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Deep Transfer Learning for Segmentation of Anatomical structures in Chest Radiographs

Deep Transfer Learning for Segmentation of Anatomical Struct...

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Brazilian Symposium on computer graphics and Image Processing (SIBGRAPI)

作者： Hugo Oliveira Jefersson dos Santos Department of Computer Science Universidade Federal de Minas Gerais Minas Gerais

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%).

关键词： Image segmentation Task analysis Lung computer architecture Heart Biomedical imaging Semantics

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Single-Shot Person Re-Identification Combining Similarity Metrics and Support Vectors

Single-Shot Person Re-Identification Combining Similarity Me...

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Brazilian Symposium on computer graphics and Image Processing (SIBGRAPI)

作者： Anderson Luís Cavalcanti Sales Rafael Henrique Vareto William Robson Schwartz Guillermo Camara Chavez Universidade Federal de Ouro Preto Ouro Preto Minas Gerais Brazil Department of Computer Science Universidade Federal de Minas Gerais Brazil

Person Re-Identification is all about determining a person's entire course as s/he walks around camera-equipped zones. More precisely, person Re-ID is the problem of matching human identities captured from non-overlapping surveillance cameras. In this work, we propose an approach that learns a new low-dimensional metric space in an attempt to cut down multi-camera matching errors. We represent the training and test samples by concatenating handcrafted features. Then, the method performs a two-step ranking using elementary distance metrics and followed by an ensemble of weighted binary classifiers. We validate our approach on CUHK01 and PRID450s datasets, providing only a sample per class for probe and only a sample for gallery (single-shot). According to the experiments, our method achieves CMC Rank-1 results up to 61.1 and 75.4, following leading literature protocols, for CUHK01 and PRID450s, respectively.

关键词： Cameras Measurement Probes Image color analysis Surveillance Feature extraction Support vector machines

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Object-Based Temporal Segment Relational Network for Activity Recognition

Object-Based Temporal Segment Relational Network for Activit...

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Brazilian Symposium on computer graphics and Image Processing (SIBGRAPI)

作者： Victor H. C. Melo Jesimon B. Santos Carlos Caetano Jessica Sena Otavio A. B. Penatti William Robson Schwartz Universidade Federal de Minas Gerais Belo Horizonte MG BR Adv. Technol. Samsung Res. Inst. Campinas Brazil Department of Computer Science Universidade Federal de Minas Gerais Brazil

Video understanding is the next frontier of computer vision, in which activity recognition plays a major role. Despite the recent improvements in holistic activity recognition, further researching part-based models such as context may allow us to better understand what is important for activities and thus improve our current activity recognition models. This work tackles contextual cues obtained from object detections, in which we posit that objects relevant to an action are related to its spatial arrangement regarding an agent. Based on that, we propose Egocentric Pyramid to encode such spatial relationships. We further extend it by proposing a data-centric approach named Temporal Segment Relational Network (TSRN). Our experiments give support to the hypothesis that object spatiality provides an important clue to activity recognition. In addition, our data-centric approach shows that besides such spatial features, there may be other important information that further enhances the object-based activity recognition, such as co-occurrence, relative size, and temporal information.

关键词： Activity recognition Videos Object detection Task analysis Detectors Histograms Dogs

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Visualization of Roll Call Data for Supporting Analyses of Political Profiles

Visualization of Roll Call Data for Supporting Analyses of P...

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Brazilian Symposium on computer graphics and Image Processing (SIBGRAPI)

作者： Rodrigo Nunes Moni da Silva Andre Spritzer Carla Dal Sasso Freitas Graduate Program in Computer Science Federal Univ. of Rio Grande do Su1 Porto Alegre RS Brazil Institute of Informatics Federal Univ. of Rio Grande do Su1 Porto Alegre RS Brazil

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.

