Pub Date : 2013-12-01DOI: 10.1109/NCVPRIPG.2013.6776174
Srimanta Mandal, A. Sao
In all image processing applications, it is important to extract the appropriate information from an image. But often the captured image is not clear enough to give the required information due to the imaging environment. Thus, it is essential to enhance the clarity of the image by some post-processing techniques. Image deblurring is one of such techniques to remove the blurry effect of the captured image. This paper looks into this problem in a different way, where the deblurring of an image is addressed by solving image super-resolution problem. The blurred image is first down-sampled and then it is fed to the super-resolution framework to produce the deblurred high resolution image. In addition, the proposed approach states the requirement of edge preservation in the problem. The experimental results are comparable with the existing image deblurring algorithms.
{"title":"Image deblurring in super-resolution framework","authors":"Srimanta Mandal, A. Sao","doi":"10.1109/NCVPRIPG.2013.6776174","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776174","url":null,"abstract":"In all image processing applications, it is important to extract the appropriate information from an image. But often the captured image is not clear enough to give the required information due to the imaging environment. Thus, it is essential to enhance the clarity of the image by some post-processing techniques. Image deblurring is one of such techniques to remove the blurry effect of the captured image. This paper looks into this problem in a different way, where the deblurring of an image is addressed by solving image super-resolution problem. The blurred image is first down-sampled and then it is fed to the super-resolution framework to produce the deblurred high resolution image. In addition, the proposed approach states the requirement of edge preservation in the problem. The experimental results are comparable with the existing image deblurring algorithms.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"25 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":"114042649","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}
Pub Date : 2013-12-01DOI: 10.1109/NCVPRIPG.2013.6776219
M. Okade, P. Biswas
This paper investigates a novel motion vector outlier rejection method based on using mean shift clustering on block motion vectors. The accuracy of compressed domain global motion estimation techniques is largely influenced by its ability to counter the outlier motion vectors. These outliers occur in the block motion vector field due to moving objects, noise or due to large matching errors as a result of the encoders priority on rate distortion optimization. In the present work it is shown that by using mean shift clustering on block motion vectors, those clusters which correspond to outlier motion vectors can be identified. Once detected these clusters are kept out of the global motion estimation process thereby increasing the robustness of estimated camera parameters. The proposed method is compared with existing state-of-the-art outlier removal methods using synthetic and real video sequences to establish and validate its superiority.
{"title":"Mean shift clustering based outlier removal for global motion estimation","authors":"M. Okade, P. Biswas","doi":"10.1109/NCVPRIPG.2013.6776219","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776219","url":null,"abstract":"This paper investigates a novel motion vector outlier rejection method based on using mean shift clustering on block motion vectors. The accuracy of compressed domain global motion estimation techniques is largely influenced by its ability to counter the outlier motion vectors. These outliers occur in the block motion vector field due to moving objects, noise or due to large matching errors as a result of the encoders priority on rate distortion optimization. In the present work it is shown that by using mean shift clustering on block motion vectors, those clusters which correspond to outlier motion vectors can be identified. Once detected these clusters are kept out of the global motion estimation process thereby increasing the robustness of estimated camera parameters. The proposed method is compared with existing state-of-the-art outlier removal methods using synthetic and real video sequences to establish and validate its superiority.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"7 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":"128732528","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}
Pub Date : 2013-12-01DOI: 10.1109/NCVPRIPG.2013.6776242
Sandeep Puthanveetil Satheesan, S. Tulyakov, V. Govindaraju
Matching score fusion is a commonly used technique for improving the performance of biometric systems. In this paper we investigate the methods for fusing the scores obtained from matching individual video frames to a stored face template. Traditional fusion rules like sum and product does not account for the diversity of information contained in consecutive frames. Instead, we propose to use a quantitative measure of the shared information content between adjacent frame pairs to capture this information and enhance the score fusion performance. We conduct our experiments in a database of 132 person videos. The results show that application of information content to score level fusion can increase the performance of a fusion algorithm and hence make it more robust to errors. The developed matching score fusion method can be applied to other systems involving the multiple biometric samples or scans.
