Pub Date : 2014-08-26DOI: 10.1109/SIBGRAPI.2014.25
R. Romeiro, R. Marroquim, Claudio Esperança, Andreia Breda, Carlos Marcelo Figueredo
Forensic facial reconstruction is the application of anthropology, art and forensic science to recreate the face of an individual from his skull. It is usually done manually by a sculptor with clay and is considered a subjective technique as it relies upon an artistic interpretation of the skull features. In this work, we propose a computerized method based on anatomical rules that systematically generates the surface of the face through a HRBF deformation procedure over a mesh template. Our main contributions are a broader set of anatomical rules being applied over the soft tissue structures and a new deformation method that dissociates the details from the overall shape of the model.
{"title":"Forensic Facial Reconstruction Using Mesh Template Deformation with Detail Transfer over HRBF","authors":"R. Romeiro, R. Marroquim, Claudio Esperança, Andreia Breda, Carlos Marcelo Figueredo","doi":"10.1109/SIBGRAPI.2014.25","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2014.25","url":null,"abstract":"Forensic facial reconstruction is the application of anthropology, art and forensic science to recreate the face of an individual from his skull. It is usually done manually by a sculptor with clay and is considered a subjective technique as it relies upon an artistic interpretation of the skull features. In this work, we propose a computerized method based on anatomical rules that systematically generates the surface of the face through a HRBF deformation procedure over a mesh template. Our main contributions are a broader set of anatomical rules being applied over the soft tissue structures and a new deformation method that dissociates the details from the overall shape of the model.","PeriodicalId":146229,"journal":{"name":"2014 27th SIBGRAPI Conference on Graphics, Patterns and Images","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123771506","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 : 2014-08-26DOI: 10.1109/SIBGRAPI.2014.14
M. Eisemann, Georgia Albuquerque, M. Magnor
The simplicity and visual clarity of scatterplots makes them one of the most widely-used visualization techniques for multivariate data. In complex data sets the important information can be hidden in subsets of the data, often obscured in the typical projections of the whole dataset. This paper presents a new interactive method to explore spatially distinct subsets of a dataset within a given projection. Precisely, we introduce a hierarchy of localized scatterplots as a novel visualization technique that allows to create scatterplots within scatterplots. The resulting visualization bears additional information that would otherwise be hidden within the data. To aid the useful interactive creation of such a hierarchy of localized scatterplots by a user we display transitions between scatterplots as animated rotations in 3D. We show the applicability of our visualization and exploration technique or different tasks, including cluster detection, classification, and comparative analyses. Additionally, we introduce a new exploration tool which we call the cross-dimensional semantic lens. Our hierarchy of localized scatterplots preserves the visual clarity and simplicity of scatterplots while providing additional and easily interpretable information about local subsets of the data.
