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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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Demo abstract: Taming many heterogeneous cores

Demo abstract: Taming many heterogeneous cores

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IEEE symposium on Real-Time and Embedded Technology and Applications

作者： Nils Asmussen Marcus Volp Benedikt Nothen Annett Ungethum Technische Universität Dresden Dresden Germany

Many-core systems are increasingly used in real-time settings to meet the performance requirements of advanced applications such as the classification and tracking of dynamic objects for autonomous driving [1] or the generation of safe trajectories through rough terrain [2]. Task sets of these applications are often mixtures of short running, low latency tasks, such as the various filtering steps required for signal or image processing, and long running tasks, such as route planning, which occupy their assigned core for extended periods of time. Short running tasks often follow a data flow programming paradigm and are organized into directed acyclic graphs (DAG) based on their input-/output-dependencies. Once these dependencies are met, they execute without further task interactions until they complete producing outputs for subsequent tasks. Long running tasks on the other hand interact frequently with other tasks, accessing data located in the memories of remote cores or interacting with operating-system services. This demonstrator shows how both types of applications can be integrated into a single many-core architecture.

关键词： computer architecture Real-time systems Trajectory Digital signal processing Kernel Hardware Mobile robots

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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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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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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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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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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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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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Localizing Pulmonary Lesions Using Fuzzy Deep Learning

Localizing Pulmonary Lesions Using Fuzzy Deep Learning

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International symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Esmitt Ramírez Carles Sánchez Debora Gil Autonomous University of Barcelona Barcelona Spain Computer Vision Center Autonomous University of Barcelona Spain

ISBN: (数字)9781728157245

ISBN: (纸本)9781728157252

The usage of medical images is part of the clinical daily in several healthcare centers around the world. Particularly, computer Tomography (CT) images are an important key in the early detection of suspicious lung lesions. The CT image exploration allows the detection of lung lesions before any invasive procedure (e.g. bronchoscopy, biopsy). The effective localization of lesions is performed using different image processing and computer vision techniques. Lately, the usage of deep learning models into medical imaging from detection to prediction shown that is a powerful tool for computer-aided software. In this paper, we present an approach to localize pulmonary lung lesion using fuzzy deep learning. Our approach uses a simple convolutional neural network based using the LIDC-IDRI dataset. Each image is divided into patches associated a probability vector (fuzzy) according their belonging to anatomical structures on a CT. We showcase our approach as part of a full CAD system to exploration, planning, guiding and detection of pulmonary lesions.

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