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Deep instance segmentation of teeth in panoramic X-ray images 31

Deep instance segmentation of teeth in panoramic X-ray image...

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31st Conference on graphics, Patterns and images (SIBGRAPI)

作者： Jader, Gil Fontinele, Jefferson Ruiz, Marco Abdalla, Kalyf Pithon, Matheus Oliveira, Luciano Univ Fed Bahia Intelligent Vis Res Lab Salvador BA Brazil Southeast State Univ Bahia Salvador BA Brazil

ISBN: (纸本)9781538692646

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 regionbased 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 Fl-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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Real-Time High-Quality Specular Highlight Removal using Efficient Pixel Clustering 31

Real-Time High-Quality Specular Highlight Removal using Effi...

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31st Conference on graphics, Patterns and images (SIBGRAPI)

作者： Souza, Antonio C. S. Macedo, Marcio C. F. Nascimento, Veronica P. Oliveira, Bruno S. Fed Inst Bahia Dept Comp Sci Salvador BA Brazil Univ Fed Bahia Dept Comp Sci Salvador BA Brazil Comp Brasil Salvador BA Brazil

ISBN: (纸本)9781538692646

On the basis of the dichromatic reflection model, recent specular highlight removal techniques typically estimate and cluster illumination chromaticity values to separate diffuse and specular reflection components from a single image. While these techniques are able to obtain visually pleasing results, their clustering algorithms suffer from bad initialization or are too costly to be computed in real time. In this paper, we propose a high-quality pixel clustering approach that allows the removal of specular highlights from a single image in real time. We follow previous work and estimate the minimum and maximum chromaticity values for every pixel. Then, we analyze the distribution pattern of those values in a minimum-maximum chromaticity space to propose an efficient pixel clustering approach. Afterwards, we estimate an intensity ratio for each cluster in order to separate diffuse and specular components. Finally, we present optimization strategies to implement our approach efficiently for both CPU and GPU architectures. Experimental results evaluated in the available dataset show that the proposed approach is not only more accurate, but is also two times faster than the state-ofthe-art when running solely on the CPU. Running on the GPU, we show that our approach requires approximate to 24 milliseconds to remove specular highlights in an image with 3840 x 2160 (4k) resolution. That makes our GPU implementation more than one order of magnitude (20 x) faster than the state-of-the-art for 4k resolution images, while providing the desired effect accurately.

关键词： Real-time systems Clustering algorithms image color analysis graphics processing units Task analysis Optimization image resolution

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A deep learning-based compatibility score for reconstruction of strip-shredded text documents 31

A deep learning-based compatibility score for reconstruction...

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31st Conference on graphics, Patterns and images (SIBGRAPI)

作者： Paixao, Thiago M. Berriel, Rodrigo F. Boeres, Maria C. S. Badue, Claudine De Souza, Alberto F. Oliveira-Santos, Thiago Univ Fed Espirito Santo Vitoria ES Brazil IFES Vitoria ES Brazil

ISBN: (纸本)9781538692646

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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SynLibras: A Disentangled Deep Generative Model for brazilian Sign Language Synthesis 35

SynLibras: A Disentangled Deep Generative Model for Brazilia...

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35th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Silveira, Wellington Alaniz, Andrew Hurtado, Marina da Silva, Bernardo Castello de Bem, Rodrigo Fed Univ Rio Grande FURG Ctr Computat Sci C3 Rio Grande Brazil

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

Recent advances regarding deep generative models have strengthened a realm of approaches in which discriminative and generative tasks are tackled jointly in an analysis-by-synthesis manner. In this category, variational autoencoders (VAEs) and generative adversarial networks (GANs) aim for learning latent data representations from which sampling of synthetic images may be performed. However, sampling in such models normally does not allow for independent control of diverse factors of variation. Despite general efforts to overcome this issue, deep generative models tailored for sign language with disentangled factors of variation are yet not vastly explored in the literature. In this work, we introduce the SynLibras, a novel model that allows for disentangling appearance and gestural communication (i.e. body, hands and face poses) on image synthesis. Our model is capable of performing cross-language pose-transfer while maintaining the appearance of the source signer. We perform experiments on the RWTH-PHOENIX-Weather dataset and evaluation using the PSNR and the SSIM metrics. To our knowledge, the SynLibras is the first method for brazilian sign language (Libras) synthesis in images. We compare our model with the EDN, a well-known general pose-transfer method, achieving better results on Libras synthesis. Finally, we also introduce the SynLibras-Pose, a dataset with annotated poses of Libras signers performing single words.

关键词： Measurement graphics Analytical models image synthesis Gesture recognition Assistive technologies Generative adversarial networks

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Analysis of the Influence of Color Normalization in the Classification of non-Hodgkin Lymphoma images 31

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

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31st Conference on graphics, Patterns and images (SIBGRAPI)

作者： Ribeiro, Matheus G. Neves, Leandro A. Roberto, Guilherme F. Tosta, Thaina A. A. Martins, Alessandro S. do Nascimento, Marcelo Z. Sao Paulo State Univ UNESP Dept Comp Sci & Stat DCCE Sao Jose Do Rio Preto SP Brazil Fed Univ Uberlandia UFU Fac Computat FACOM Uberlandia MG Brazil Fed Univ ABC Ctr Math Comp & Cognit Santo Andre SP Brazil Fed Inst Triangulo Mineiro IFTM Ituiutaba MG Brazil

ISBN: (纸本)9781538692646

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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Real-time Automatic License Plate Recognition Through Deep Multi-Task Networks 31

Real-time Automatic License Plate Recognition Through Deep M...