{"title":"A Nested Hierarchy of Localized Scatterplots","authors":"M. Eisemann, Georgia Albuquerque, M. Magnor","doi":"10.1109/SIBGRAPI.2014.14","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2014.14","url":null,"abstract":"The simplicity and visual clarity of scatterplots makes them one of the most widely-used visualization techniques for multivariate data. In complex data sets the important information can be hidden in subsets of the data, often obscured in the typical projections of the whole dataset. This paper presents a new interactive method to explore spatially distinct subsets of a dataset within a given projection. Precisely, we introduce a hierarchy of localized scatterplots as a novel visualization technique that allows to create scatterplots within scatterplots. The resulting visualization bears additional information that would otherwise be hidden within the data. To aid the useful interactive creation of such a hierarchy of localized scatterplots by a user we display transitions between scatterplots as animated rotations in 3D. We show the applicability of our visualization and exploration technique or different tasks, including cluster detection, classification, and comparative analyses. Additionally, we introduce a new exploration tool which we call the cross-dimensional semantic lens. Our hierarchy of localized scatterplots preserves the visual clarity and simplicity of scatterplots while providing additional and easily interpretable information about local subsets of the data.","PeriodicalId":146229,"journal":{"name":"2014 27th SIBGRAPI Conference on Graphics, Patterns and Images","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122727513","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 : 2014-08-26DOI: 10.1109/SIBGRAPI.2014.45
W. P. Amorim, A. Falcão, M. H. Carvalho
We introduce a semi-supervised pattern classification approach based on the optimum-path forest (OPF) methodology. The method transforms the training set into a graph, finds prototypes in all classes among labeled training nodes, as in the original supervised OPF training, and propagates the class of each prototype to its most closely connected samples among the remaining labeled and unlabeled nodes of the graph. The classifier is an optimum-path forest rooted at those prototypes and the class of a new sample is determined, in an incremental way, as the class of its most closely connected prototype. We compare it with the supervised version using different learning strategies and an efficient method, Transductive Support Vector Machines (TSVM), on several datasets. Experimental results show the semi-supervised approach advantages in accuracy with statistical significance over the supervised method and TSVM. We also show the gain in accuracy of semi-supervised approach when more representative samples are selected for the training set.
{"title":"Semi-supervised Pattern Classification Using Optimum-Path Forest","authors":"W. P. Amorim, A. Falcão, M. H. Carvalho","doi":"10.1109/SIBGRAPI.2014.45","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2014.45","url":null,"abstract":"We introduce a semi-supervised pattern classification approach based on the optimum-path forest (OPF) methodology. The method transforms the training set into a graph, finds prototypes in all classes among labeled training nodes, as in the original supervised OPF training, and propagates the class of each prototype to its most closely connected samples among the remaining labeled and unlabeled nodes of the graph. The classifier is an optimum-path forest rooted at those prototypes and the class of a new sample is determined, in an incremental way, as the class of its most closely connected prototype. We compare it with the supervised version using different learning strategies and an efficient method, Transductive Support Vector Machines (TSVM), on several datasets. Experimental results show the semi-supervised approach advantages in accuracy with statistical significance over the supervised method and TSVM. We also show the gain in accuracy of semi-supervised approach when more representative samples are selected for the training set.","PeriodicalId":146229,"journal":{"name":"2014 27th SIBGRAPI Conference on Graphics, Patterns and Images","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133854931","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 : 2014-08-26DOI: 10.1109/SIBGRAPI.2014.31
Gwanggil Jeon, Kibaek Kim, Jechang Jeong
In this paper, we propose a lossless intra prediction by applying DPCM on the residuals. Since an additional DPCM on the residuals after pixel-by-pixel DPCM is applied, additional spatial redundancy is reduced in proposed method. Multiple residuals are used for additional DPCM of proposed method in contrast with conventional method. The experimental results show that the proposed method achieves the bit saving of 10.11% on average compared to HEVC lossless intra coding. The proposed method results in slightly better compression performance compared to conventional algorithm.
{"title":"Improved Residual DPCM for HEVC Lossless Coding","authors":"Gwanggil Jeon, Kibaek Kim, Jechang Jeong","doi":"10.1109/SIBGRAPI.2014.31","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2014.31","url":null,"abstract":"In this paper, we propose a lossless intra prediction by applying DPCM on the residuals. Since an additional DPCM on the residuals after pixel-by-pixel DPCM is applied, additional spatial redundancy is reduced in proposed method. Multiple residuals are used for additional DPCM of proposed method in contrast with conventional method. The experimental results show that the proposed method achieves the bit saving of 10.11% on average compared to HEVC lossless intra coding. The proposed method results in slightly better compression performance compared to conventional algorithm.","PeriodicalId":146229,"journal":{"name":"2014 27th SIBGRAPI Conference on Graphics, Patterns and Images","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123536872","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 : 2014-08-26DOI: 10.1109/SIBGRAPI.2014.21
L. A. Pereira, Andrei Dabravolski, Ing Ren Tsang, George D. C. Cavalcanti, Jan Sijbers
This paper presents a reconstruction method for a conveyor belt X-ray scanning geometry, consisting of a static X-ray source/detector system and an object in uniform motion. Applying conventional reconstruction methods to data acquired in this geometry leads to severe artefacts. We show that by incorporating prior knowledge of the material as well as domain specific knowledge, such artefacts can be largely reduced. This is done by combining concepts of discrete tomography with the expected object domain.