关键词： Data visualization Principal component analysis Dimensionality reduction History Tools Voting Radar

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A Benchmark Methodology for Child Pornography Detection

A Benchmark Methodology for Child Pornography Detection

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Brazilian Symposium on computer graphics and Image Processing (SIBGRAPI)

作者： João Macedo Filipe Costa Jefersson A. dos Santos Brazilian Federal Police Belo Horizonte Minas Gerais Brazil CPqD - Image and Speech Processing Management Campinas São Paulo Brazil Department of Computer Science Universidade Federal de Minas Gerais Brazil

The acquisition and distribution of child sexual content are some of the most important concerns for legislative systems and law enforcement agencies around the world. There is a great demand for automatic detection of child pornography, mainly due to the large amount of existent data and the facility someone can share this content over the internet. Although there are some proposed methods to automatically detect child pornography content in the literature, there is no available dataset to assess and compare the performance of these methods due to legal restrictions, considering that in many countries the distribution or possession of this material is a crime by Law. To mitigate this problem, we work with the Brazilian Federal Police to structure and organize a benchmark methodology for child pornography to make it possible the comparison of distinct categories of child pornography detectors. Therefore, we present in this paper the used methodology for the creation of a new annotated dataset of images of child pornography. We also propose a child pornography detection step-wise methodology based on automatic age estimation combined with a pornography detector, which is evaluated using the described benchmark dataset. The proposed approach achieved results (79.84% accuracy) that overcome two tools currently used by the Brazilian Federal Police.

关键词： Face Estimation Law enforcement Detectors Image color analysis Visualization Feature extraction

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An Alternative Method of Pattern Recognition and Tracking of Moving Objects Using 3D Depth Sensors 31

An Alternative Method of Pattern Recognition and Tracking of...

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31st IEEE International conference on Advanced Information Networking and Applications (IEEE AINA)

作者： Sun, Ningping Okumura, Ryosuke Kumamoto Coll Natl Inst Technol Dept Human Oriented Informat Syst Engn 2659-2 Suya Koshi Kumamoto 8611102 Japan Kumamoto Coll Natl Inst Technol Adv Elect & Informat Syst Engn Course 2659-2 Suya Koshi Kumamoto 8611102 Japan

We proposed an alternative method to use 3D depth sensors for tracking moving target instead of normal camera, and we developed a pattern recognition algorithm based on our real-time tracking results. In this paper we... 详细信息

ISBN: (纸本)9781509062317

关键词： pattern recognition object tracking 3D depth sensor computer graphics transformation computation

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New Approaches to Human Gait Simulation Using Motion Sensors 31

New Approaches to Human Gait Simulation Using Motion Sensors

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31st IEEE International conference on Advanced Information Networking and Applications (IEEE AINA)

作者： Sun, Ningping Sakai, Yuya Kumamoto Coll Natl Inst Technol Dept Human Oriented Informat Syst Engn 2659-2 Suya Koshi Kumamoto 8611102 Japan Kumamoto Coll Natl Inst Technol Adv Elect & Informat Syst Engn Course 2659-2 Suya Koshi Kumamoto 8611102 Japan

We have introduced motion sensors into our researches of human motion capture in order to decrease the difficulty during anime production by free-hand. In this paper we will present an algorithm and approaches to simu... 详细信息

ISBN: (纸本)9781509062317

关键词： stride measurement simulation motion sensor computer graphics transformation human engineering

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Patch PlaNet: Landmark Recognition with Patch Classification Using Convolutional Neural Networks

Patch PlaNet: Landmark Recognition with Patch Classification...

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Brazilian Symposium on computer graphics and Image Processing (SIBGRAPI)

作者： Kelvin B. da Cunha Lucas Maggi Veronica Teichrieb João Paulo Lima Jonysberg Peixoto Quintino Fabio Q.B. da Silva André L.M. Santos Helder Pinho Voxar Labs - Centro de Informática Universidade Federal de Pernambuco Recife Brazil Universidade Federal Rural de Pernambuco Recife PE BR Projeto de P&D CIn/Samsung Universidade Federal de Pernambuco Recife Brazil Centro de Informática Universidade Federal de Pernambuco Recife Brazil Universidade Federal de Pernambuco Recife PE BR Desenvolvimento para a Informática Samsung Instituto de Campinas Brazil

In this work we address the problem of landmark recognition. We extend PlaNet, a model based on deep neural networks that approaches the problem of landmark recognition as a classification problem and performs the recognition of places around the world. We propose an extension of the PlaNet technique in which we use a voting scheme to perform the classification, dividing the image into previously defined regions and inferring the landmark based on these regions. The prediction of the model depends not only on the information of the features learned by the deep convolutional neural network architecture during training, but also uses local information from each region in the image for which the classification is made. To validate our proposal, we performed the training of the original PlaNet model and our variation using a database built with images from Flickr, and evaluated the models in the Paris and Oxford Buildings datasets. It was possible to notice that the addition of image division and voting structure improves the accuracy result of the model by 5-11 percentage points on average, reducing the level of ambiguity found during the inference of the model.