{"title":"A feature information based approach for enhancing score-level fusion in multi-sample biometric systems","authors":"Sandeep Puthanveetil Satheesan, S. Tulyakov, V. Govindaraju","doi":"10.1109/NCVPRIPG.2013.6776242","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776242","url":null,"abstract":"Matching score fusion is a commonly used technique for improving the performance of biometric systems. In this paper we investigate the methods for fusing the scores obtained from matching individual video frames to a stored face template. Traditional fusion rules like sum and product does not account for the diversity of information contained in consecutive frames. Instead, we propose to use a quantitative measure of the shared information content between adjacent frame pairs to capture this information and enhance the score fusion performance. We conduct our experiments in a database of 132 person videos. The results show that application of information content to score level fusion can increase the performance of a fusion algorithm and hence make it more robust to errors. The developed matching score fusion method can be applied to other systems involving the multiple biometric samples or scans.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"1 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":"129230905","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}
Pub Date : 2013-12-01DOI: 10.1109/NCVPRIPG.2013.6776235
Aditi Kapoor, K. K. Biswas, M. Hanmandlu
In this paper we propose to summarize videos based on key frames. We improve upon the histogram and pixel difference based approach with fuzzy rule based approach and also give a new approach which reduces the computation of framewise differences. We test our methods using fidelity ratio and compression ratio on videos of sports from YouTube and UCF sports dataset, videos of commercials and sitcoms. The results of our methods are seen to be comparable to other state of the art approaches.
{"title":"Fuzzy video summarization using key frame extraction","authors":"Aditi Kapoor, K. K. Biswas, M. Hanmandlu","doi":"10.1109/NCVPRIPG.2013.6776235","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776235","url":null,"abstract":"In this paper we propose to summarize videos based on key frames. We improve upon the histogram and pixel difference based approach with fuzzy rule based approach and also give a new approach which reduces the computation of framewise differences. We test our methods using fidelity ratio and compression ratio on videos of sports from YouTube and UCF sports dataset, videos of commercials and sitcoms. The results of our methods are seen to be comparable to other state of the art approaches.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"54 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":"129331693","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}
This paper presents a novel method for discovery and recognition of hairstyles in a collection of colored face images. We propose the use of Agglomerative clustering for automatic discovery of distinct hairstyles. Our method proposes automated approach for generation of hair, background and face-skin probability-masks for different hairstyle category without requiring manual annotation. The probability-masks based density estimates are subsequently applied for recognizing the hairstyle in a new face image. The proposed methodology has been verified with a synthetic dataset of approximately thousand images, randomly collected from the Internet.
{"title":"A density based method for automatic hairstyle discovery and recognition","authors":"Jyotikrishna Dass, Monika Sharma, Ehtesham Hassan, Hiranmay Ghosh","doi":"10.1109/NCVPRIPG.2013.6776234","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776234","url":null,"abstract":"This paper presents a novel method for discovery and recognition of hairstyles in a collection of colored face images. We propose the use of Agglomerative clustering for automatic discovery of distinct hairstyles. Our method proposes automated approach for generation of hair, background and face-skin probability-masks for different hairstyle category without requiring manual annotation. The probability-masks based density estimates are subsequently applied for recognizing the hairstyle in a new face image. The proposed methodology has been verified with a synthetic dataset of approximately thousand images, randomly collected from the Internet.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"1 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":"130528648","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}
Pub Date : 2013-12-01DOI: 10.1109/NCVPRIPG.2013.6776210
R. Prashanth, Sumantra Dutta Roy, P. Mandal, Shantanu Ghosh
Dopaminergic imaging using Single Photon Emission Computed Tomography (SPECT) with 123I-Ioflupane have shown to increase the diagnostic accuracy in Parkinson's Disease (PD). Studies show that around 10% of subjects who are clinically diagnosed as PD, have SPECT scans in the normal range and are called Scans Without Evidence of Dopaminergic Deficit (SWEDD) subjects. Subsequent follow-up on these subjects has indicated that they are unlikely to have PD. Detection and differentiation of PD and SWEDD is problematic in the early stages of the disease. Early and accurate diagnosis of PD and also SWEDD is crucial for early management, avoidance of unnecessary medical examinations and therapies; and their side-effects. We in our paper, use the SPECT images from 35 Normal, 36 PD and 38 SWEDD subjects as obtained from the Parkinson's Progression Markers Initiative (PPMI) database, to carry out intensity-based surface fitting using polynomial model. This is the first time that such kind of modeling is carried out on the SPECT images for the characterization of PD. Our results show that the surface profile in terms of model coefficients and goodness-of-fit parameters is different for Normal, Early PD and SWEDD subjects. Such kind of modeling may aid in the diagnosis of early PD and SWEDD from SPECT images.