{"title":"Conveyor Belt X-ray CT Using Domain Constrained Discrete Tomography","authors":"L. A. Pereira, Andrei Dabravolski, Ing Ren Tsang, George D. C. Cavalcanti, Jan Sijbers","doi":"10.1109/SIBGRAPI.2014.21","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2014.21","url":null,"abstract":"This paper presents a reconstruction method for a conveyor belt X-ray scanning geometry, consisting of a static X-ray source/detector system and an object in uniform motion. Applying conventional reconstruction methods to data acquired in this geometry leads to severe artefacts. We show that by incorporating prior knowledge of the material as well as domain specific knowledge, such artefacts can be largely reduced. This is done by combining concepts of discrete tomography with the expected object domain.","PeriodicalId":146229,"journal":{"name":"2014 27th SIBGRAPI Conference on Graphics, Patterns and Images","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124861190","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 : 2014-08-26DOI: 10.1109/SIBGRAPI.2014.49
J. E. Vargas, P. T. Saito, A. Falcão, P. J. Rezende, J. A. D. Santos
Very high resolution (VHR) images are large datasets for pixel annotation -- a process that has depended on the supervised training of an effective pixel classifier. Active learning techniques have mitigated this problem, but pixel descriptors are limited to local image information and the large number of pixels makes the response time to the user's actions impractical, during active learning. To circumvent the problem, we present an active learning strategy that relies on superpixel descriptors and a priori dataset reduction. Firstly, we compare VHR image annotation using superpixel- and pixel-based classifiers, as designed by the same state-of-the-art active learning technique -- Multi-Class Level Uncertainty (MCLU). Even with the dataset reduction provided by the superpixel representation, MCLU remains unfeasible for user interaction. Therefore, we propose a technique to considerably reduce the superpixel dataset for active learning. Moreover, we subdivide the reduced dataset into a list of subsets with random sample rearrangement to gain both speed and sample diversity during the active learning process.
{"title":"Superpixel-Based Interactive Classification of Very High Resolution Images","authors":"J. E. Vargas, P. T. Saito, A. Falcão, P. J. Rezende, J. A. D. Santos","doi":"10.1109/SIBGRAPI.2014.49","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2014.49","url":null,"abstract":"Very high resolution (VHR) images are large datasets for pixel annotation -- a process that has depended on the supervised training of an effective pixel classifier. Active learning techniques have mitigated this problem, but pixel descriptors are limited to local image information and the large number of pixels makes the response time to the user's actions impractical, during active learning. To circumvent the problem, we present an active learning strategy that relies on superpixel descriptors and a priori dataset reduction. Firstly, we compare VHR image annotation using superpixel- and pixel-based classifiers, as designed by the same state-of-the-art active learning technique -- Multi-Class Level Uncertainty (MCLU). Even with the dataset reduction provided by the superpixel representation, MCLU remains unfeasible for user interaction. Therefore, we propose a technique to considerably reduce the superpixel dataset for active learning. Moreover, we subdivide the reduced dataset into a list of subsets with random sample rearrangement to gain both speed and sample diversity during the active learning process.","PeriodicalId":146229,"journal":{"name":"2014 27th SIBGRAPI Conference on Graphics, Patterns and Images","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125593177","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}
Voronoi diagrams are fundamental data structures in computational geometry with applications on different areas. Recent soft object simulation algorithms for real time physics engines require the computation of Voronoi diagrams over 3D images with non-Euclidean distances. In this case, the computation must be performed over a graph, where the edges encode the required distance information. But excessive computation time of Voronoi diagrams prevent more sophisticated deformations that require interactive topological changes, such as cutting or stitching used in virtual surgery simulations. The major bottleneck in the Voronoi computation in this case is a shortest-path algorithm that must be computed multiple times during the deformation. In this paper, we tackle this problem by proposing a GPU algorithm of the shortest-path algorithm from multiple sources using generalized distance functions. Our algorithm was designed to leverage the grid-based nature of the underlying graph used in the simulation. Experimental results report speed-ups up to 65× over a current reference sequential method.
