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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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EdgeIso: Effective Performance Isolation for Edge Devices

EdgeIso: Effective Performance Isolation for Edge Devices

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International symposium on Parallel and Distributed processing (IPDPS)

作者： Yoonsung Nam Yongjun Choi Byeonghun Yoo Hyeonsang Eom Yongseok Son Department of Computer Science and Engineering Seoul National University Seoul South Korea School of Computer Science and Engineering Chung-Ang University Seoul South Korea

ISBN: (数字)9781728168760

ISBN: (纸本)9781728168777

Edges enable cloud services to be provided at low-latency and efficiently reduce the amount of transferred data by placing latency-critical tasks close to users. However, multi-tasking results in resource contention on edge devices, making it challenging to meet the service level objectives (SLOs) of tasks. Compared to the clouds, edges have relatively limited resources, but their tasks are required to meet a higher level of SLOs than clouds. Furthermore, modern edge devices equipped with additional accelerators (e.g., GPU) may worsen the resource contention due to the edge's integrated architecture, sharing the memory bandwidth between CPUs and accelerators. To address these challenges, we present EdgeIso, a light-weight scheduler that dynamically isolates the performance of tasks on edges. EdgeIso periodically monitors the resource contention and mitigates the contention to meet the SLOs of tasks by efficiently enforcing several isolation techniques (e.g., DVFS and core allocation) in an incremental manner. Moreover, it detects the changes of task executions or offered loads for tasks, thus handling high load fluctuations adaptively. We implement EdgeIso as a user-level scheduler on the Linux integrates into an NVIDIA Jetson TX2. Our experimental results show that EdgeIso improves the performance of the low-latency tasks significantly while improving resource efficiency compared with both the offloading and reservation scheme used in clouds.

关键词： Task analysis image edge detection graphics processing units Cloud computing Bandwidth Resource management Performance evaluation

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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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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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Exploring Double Cross Cyclic Interpolation in Unpaired image-to-image Translation

Exploring Double Cross Cyclic Interpolation in Unpaired Imag...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Jorge López Antoni Mauricio Guillermo Cámara Universidad Catolica San Pablo Arequipa Peru Department of Computer Science Universidad Catòlica San Pablo Arequipa Perú Federal University of Ouro Preto Ouro Preto Brazil

The unpaired image-to-image translation consists of transferring a sample a in the domain A to an analog sample b in the domain B without intensive pixel-to-pixel supervision. The current vision focuses on learning a generative function that maps both domains but ignoring the latent information, although its exploration is not explicit supervision. This paper proposes a cross-domain GAN-based model to achieve a bi-directional translation guided by latent space supervision. The proposed architecture provides a double-loop cyclic reconstruction loss in an exchangeable training adopted to reduce mode collapse and enhance local details. Our proposal has outstanding results in visual quality, stability, and pixel-level segmentation metrics over different public datasets.

关键词： Gallium nitride Generators image reconstruction Zirconium Training Task analysis Mathematical model

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Hierarchy-of-Visual-Words: a Learning-Based Approach for Trademark image Retrieval

Hierarchy-of-Visual-Words: a Learning-Based Approach for Tra...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Vítor N. Lourenço Gabriela G. Silva Leandro A. F. Fernandes Universidade Federal Fluminense Niteroi Rio de Janeiro Brazil Instituto de Computação Universidade Federal Fluminense Niterói Rio de Janeiro Brazil

In this paper, we present the Hierarchy-of-Visual-Words (HoVW), a novel trademark image retrieval (TIR) method that decomposes images into simpler geometric shapes and defines a descriptor for binary trademark image representation by encoding the hierarchical arrangement of component shapes. The proposed hierarchical organization of visual data stores each component shape as a visual word. It is capable of representing the geometry of individual elements and the topology of the trademark image, making the descriptor robust against linear as well as to some level of nonlinear transformation. Experiments show that HoVW outperforms previous TIR methods on the MPEG-7 CE-1 and MPEG-7 CE-2 image databases.

关键词： Shape Trademarks Visualization Feature extraction Training image retrieval Topology

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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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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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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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Exploring Hierarchy Simplification for Non-Significant Region Removal

Exploring Hierarchy Simplification for Non-Significant Regio...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Isabela Borlido Barcelos Gabriel Barbosa da Fonseca Laurent Najman Yukiko Kenmochi Benjamin Perret Jean Cousty Zenilton K. G. do Patrocínio Silvio Jamil F. Guimarães Universite Paris-Est Noisy-le-Grand France Université Paris-Est CNRS Noisy-le-Grand France

image segmentation is a classic subject in the field of digital image processing, and it can be used to solve a large variety of problems or serve as preprocessing for other methods of image analysis. Hierarchical image segmentation methods provide a multiscale representation, therefore they produce a nested set of image segmentations in which a result at a given level can be produced by merging regions of the segmentation at its previous level. However, a hierarchical representation may produce small components at its coarser levels, leading to over segmentations on such scales. To solve this problem, we explore strategies to simplify hierarchies in order to remove non-significant regions, in terms of area, while trying to preserve the hierarchical structure. We evaluate the proposed simplification strategies with different hierarchical segmentation methods on the Pascal Context dataset by using precision-recall measures and fragmentation curves, along with a qualitative assessment showing that the simplification of hierarchies can lead to visually better image segmentations.

关键词： image segmentation image edge detection Merging image analysis Task analysis Transforms graphics

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