2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)最新文献_第3页

A Machine Learning Approach for Graph-Based Page Segmentation 基于图的页面分割的机器学习方法

2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) Pub Date : 2018-10-01 DOI: 10.1109/SIBGRAPI.2018.00061

A. L. L. Maia, Frank D. Julca-Aguilar, N. Hirata

{"title":"A Machine Learning Approach for Graph-Based Page Segmentation","authors":"A. L. L. Maia, Frank D. Julca-Aguilar, N. Hirata","doi":"10.1109/SIBGRAPI.2018.00061","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2018.00061","url":null,"abstract":"We propose a new approach for segmenting a document image into its page components (e.g. text, graphics and tables). Our approach consists of two main steps. In the first step, a set of scores corresponding to the output of a convolutional neural network, one for each of the possible page component categories, is assigned to each connected component in the document. The labeled connected components define a fuzzy over-segmentation of the page. In the second step, spatially close connected components that are likely to belong to a same page component are grouped together. This is done by building an attributed region adjacency graph of the connected components and modeling the problem as an edge removal problem. Edges are then kept or removed based on a pre-trained classifier. The resulting groups, defined by the connected subgraphs, correspond to the detected page components. We evaluate our method on the ICDAR2009 dataset. Results show that our method effectively segments pages, being able to detect the nine types of page components. Furthermore, as our approach is based on simple machine learning models and graph-based techniques, it should be easily adapted to the segmentation of a variety of document types.","PeriodicalId":208985,"journal":{"name":"2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133582686","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

[Copyright notice] (版权)

2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) Pub Date : 2018-10-01 DOI: 10.1109/sibgrapi.2018.00003

引用次数: 0

Visualization of Roll Call Data for Supporting Analyses of Political Profiles 支持政治档案分析的点名数据可视化

2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) Pub Date : 2018-10-01 DOI: 10.1109/SIBGRAPI.2018.00026

Rodrigo Nunes Moni da Silva, A. Spritzer, C. Freitas

{"title":"Visualization of Roll Call Data for Supporting Analyses of Political Profiles","authors":"Rodrigo Nunes Moni da Silva, A. Spritzer, C. Freitas","doi":"10.1109/SIBGRAPI.2018.00026","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2018.00026","url":null,"abstract":"In this paper, we propose a web-based application where the user can instantiate multiple, coordinated panels for exploring data concerning the votes of representatives in Brazil's lower legislative house (the Chamber of Deputies). Open data about roll calls made available by the Chamber allowed us to build a set of interactive visualizations to let users explore deputies' votes and build an understanding of their political profiles. Based on the set of roll call voting results from 1991 to 2016, our application displays the political behaviour of parties in a timeline from which users can select periods and instantiate panels showing the political spectrum of deputies using different methods of dimensionality reduction. Deputies can be separated in clusters based on their position in the political spectrum, and other panels can be instantiated showing details about each cluster. Users can select parts of the timeline and simultaneously analyze the behavior of parties and one or more deputies. Roll calls are represented as a combination of heatmaps and histograms. We illustrate the use of the different visualization techniques in a case study on party cohesiveness over time.","PeriodicalId":208985,"journal":{"name":"2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132994639","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: 6

Deep Transfer Learning for Segmentation of Anatomical Structures in Chest Radiographs 基于深度迁移学习的胸片解剖结构分割

2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) Pub Date : 2018-10-01 DOI: 10.1109/SIBGRAPI.2018.00033

H. Oliveira, J. A. D. Santos

{"title":"Deep Transfer Learning for Segmentation of Anatomical Structures in Chest Radiographs","authors":"H. Oliveira, J. A. D. Santos","doi":"10.1109/SIBGRAPI.2018.00033","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2018.00033","url":null,"abstract":"Segmentation of anatomical structures in Chest Posterior-Anterior Radiographs is a classical task on biomedical image analysis. Deep Learning has been widely used for detection and diagnosis of illnesses in several medical image modalities over the last years, but the portability of deep methods is still limited, hampering the reusability of pre-trained models in new data. We address this problem by proposing a novel method for Cross-Dataset Transfer Learning in Chest X-Ray images based on Unsupervised Image Translation architectures. Our Transfer Learning approach achieved Jaccard values of 88.20% on lung field segmentation in the Montgomery Set by using a pre-trained model on the JSRT dataset and no labeled data from the target dataset. Several experiments in unsupervised and semi-supervised transfer were performed and our method consistently outperformed simple fine-tuning when a limited amount of labels is used. Qualitative analysis on the tasks of clavicle and heart segmentation are also performed on Montgomery samples and pre-trained models from JSRT dataset. Our secondary contributions encompass several experiments in anatomical structure segmentation on JSRT, achieving state-of-the-art results in lung field (96.02%), heart (89.64%) and clavicle segmentation (87.30%).","PeriodicalId":208985,"journal":{"name":"2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124920366","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: 15