{"title":"Parallel Shortest Path Algorithm for Voronoi Diagrams with Generalized Distance Functions","authors":"J. Toss, J. Comba, B. Raffin","doi":"10.1109/SIBGRAPI.2014.1","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2014.1","url":null,"abstract":"Voronoi diagrams are fundamental data structures in computational geometry with applications on different areas. Recent soft object simulation algorithms for real time physics engines require the computation of Voronoi diagrams over 3D images with non-Euclidean distances. In this case, the computation must be performed over a graph, where the edges encode the required distance information. But excessive computation time of Voronoi diagrams prevent more sophisticated deformations that require interactive topological changes, such as cutting or stitching used in virtual surgery simulations. The major bottleneck in the Voronoi computation in this case is a shortest-path algorithm that must be computed multiple times during the deformation. In this paper, we tackle this problem by proposing a GPU algorithm of the shortest-path algorithm from multiple sources using generalized distance functions. Our algorithm was designed to leverage the grid-based nature of the underlying graph used in the simulation. Experimental results report speed-ups up to 65× over a current reference sequential method.","PeriodicalId":146229,"journal":{"name":"2014 27th SIBGRAPI Conference on Graphics, Patterns and Images","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127258977","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 : 2014-08-26DOI: 10.1109/SIBGRAPI.2014.51
Lilian Chaves Brandao dos Santos, S. Guimarães, A. Araújo, J. A. D. Santos
Remote sensing image classification aims to automatically categorize a monitored area in land cover classes. Hyperspectral images, which provide plenty of spectral information per pixel, allow achieving good accuracy results in classification problems. However, the vast amount of information also can compromise the efficiency due to noisy bands, redundancy, and high-dimensionality. Some dimensionality reduction techniques have been proposed in order to better use the available information. One approach is to perform a band selection, which aims to select the best bands for the classification in order to decrease the dimensionality without degradation of information, i.e., keeping the physical properties acquired by the sensors. This paper introduces a new unsupervised band selection method based on dissimilarity between bands, which are represented by a spectral rhythm, using a bipartite graph matching approach. We carried out experiments in three well known real hyperspectral images datasets. The accuracy results with few bands can achieve levels comparable with the classification made with all data. Our approach can also yield better results in some cases, which is only observed with using supervised approaches in the literature.