{"title":"Surface fitting in SPECT imaging useful for detecting Parkinson's Disease and Scans Without Evidence of Dopaminergic Deficit","authors":"R. Prashanth, Sumantra Dutta Roy, P. Mandal, Shantanu Ghosh","doi":"10.1109/NCVPRIPG.2013.6776210","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776210","url":null,"abstract":"Dopaminergic imaging using Single Photon Emission Computed Tomography (SPECT) with 123I-Ioflupane have shown to increase the diagnostic accuracy in Parkinson's Disease (PD). Studies show that around 10% of subjects who are clinically diagnosed as PD, have SPECT scans in the normal range and are called Scans Without Evidence of Dopaminergic Deficit (SWEDD) subjects. Subsequent follow-up on these subjects has indicated that they are unlikely to have PD. Detection and differentiation of PD and SWEDD is problematic in the early stages of the disease. Early and accurate diagnosis of PD and also SWEDD is crucial for early management, avoidance of unnecessary medical examinations and therapies; and their side-effects. We in our paper, use the SPECT images from 35 Normal, 36 PD and 38 SWEDD subjects as obtained from the Parkinson's Progression Markers Initiative (PPMI) database, to carry out intensity-based surface fitting using polynomial model. This is the first time that such kind of modeling is carried out on the SPECT images for the characterization of PD. Our results show that the surface profile in terms of model coefficients and goodness-of-fit parameters is different for Normal, Early PD and SWEDD subjects. Such kind of modeling may aid in the diagnosis of early PD and SWEDD from SPECT images.","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":"114067806","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}
Pub Date : 2013-12-01DOI: 10.1109/NCVPRIPG.2013.6776178
Soumyadeep Ghosh, Soumen Bag
Thinning is an important preprocessing operation used in different document image processing and analysis applications. The main objective of thinning is to obtain single-pixel thin skeleton without any shape distortion. It is noticed that documents written in ink-sketch pens and scanned with high precision scanners suffer from high degree of unevenness on their outer surfaces. This unevenness results in severe distortions in the shapes of thinned images, which makes them unsuitable for efficient recognition. These distortions are mainly two types namely, spurious loops and spurious strokes. Our proposed algorithm gets rid of these distortions in the thinned image. We have tested our approach on our own data set of about 1500 characters and have got promising results.
{"title":"An improvement on thinning to handle characters with noisy contour","authors":"Soumyadeep Ghosh, Soumen Bag","doi":"10.1109/NCVPRIPG.2013.6776178","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776178","url":null,"abstract":"Thinning is an important preprocessing operation used in different document image processing and analysis applications. The main objective of thinning is to obtain single-pixel thin skeleton without any shape distortion. It is noticed that documents written in ink-sketch pens and scanned with high precision scanners suffer from high degree of unevenness on their outer surfaces. This unevenness results in severe distortions in the shapes of thinned images, which makes them unsuitable for efficient recognition. These distortions are mainly two types namely, spurious loops and spurious strokes. Our proposed algorithm gets rid of these distortions in the thinned image. We have tested our approach on our own data set of about 1500 characters and have got promising results.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"32 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":"124040296","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}
Pub Date : 2013-12-01DOI: 10.1109/NCVPRIPG.2013.6776196
Vandana Agarwal, S. Bhanot
In this paper, it is proposed to use Multiquadric basis functions at hidden layer of radial basis functions neural networks (RBFNN) for face recognition. The performance of RBFNN depends on the design of the structure of RBFNN, which includes optimal center selection and spread of RBF units, number of neurons at hidden layer, weights etc. Design of hidden layer of RBFNN also includes the choice of basis functions which is proposed to be of Multiquadric basis functions. The shape of Multiquadric basis function plays an important role in the performance of RBFNN in face recognition. A novel evolutionary shape parameter optimization technique inspired by the attractiveness of the natural fireflies is proposed and is used in the design of Multiquadric basis functions for the given face database. The algorithm is tested on two benchmarked face databases ORL and Indian face databases. The proposed technique significantly outperforms the performance of the Gaussian basis functions based RBFNN in terms of face recognition accuracy.