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31st Conference on graphics, Patterns and images (SIBGRAPI)

作者： Goncalves, Gabriel R. Diniz, Matheus A. Laroca, Rayson Menotti, David Schwartz, William Robson Univ Fed Minas Gerais Dept Comp Sci Smart Sense Lab Belo Horizonte MG Brazil Univ Fed Parana Lab Vis Robot & Imaging Curitiba Parana Brazil

ISBN: (纸本)9781538692646

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-theart 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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Barrett's Esophagus Identification Using Color Co-occurrence Matrices 31

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

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31st Conference on graphics, Patterns and images (SIBGRAPI)

作者： de Souza, Luis A., Jr. Ebigbo, Alanna Probst, Andreas Messmann, Helmut Papa, Joao P. Mendel, Robert Palm, Christoph Univ Fed Sao Carlos Dept Comp Sao Carlos SP Brazil Klinikum Augsburg III Med Klin Augsburg Germany Sao Paulo State Univ Dept Comp Bauru Brazil Ostbayer Tech Hsch Regensburg Regensburg Med Image Comp Regensburg Germany

ISBN: (纸本)9781538692646

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： Barrett's Esophagus Co-occurrence Matrices Machine Learning Texture Analysis

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The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments 31

The Impact of Preprocessing on Deep Representations for Iris...

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31st Conference on graphics, Patterns and images (SIBGRAPI)

作者： Zanlorensi, Luiz A. Luz, Eduardo Laroca, Rayson Britto, Alceu S., Jr. Oliveira, Luiz S. Menotti, David Fed Univ Parana UFPR Dept Informat Curitiba PR Brazil Fed Univ Ouro Preto UFOP Comp Dept Ouro Preto MG Brazil Pontifical Catholic Univ Parana PUCPR Postgrad Program Informat Curitiba PR Brazil

ISBN: (纸本)9781538692646

The use of iris as a biometric trait is widely used because of its high level of distinction and uniqueness. Nowadays, one of the major research challenges relies on the recognition of iris images obtained in visible spectrum under unconstrained environments. In this scenario, the acquired iris are affected by capture distance, rotation, blur, motion blur, low contrast and specular reflection, creating noises that disturb the iris recognition systems. Besides delineating the iris region, usually preprocessing techniques such as normalization and segmentation of noisy iris images are employed to minimize these problems. But these techniques inevitably run into some errors. In this context, we propose the use of deep representations, more specifically, architectures based on VGG and ResNet-50 networks, for dealing with the images using (and not) iris segmentation and normalization. We use transfer learning from the face domain and also propose a specific data augmentation technique for iris images. Our results show that the approach using non-normalized and only circle-delimited iris images reaches a new state of the art in the official protocol of the *** competition, a subset of the UBIRIS database, one of the most challenging databases on unconstrained environments, reporting an average Equal Error Rate (EER) of 13.98% which represents an absolute reduction of about 5%.

关键词： Iris recognition Databases image segmentation Feature extraction Deep learning Protocols image recognition

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Evaluating Deep Neural Networks for image Document Enhancement 21

Evaluating Deep Neural Networks for Image Document Enhanceme...

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21st ACM symposium on Document Engineering (DocEng)

作者： Kirsten, Lucas N. Piccoli, Ricardo Ribani, Ricardo HP Inc R&D Porto Alegre RS Brazil HP Inc R&D Barueri Brazil

ISBN: (纸本)9781450385961

This work evaluates six state-of-the-art deep neural network (DNN) architectures applied to the problem of enhancing camera-captured document images. The results from each network were evaluated both qualitatively (i. e. with manual visual inspection) and quantitatively (i. e. using image Quality Assessment metrics - IQA), and also compared with an existing approach based on traditional computer vision techniques. The best performing architectures generally produced good enhancement compared to the existing algorithm, showing that it is possible to use DNNs for document image enhancement. Furthermore, the best performing architectures could work as a baseline for future investigations on document enhancement using deep learning techniques. The main contributions of this paper are: a baseline of deep learning techniques that can be further improved to provide better results, and a evaluation methodology using IQA metrics for quantitatively comparing the produced images from the neural networks to a ground truth.

关键词： document analysis image enhancement deep learning

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Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks 31

Robust Iris Segmentation Based on Fully Convolutional Networ...

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31st Conference on graphics, Patterns and images (SIBGRAPI)

作者： Bezerra, Cides S. Laroca, Rayson Lucio, Diego R. Severo, Evair Oliveira, Lucas F. Britto, Alceu S., Jr. Menotti, David Fed Univ Parana UFPR Curitiba PR Brazil Pontifical Catholic Univ Parana PUCPR Curitiba PR Brazil

ISBN: (纸本)9781538692646

The iris can be considered as one of the most important biometric traits due to its high degree of uniqueness. Iris-based biometrics applications depend mainly on the iris segmentation whose suitability is not robust for different environments such as near-infrared (NIR) and visible (VIS) ones. In this paper, two approaches for robust iris segmentation based on Fully Convolutional Networks (FCNs) and Generative Adversarial Networks (GANs) are described Similar to a common convolutional network, but without the fully connected layers (i.e., the classification layers), an FCN employs at its end a combination of pooling layers from different convolutional layers. Based on the game theory, a GAN is designed as two networks competing with each other to generate the best segmentation. The proposed segmentation networks achieved promising results in all evaluated datasets (i.e., BioSec, CasiaI3, CasiaT4, IITD-1) of NIR images and (NICE.I, CrEye-Iris and MICHE-I) of VIS images in both non-cooperative and cooperative domains, outperforming the baselines techniques which are the best ones found so far in the literature, i.e., a new state of the art for these datasets. Furthermore, we manually labeled 2,431 images from CasiaT4, CrEye-Iris and MICHE-I datasets, making the masks available for research purposes.

关键词： image segmentation Iris recognition Iris Generative adversarial networks Gallium nitride Biomedical imaging Feature extraction

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