{"title":"Unsupervised Hyperspectral Band Selection Based on Spectral Rhythm Analysis","authors":"Lilian Chaves Brandao dos Santos, S. Guimarães, A. Araújo, J. A. D. Santos","doi":"10.1109/SIBGRAPI.2014.51","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2014.51","url":null,"abstract":"Remote sensing image classification aims to automatically categorize a monitored area in land cover classes. Hyperspectral images, which provide plenty of spectral information per pixel, allow achieving good accuracy results in classification problems. However, the vast amount of information also can compromise the efficiency due to noisy bands, redundancy, and high-dimensionality. Some dimensionality reduction techniques have been proposed in order to better use the available information. One approach is to perform a band selection, which aims to select the best bands for the classification in order to decrease the dimensionality without degradation of information, i.e., keeping the physical properties acquired by the sensors. This paper introduces a new unsupervised band selection method based on dissimilarity between bands, which are represented by a spectral rhythm, using a bipartite graph matching approach. We carried out experiments in three well known real hyperspectral images datasets. The accuracy results with few bands can achieve levels comparable with the classification made with all data. Our approach can also yield better results in some cases, which is only observed with using supervised approaches in the literature.","PeriodicalId":146229,"journal":{"name":"2014 27th SIBGRAPI Conference on Graphics, Patterns and Images","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128932753","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 : 2014-08-26DOI: 10.1109/SIBGRAPI.2014.22
D. R. Pereira, J. Delpiano, J. Papa
Optical flow methods are accurate algorithms for estimating the displacement and velocity fields of objects in a wide variety of applications, being their performance dependent on the configuration of a set of parameters. Since there is a lack of research that aims to automatically tune such parameters, in this work we have proposed an evolutionary-based framework for such task, thus introducing three techniques for such purpose: Particle Swarm Optimization, Harmony Search and Social-Spider Optimization. The proposed framework has been compared against with the well-known Large Displacement Optical Flow approach, obtaining the best results in three out eight image sequences provided by a public dataset. Additionally, the proposed framework can be used with any other optimization technique.
{"title":"Evolutionary Optimization Applied for Fine-Tuning Parameter Estimation in Optical Flow-Based Environments","authors":"D. R. Pereira, J. Delpiano, J. Papa","doi":"10.1109/SIBGRAPI.2014.22","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2014.22","url":null,"abstract":"Optical flow methods are accurate algorithms for estimating the displacement and velocity fields of objects in a wide variety of applications, being their performance dependent on the configuration of a set of parameters. Since there is a lack of research that aims to automatically tune such parameters, in this work we have proposed an evolutionary-based framework for such task, thus introducing three techniques for such purpose: Particle Swarm Optimization, Harmony Search and Social-Spider Optimization. The proposed framework has been compared against with the well-known Large Displacement Optical Flow approach, obtaining the best results in three out eight image sequences provided by a public dataset. Additionally, the proposed framework can be used with any other optimization technique.","PeriodicalId":146229,"journal":{"name":"2014 27th SIBGRAPI Conference on Graphics, Patterns and Images","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115798593","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 : 2014-08-26DOI: 10.1109/SIBGRAPI.2014.17
R. Veras, F. Medeiros, Romuere R. V. Silva, K. Aires
This paper presents a new method for fovea detection in color retinal images. Automatic detection of this anatomical structure is a prerequisite for computer aided diagnosis of several retinal diseases, such as macular degeneration. The proposed algorithm detects the macula center by determining a region of interest (ROI) and taking into account optic disk (OD) coordinates and the fact that the central region, i.e. fovea, is a homogenous dark area without blood vessels. Our segmentation algorithm searches for the lowest mean color intensity window in the enhanced image that results from a fusion between the red and green channels. Then, tests were carried on three public benchmark databases, which constitute a total of 254 images.
{"title":"Automatic Detection of Fovea in Retinal Images Using Fusion of Color Bands","authors":"R. Veras, F. Medeiros, Romuere R. V. Silva, K. Aires","doi":"10.1109/SIBGRAPI.2014.17","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2014.17","url":null,"abstract":"This paper presents a new method for fovea detection in color retinal images. Automatic detection of this anatomical structure is a prerequisite for computer aided diagnosis of several retinal diseases, such as macular degeneration. The proposed algorithm detects the macula center by determining a region of interest (ROI) and taking into account optic disk (OD) coordinates and the fact that the central region, i.e. fovea, is a homogenous dark area without blood vessels. Our segmentation algorithm searches for the lowest mean color intensity window in the enhanced image that results from a fusion between the red and green channels. Then, tests were carried on three public benchmark databases, which constitute a total of 254 images.","PeriodicalId":146229,"journal":{"name":"2014 27th SIBGRAPI Conference on Graphics, Patterns and Images","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132283581","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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