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A Deep Learning-Based Compatibility Score for Reconstruction of strip-Shredded Text Documents

A Deep Learning-Based Compatibility Score for Reconstruction...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Thiago M. Paixão Rodrigo F. Berriel Maria C.S. Boeres Claudine Badue Alberto F. De Souza Thiago Oliveira-Santos Universidade Federal do Espírito Santo (UFES)

The use of paper-shredder machines (mechanical shredding) to destroy documents can be illicitly motivated when the purpose is hiding evidence of fraud and other sorts of crimes. Therefore, reconstructing such documents is of great value for forensic investigation, but it is admittedly a stressful and time-consuming task for humans. To address this challenge, several computational techniques have been proposed in literature, particularly for documents with text-based content. In this context, a critical challenge for automated reconstruction is to measure properly the fitting (compatibility) between paper shreds (strips), which has been observed to be the main limitation of literature on this topic. The main contribution of this paper is a deep learning-based compatibility score to be applied in the reconstruction of strip-shredded text documents. Since there is no abundance of real-shredded data, we propose a training scheme based on digital simulated-shredding of documents from a well-known OCR database. The proposed score was coupled to a black-box optimization tool, and the resulting system achieved an average accuracy of 94.58% in the reconstruction of mechanically-shredded documents.

关键词： strips image reconstruction Training Pipelines Databases Optimization Computational modeling

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A Novel Fingerprint Quality Assessment Based on Gabor Filters

A Novel Fingerprint Quality Assessment Based on Gabor Filter...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Igor Lucena Peixoto Andrezza João Janduy Brasileiro Primo Erick Vagner Cabral de Lima Borges Arnaldo Gualberto de Andrade e Silva Leonardo Vidal Batista Herman Martins Gomes Vsoft Tecnologia João Pessoa-PB Brazil Federal University of Campina Grande Campina Grande-PB Brazil Federal University of Paraiba João Pessoa-PB Brazil

Fingerprints are the most widely deployed biometric characteristics. However, the recognition of a fingerprint may be influenced by a lot of factors (e.g., skin conditions, sensor conditions) and a matching algorithm is highly affected by the quality of the images involved. This work proposes a novel method for Fingerprint Quality Assessment (FQA) based on the analysis of the Gabor filters response on a fingerprint image. The correlation between the worst quality templates and the matching score has also been analyzed. The method is validated on FVC2000DB3, FVC2004DB2, FVC2004DB3, and FVC2006DB3 databases. This work was compared to other FQAs in order to evaluate performance and with different matching algorithms for fair comparison. The results found pointed that the proposed method is able to identify the images which most affect the error rates of an AFIS, better than the other methods presented in the literature.

关键词： Fingerprint recognition Quality assessment Databases standards Histograms Forensics image recognition

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Delaunay Triangulation Data Augmentation Guided by Visual Analytics for Deep Learning

Delaunay Triangulation Data Augmentation Guided by Visual An...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Alan Zanoni Peixinho Bárbara Caroline Benato Luis Gustavo Nonato Alexandre Xavier Falcão Institute of Computing University of Campinas Campinas Brazil Institute of Mathematical and Computer Sciences University of São Paulo São Carlos Brazil

It is well known that image classification problems can be effectively solved by Convolutional Neural Networks (CNNs). However, the number of supervised training examples from all categories must be high enough to avoid model overfitting. In this case, two key alternatives are usually presented (a) the generation of artificial examples, known as data augmentation, and (b) reusing a CNN previously trained over a large supervised training set from another image classification problem - a strategy known as transfer learning. Deep learning approaches have rarely exploited the superior ability of humans for cognitive tasks during the machine learning loop. We advocate that the expert intervention through visual analytics can improve machine learning. In this work, we demonstrate this claim by proposing a data augmentation framework based on Encoder-Decoder Neural Networks (EDNNs) and visual analytics for the design of more effective CNN-based image classifiers. An EDNN is initially trained such that its encoder extracts a feature vector from each training image. These samples are projected from the encoder feature space on to a 2D coordinate space. The expert includes points to the projection space and the feature vectors of the new samples are obtained on the original feature space by interpolation. The decoder generates artificial images from the feature vectors of the new samples and the augmented training set is used to improve the CNN-based classifier. We evaluate methods for the proposed framework and demonstrate its advantages using data from a real problem as case study - the diagnosis of helminth eggs in humans. We also show that transfer learning and data augmentation by affine transformations can further improve the results.

关键词： Training Visual analytics Feature extraction Impurities Deep learning Decoding

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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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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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Barrett's Esophagus Identification Using Color Co-Occurrence Matrices

Barrett's Esophagus Identification Using Color Co-Occurrence...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Luis Souza Alanna Ebigbo Andreas Probst Helmut Messmann João Paulo Papa Robert Mendel Christoph Palm Universidade Federal de Sao Carlos Sao Carlos SP BR Klinikum Augsburg III Medizinische Klinik Augsburg Germany Department of Computing São Paulo State University Bauru Brazil Regensburg Medical Image Computing Ostbayerische Technische Hochschule Regensburg Regensburg Germany

In this work, we propose the use of single channel Color Co-occurrence Matrices for texture description of Barrett's Esophagus (BE) and adenocarcinoma images. Further classification using supervised learning techniques, such as Optimum-Path Forest (OPF), Support Vector Machines with Radial Basis Function (SVM-RBF) and Bayesian classifier supports the context of automatic BE and adenocarcinoma diagnosis. We validated three approaches of classification based on patches, patients and images in two datasets (MICCAI 2015 and Augsburg) using the color-and-texture descriptors and the machine learning techniques. Concerning MICCAI 2015 dataset, the best results were obtained using the blue channel for the descriptors and the supervised OPF for classification purposes in the patch-based approach, with sensitivity nearly to 73% for positive adenocarcinoma identification and specificity close to 77% for BE (non-cancerous) patch classification. Regarding the Augsburg dataset, the most accurate results were also obtained using both OPF classifier and blue channel descriptor for the feature extraction, with sensitivity close to 67% and specificity around to 76%. Our work highlights new advances in the related research area and provides a promising technique that combines color and texture information, allied to three different approaches of dataset pre-processing aiming to configure robust scenarios for the classification step.