{"title":"Evolutionary design of Multiquadric radial basis functions neural network for face recognition","authors":"Vandana Agarwal, S. Bhanot","doi":"10.1109/NCVPRIPG.2013.6776196","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776196","url":null,"abstract":"In this paper, it is proposed to use Multiquadric basis functions at hidden layer of radial basis functions neural networks (RBFNN) for face recognition. The performance of RBFNN depends on the design of the structure of RBFNN, which includes optimal center selection and spread of RBF units, number of neurons at hidden layer, weights etc. Design of hidden layer of RBFNN also includes the choice of basis functions which is proposed to be of Multiquadric basis functions. The shape of Multiquadric basis function plays an important role in the performance of RBFNN in face recognition. A novel evolutionary shape parameter optimization technique inspired by the attractiveness of the natural fireflies is proposed and is used in the design of Multiquadric basis functions for the given face database. The algorithm is tested on two benchmarked face databases ORL and Indian face databases. The proposed technique significantly outperforms the performance of the Gaussian basis functions based RBFNN in terms of face recognition accuracy.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"55 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":"126347746","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}
Pub Date : 2013-12-01DOI: 10.1109/NCVPRIPG.2013.6776251
Shrishail S. Gajbhar, M. Joshi
In this paper, we propose two designs of redundant finer directional wavelet transform (FiDWT) and explain its application to image denoising. 2-channel perfect reconstruction (PR) checkerboard-shaped filter bank (CSFB) is at the core of the designs. The 2-channel CSFB, uses 2-D nonseparable analysis and synthesis filter responses without downsampling/upsampling matrices resulting in redundancy factor of 2. Both these designs have two lowpass and six highpass directional subbands. The hard-thresholding results for image denoising using proposed designs clearly shows improvement in PSNR as well as visual quality of the denoised images. Using the Bayes least squares-Gaussian scale mixture (BLS-GSM), a current state-of-the-art wavelet-based image denoising technique with the proposed two times redundant FiDWT design indicates encouraging results on textural images with much less computational cost.
{"title":"Image denoising using redundant finer directional wavelet transform","authors":"Shrishail S. Gajbhar, M. Joshi","doi":"10.1109/NCVPRIPG.2013.6776251","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776251","url":null,"abstract":"In this paper, we propose two designs of redundant finer directional wavelet transform (FiDWT) and explain its application to image denoising. 2-channel perfect reconstruction (PR) checkerboard-shaped filter bank (CSFB) is at the core of the designs. The 2-channel CSFB, uses 2-D nonseparable analysis and synthesis filter responses without downsampling/upsampling matrices resulting in redundancy factor of 2. Both these designs have two lowpass and six highpass directional subbands. The hard-thresholding results for image denoising using proposed designs clearly shows improvement in PSNR as well as visual quality of the denoised images. Using the Bayes least squares-Gaussian scale mixture (BLS-GSM), a current state-of-the-art wavelet-based image denoising technique with the proposed two times redundant FiDWT design indicates encouraging results on textural images with much less computational cost.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"438 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":"134250719","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}
Pub Date : 2013-12-01DOI: 10.1109/NCVPRIPG.2013.6776184
K. V. Arya, P. Prasad
The popular techniques to eliminate temporal redundancy in video sequences are Motion Estimation and Motion Compensation. These techniques have also been used in popular H.264, MPEG-2 and MPEG-4 video coding standards. Conventional fast Block Matching Algorithms (BMA) perform exhaustive search between the current and the reference frame. Although BMA technique gives the exact result but it is computationally very expensive. Another drawback of this method is that it easily gets trapped into the local minima which eventually lead to degradation of the video quality. The proposed Motion Estimation Technique exploits the fact that the human eyes are incapable of detecting different frames when they are run at particular frame rate. The experimental results on various video sequences demonstrate that the proposed technique has outperformed all the existing conventional motion estimation techniques.
{"title":"A new Motion Estimation Technique for video compression","authors":"K. V. Arya, P. Prasad","doi":"10.1109/NCVPRIPG.2013.6776184","DOIUrl":"https://doi.org/10.1109/NCVPRIPG.2013.6776184","url":null,"abstract":"The popular techniques to eliminate temporal redundancy in video sequences are Motion Estimation and Motion Compensation. These techniques have also been used in popular H.264, MPEG-2 and MPEG-4 video coding standards. Conventional fast Block Matching Algorithms (BMA) perform exhaustive search between the current and the reference frame. Although BMA technique gives the exact result but it is computationally very expensive. Another drawback of this method is that it easily gets trapped into the local minima which eventually lead to degradation of the video quality. The proposed Motion Estimation Technique exploits the fact that the human eyes are incapable of detecting different frames when they are run at particular frame rate. The experimental results on various video sequences demonstrate that the proposed technique has outperformed all the existing conventional motion estimation techniques.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"19 3-4 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":"132441897","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}
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