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Real-Time High-Quality Specular Highlight Removal Using Efficient Pixel Clustering

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

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Antonio C.S. Souza Márcio C.F. Macedo Verônica P. Nascimento Bruno S. Oliveira Dept. of Comput. Sci. Fed. Inst. of Bahia Salvador Brazil Universidade Federal da Bahia Salvador BA BR Computaçáo Brasil Salvador BA Brazil

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-of-the-art when running solely on the CPU. Running on the GPU, we show that our approach requires ≈24 milliseconds to remove specular highlights in an image with 3840×2160 (4k) resolution. That makes our GPU implementation more than one order of magnitude (20×) 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 Square Pointillism image Generation, and its GPU Acceleration 5

A Square Pointillism Image Generation, and its GPU Accelerat...

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5th International symposium on Computing and Networking (CANDAR)

作者： Tokura, Hiroki Kuroda, Yuki Ito, Yasuaki Nakano, Koji Hiroshima Univ Dept Informat Engn Kagamiyama 1-4-1 Higashihiroshima 7398527 Japan

ISBN: (纸本)9781538620878

Non-photorealistic rendering is one of the digital art techniques. It generates digital images resembling artistic representation. The main contribution of this paper is to show a non-photorealistic rendering for high quality pointillism image generation with squares by pasting square patterns on canvas. Our technique is inspired by the characteristic of the human visual system to optimize generated images. Although it can generate high quality pointillistic images, a lot of time is necessary. Hence, we have implemented our technique in a graphics processing unit (GPU) to accelerate the computation. The experimental results show that the GPU implementation can achieve a speed-up factor of 160 over the sequential CPU implementation.

关键词： image generation image color analysis graphics processing units Visual systems Microsoft Windows Rendering (computer graphics) Central processing Unit image generation central processing units graphics processing Unit image colour analysis Microsoft Windows(software) Visual System pointillism GRAPPER PICK UP Canvas

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A Pilot Study on Iranian Skin Color

A Pilot Study on Iranian Skin Color

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2018 Colour and Visual Computing symposium, CVCS 2018

作者： Ansari-Asl, Majid Ansari, Keivan Color Imaging Laboratory University of Granada E-Granada Spain Department of Color Imaging Color Image Processing Iranian Institute for Color Science and Technology Tehran Iran

ISBN: (纸本)9781538656457

Accurate measurement of the human skin color is very important in many multidisciplinary applications such as medicine for diagnostic purposes and prostheses color matching, cosmetology for makeup and skin care, computer graphics for skin rendering, and computer vision for skin detection and tracking. It must be noted that in a previous study we found that the Iranian skin colors are not truly encompassed by the Pantone SkinTone Guide, so the present study was carried out. In this research, a pilot dataset of Iranian skin color was collected to analyze its properties due to the variations in different parameters which affect the skin color and also to compare Iranian skin color gamut with different international ethnicities. The skin spectral data for a group of 60 subjects were taken at three reference locations on the face: the forehead, the left cheek, and the right cheek. The measurements were done using a spectrophotometer after filling in a questionnaire consists of a consent form in addition to name, gender, age, parent ethnicities, and blood type of the individuals. Afterward, the spectral data were converted into L∗ (Lightness), a∗ (Redness) and b∗ (Yellowness) color coordinates. These coordinate values of skin colors were analyzed according to gender, age, body part, and blood type. The results showed variations in skin color due to gender, age, body part, and ethnicity differences. Iranian males are found to have a little darker and more reddish and yellowish skin than females, and it is reported that the age increase causes a decrease in lightness and yellowness and an increase in redness for the collected data. Moreover, cheeks are identified lighter and less reddish than foreheads. A comprehensive skin color gamut is presented for Iranian population which can be used to develop a national skin color guide. © 2018 IEEE.

关键词： Color matching

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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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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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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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GPU Accelerated Contactless Human Machine Interface for Driving Car 16

GPU Accelerated Contactless Human Machine Interface for Driv...

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16th International symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Magoules, Frederic Zou, Qinmeng Univ Paris Saclay CentraleSupelec Gif Sur Yvette France

ISBN: (纸本)9781538621622

In this paper we present an original contactless human machine interface for driving car. The proposed framework is based on the image sent by a simple camera device, which is then processed by various computer vision algorithms. These algorithms allow the isolation of the user's hand on the camera frame and translate its movements into orders sent to the computer in a real time process. The optimization of the implemented algorithms on graphics processing unit leads to real time interaction between the user, the computer and the machine. The user can easily modify or create the interfaces displayed by the proposed framework to fit his personnel needs. A contactless driving car interface is here produced to illustrate the principle of our framework.

关键词： computer vision gesture analysis video analysis image processing machine learning parallel computing graphics processing unit

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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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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 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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