Semi-Supervised Learning with Interactive Label Propagation Guided by Feature Space Projections 基于特征空间投影的交互式标签传播半监督学习

2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) Pub Date : 2018-10-01 DOI: 10.1109/SIBGRAPI.2018.00057

B. C. Benato, A. Telea, A. Falcão

{"title":"Semi-Supervised Learning with Interactive Label Propagation Guided by Feature Space Projections","authors":"B. C. Benato, A. Telea, A. Falcão","doi":"10.1109/SIBGRAPI.2018.00057","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2018.00057","url":null,"abstract":"While the number of unsupervised samples for data annotation is usually high, the absence of large supervised training sets for effective feature learning and design of high-quality classifiers is a known problem whenever specialists are required for data supervision. By exploring the feature space of supervised and unsupervised samples, semi-supervised learning approaches can usually improve the classification system. However, these approaches do not usually exploit the pattern-finding power of the user's visual system during machine learning. In this paper, we incorporate the user in the semi-supervised learning process by letting the feature space projection of unsupervised and supervised samples guide the label propagation actions of the user to the unsupervised samples. We show that this procedure can significantly reduce user effort while improving the quality of the classifier on unseen test sets. Due to the limited number of supervised samples, we also propose the use of auto-encoder neural networks for feature learning. For validation, we compare the classifiers that result from the proposed approach with the ones trained from the supervised samples only and semi-supervised trained using automatic label propagation.","PeriodicalId":208985,"journal":{"name":"2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124684246","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: 23

Image-Based State Recognition for Disconnect Switches in Electric Power Distribution Substations 基于图像的配电变电站断开开关状态识别

2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) Pub Date : 2018-10-01 DOI: 10.1109/SIBGRAPI.2018.00062

B. Nassu, L. Lippmann, Bruno Marchesi, Amanda Canestraro, Rafael Wagner, Vanderlei Zarnicinski

{"title":"Image-Based State Recognition for Disconnect Switches in Electric Power Distribution Substations","authors":"B. Nassu, L. Lippmann, Bruno Marchesi, Amanda Canestraro, Rafael Wagner, Vanderlei Zarnicinski","doi":"10.1109/SIBGRAPI.2018.00062","DOIUrl":"https://doi.org/10.1109/SIBGRAPI.2018.00062","url":null,"abstract":"Knowing the state of the disconnect switches in a power distribution substation is important to avoid accidents, damaged equipment, and service interruptions. This information is usually provided by human operators, who can commit errors because of the cluttered environment, bad weather or lighting conditions, or lack of attention. In this paper, we introduce an approach for determining the state of each switch in a substation, based on images captured by regular pan-tilt-zoom surveillance cameras. The proposed approach includes noise reduction, image registration using phase correlation, and classification using a convolutional neural network and a support vector machine fed with gradient-based descriptors. By combining information given in an initial labeling stage with image processing techniques to reduce variations in viewpoint, our approach achieved 100% accuracy on experiments performed at a real substation over multiple days. We also show how modifications to the standard phase correlation image registration algorithm can make it more robust to lighting variations, and how SIFT (Scale-Invariant Feature Transform) descriptors can be made more robust in scenarios where the relevant objects may be brighter or darker than the background.","PeriodicalId":208985,"journal":{"name":"2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117016106","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: 9

End-to-End Bone Age Assessment with Residual Learning 残差学习的端到端骨龄评估

2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) Pub Date : 2018-10-01 DOI: 10.1109/SIBGRAPI.2018.00032

Daniel Souza, M. M. O. Neto

引用次数: 9

A Photon Tracing Approach to Solve Inverse Rendering Problems 一种解决逆向渲染问题的光子跟踪方法

2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) Pub Date : 2018-10-01 DOI: 10.1109/SIBGRAPI.2018.00038

Ignacio Avas, Eduardo Fernández

引用次数: 0

A Stable Greedy Insertion Treemap Algorithm for Software Evolution Visualization 一种用于软件进化可视化的稳定贪婪插入树图算法

2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) Pub Date : 2018-10-01 DOI: 10.1109/SIBGRAPI.2018.00027

E. F. Vernier, J. Comba, A. Telea

引用次数: 8

RISF: Recursive Iterative Spanning Forest for Superpixel Segmentation RISF:超像素分割的递归迭代生成森林

2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) Pub Date : 2018-10-01 DOI: 10.1109/SIBGRAPI.2018.00059

F. L. Galvão, A. Falcão, A. Chowdhury

引用次数: 13