关键词： image color analysis Feature extraction Databases Gray-scale Esophagus Histograms Protocols

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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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Real-Time Motion Feedback System based on Smart Mirror Vision

Real-Time Motion Feedback System based on Smart Mirror Visio...

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International symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Seoungtak Kim Dasol Seo Sangyong Lee Yeonjun Kim Hyun Wook Kang Yong-Sik Choi Jin-Woo Jung Dongguk University Seoul Republic of Korea

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

Modern people do not have much time to take care of their health due to heavy workloads. Therefore, we propose a smart mirror system, through which people can take exercises at home and get corrections for their movements. The users' motions are sent to the server using Raspberry Pi. Once the server receives the video, it sends the correct movements back to the user. Upon receiving the correct movements, the feedback, which is provided from the Guide video, the user can compare their movements to it. The users can correct their motions by following the correct motions provided from the Guide video. The incorrect motion is determined by the gap of the angle of the user's joints from the Guide video. This proposed Smart mirror system is assumed to contribute to people's health due to its ease of use and greater accessibility.

关键词： streaming media Servers Mirrors graphics processing units Real-time systems image color analysis computer science

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Towards Fast Edge Detection Approach for Industrial Products

Towards Fast Edge Detection Approach for Industrial Products

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IEEE International symposium on Parallel and Distributed processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Dhafer G. Honi Ali Hasan Ali Zaid Ameen Abduljabbar Junchao Ma Vincent Omollo Nyangaresi Keyan Abdul-Aziz Mutlaq Samir M. Umran College of Education for Pure Sciences University of Basrah Basrah Iraq College of Big Data and Internet Shenzhen Technology University Shenzhen China Faculty of Biological and Physical Tom Mboya University Homabay Kenya It and Communication center University of Basrah Basrah Iraq School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

Edge detection is a critical component of many elements of image processing applications and computer visualization, such as image segmentation, detection of imperfections in industrial products, medical image processing, and object identification. The edge detection technique seeks to accelerate image analysis by limiting the quantity of information processed. The object’s dimensions are a major component in manufacturing, engineering and other crucial fields, therefore the less time this takes, the more efficient the whole process becomes. This study details the use of unique image processing technology to advance the automated quantifying of an industrial object’s dimensions. Firstly, an image improvement approach founded on fuzzy entropy was devised to clarify the original deteriorated image. Secondly, the edges of the object were detected via the method of quick fuzzy edge detection. Finally, using Freeman Chain Codes (FCC), the essential corner locations were determined. Thus, the edges and corners of any given object image are accurately detected. This strategy is uncomplicated to implement, can efficiently identify size, shape, and any defects, and helps to determine acceptance or rejection of the final product. The results of the corresponding experiment indicate that the proposed method has a much lower computational cost in general compared to the related works.

关键词： Visualization image analysis Shape image edge detection FCC Ubiquitous computing Entropy

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Deep Instance Segmentation of Teeth in Panoramic X-Ray images

Deep Instance Segmentation of Teeth in Panoramic X-Ray Image...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Gil Jader Jefferson Fontineli Marco Ruiz Kalyf Abdalla Matheus Pithon Luciano Oliveira Intelligent Vision Research Lab Federal University of Bahia Southeast State University of Bahia

In dentistry, radiological examinations help specialists by showing structure of the tooth bones with the goal of screening embedded teeth, bone abnormalities, cysts, tumors, infections, fractures, problems in the temporomandibular regions, just to cite a few. Sometimes, relying solely in the specialist's opinion can bring differences in the diagnoses, which can ultimately hinder the treatment. Although tools for complete automatic diagnosis are no yet expected, image pattern recognition has evolved towards decision support, mainly starting with the detection of teeth and their components in X-ray images. Tooth detection has been object of research during at least the last two decades, mainly relying in threshold and region-based methods. Following a different direction, this paper proposes to explore a deep learning method for instance segmentation of the teeth. To the best of our knowledge, it is the first system that detects and segment each tooth in panoramic X-ray images. It is noteworthy that this image type is the most challenging one to isolate teeth, since it shows other parts of patient's body (e.g., chin, spine and jaws). We propose a segmentation system based on mask region-based convolutional neural network to accomplish an instance segmentation. Performance was thoroughly assessed from a 1500 challenging image data set, with high variation and containing 10 categories of different types of buccal image. By training the proposed system with only 193 images of mouth containing 32 teeth in average, using transfer learning strategies, we achieved 98% of accuracy, 88% of F1-score, 94% of precision, 84% of recall and 99% of specificity over 1224 unseen images, results very superior than other 10 unsupervised methods.

关键词： Teeth image segmentation Dentistry X-ray imaging Training image restoration Feature extraction

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