to the classifier for recognition. In specific, two classifiers namely Naive Bayes classifier and Support Vector Machine are used. Results of face recognition of different data sets are highly impressive and at the same time results of facial expressions are encouraging. Experiments have also been carried out by taking a supervised version of ELPP (ESLPP).","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"39 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":"133183055","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: 3

Geometric invariant Target classification using 2D Mellin cepstrum with modified grid formation 基于改进网格结构的二维Mellin倒谱几何不变目标分类

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776260

B. Sathyabama, S. Roomi, R. EvangelineJenitaKamalam

{"title":"Geometric invariant Target classification using 2D Mellin cepstrum with modified grid formation","authors":"B. Sathyabama, S. Roomi, R. EvangelineJenitaKamalam","doi":"10.1109/NCVPRIPG.2013.6776260","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776260","url":null,"abstract":"The Classification of Targets in Synthetic Aperture Radar Images is greatly affected by scale, rotation and translation. This paper proposes a geometric invariant algorithm to classify military targets based on extracting cepstral features derived from the modified grid selection over spectral components of Fourier Mellin Transform. The proposed non uniform grid is formed by a window with a cell of 2×2 pixels at the center, surrounded by the cells of 4×4 pixels, and so on, with overlapping concept to extract better representative features. Further each cell is divided into upper and lower triangular bins. The energy of each bin forms the down sampled M×M data accounting the larger value between the two triangles so that the information is enhanced. The experiments are carried out with a total of 700 SAR images collected from MSTAR database with different combinations of rotation, scale and translations. The proposed method has been tested against existing methods such as Region Covariance, Co-differencing and 2D Mellin cepstrum with non- overlapping grids. The results from 2D-Mellin Cepstrum using the proposed grid formation have been observed to be better in terms of 92% detection accuracy compared with 86% for region covariance method and 89% for non-uniform grid formation method.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"46 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":"124553743","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: 0

Single image super-resolution using compressive sensing with learned overcomplete dictionary 基于学习过完全字典的压缩感知单幅图像超分辨率

2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) Pub Date : 2013-12-01 DOI: 10.1109/NCVPRIPG.2013.6776176

B. Deka, Kanchan Kumar Gorain, Navadeep Kalita, B. Das

引用次数: 6