关键词： Planets Flickr Training Task analysis Image recognition Feature extraction computer architecture

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Real-Time Automatic License Plate Recognition through Deep Multi-Task Networks

Real-Time Automatic License Plate Recognition through Deep M...

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Brazilian Symposium on computer graphics and Image Processing (SIBGRAPI)

作者： Gabriel Resende Gonçalves Matheus Alves Diniz Rayson Laroca David Menotti William Robson Schwartz Department of Computer Science Universidade Federal de Minas Gerais Brazil Laboratory of Vision Robotics and Imaging Universidade Federal do Paraná Brazil

With the increasing number of cameras available in the cities, video traffic analysis can provide useful insights for the transportation segment. One of such analysis is the Automatic License Plate Recognition (ALPR). Previous approaches divided this task into several cascaded subtasks, i.e., vehicle location, license plate detection, character segmentation and optical character recognition. However, since each task has its own accuracy, the error propagation between each subtask is detrimental to the final accuracy. Therefore, focusing on the reduction of error propagation, we propose a technique that is able to perform ALPR using only two deep networks, the first performs license plate detection (LPD) and the second performs license plate recognition (LPR). The latter does not execute explicit character segmentation, which reduces significantly the error propagation. As these deep networks need a large number of samples to converge, we develop new data augmentation techniques that allow them to reach their full potential as well as a new dataset to train and evaluate ALPR approaches. According to experimental results, our approach is able to achieve state-of-the-art results in the SSIG-SegPlate dataset, reaching improvements up to 1.4 percentage point when compared to the best baseline. Furthermore, the approach is also able to perform in real time even in scenarios where many plates are present at the same frame, reaching significantly higher frame rates when compared with previously proposed approaches.

关键词： Licenses Detectors Task analysis Character recognition Proposals Image segmentation Real-time systems

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Analysis of the Influence of Color Normalization in the Classification of Non-Hodgkin Lymphoma Images

Analysis of the Influence of Color Normalization in the Clas...

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Brazilian Symposium on computer graphics and Image Processing (SIBGRAPI)

作者： Matheus G. Ribeiro Leandro A. Neves Guilherme F. Roberto Thaína A. A. Tosta Alessandro S. Martins Marcelo Z. do Nascimento Department of Computer Science and Statistics (DCCE) São Paulo State University (UNESP) São Paulo Brazil Universidade Federal de Uberlandia Uberlandia MG BR Center of Mathematics Computing and Cognition Federal University of ABC Santo André Uberlândia Brazil Federal Institute of Triangulo Mineiro (IFTM) Ituiutaba Minas Gerais Brazil Faculty of Computation (FACOM) Federal University of Uberlândia (UFU) Uberlândia Minas Gerais Brazil

In this work, a method is proposed to analyze the influence of color normalization in the classification lymphoma images. The approach combines multidimensional fractal techniques, curvelet transforms and Haralick features. The method considered a feature selection technique and different classification approaches to evaluate the combinations, such as decision tree, random forest, support vector machine, naive bayes and k-star. The classifications were analyzed considering three common lymphoma classes: mantle cell lymphoma, follicular lymphoma and chronic lymphocytic leukemia. The best result was achieved for the extraction from input images, features obtained mostly from lacunarity and percolation from curvelet sub-images, using random forest classifier. The tests were considered with 10-fold cross-validation and the result was a rate of AUC=0.963. The color normalization was not able to provide relevant classification rates. The obtained performance with the analysis over different types of features, classifiers and color normalization influence are important contributions to the identification of the lymphoma cancer.

关键词： Feature extraction Image color analysis Fractals Transforms Cancer Probabilistic logic Support vector machines

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