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检索条件"任意字段=Brazilian Symposium on Computer Graphics and Image Processing"

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Towards real-time and real-life image classification and detection using CNN: a review of practical applications requirements, algorithms, hardware and current trends

Towards real-time and real-life image classification and det...

引用

International symposium for Design and Technology of Electronics Packages (SIITME)

作者： Mariana Eugenia Ilas Constantin Ilas “Politehnica” University of Bucharest Telecom. and IT Bucharest Romania “Politehnica” University of Bucharest Bucharest Romania

ISBN: (数字)9781728175065

ISBN: (纸本)9781728175072

CNN are already used in production applications in some areas, whereas for many real-life applications this is not possible yet, mainly because of speed and/ or power consumption limitations. In parallel, there are sustained efforts for simplifying CNN structure and for designing new processor architectures. In this paper we analyze the requirements of inference time and power consumption for a large variety of applications, running on all type of platforms, from edge devices to servers. We also review all hardware systems which can be used for CNN implementation, GPUs, TPUs, FPGA, prototype ASICs. Finally, we correlate the performance of these devices with the real-time and power requirements and the complexity of most popular CNN architectures. Thus, we determine what types of practical applications are currently possible, quantify the gaps for the others, and discuss how these gaps can be reduced.

关键词： Power demand Real-time systems Hardware Complexity theory Automobiles graphics processing units computer architecture

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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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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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GPU-Based Rendering of Arbitrarily Complex Cutting Surfaces for Black Oil Reservoir Models

GPU-Based Rendering of Arbitrarily Complex Cutting Surfaces ...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Bernardo Franceschin Frederico Abraham Luiz Felipe Netto Waldemar Celes Computer Science Department Pontifical Catholic University of Rio de Janeiro Brazil

Numerical simulation of black oil reservoir models is extensively used by the oil industry to predict and plan field exploration. Such simulations produce a large amount of volume data that need to be inspected. One popular visualization technique to inspect volume data is the rendering of cutting surfaces, shaded by mapping properties associated with model elements. In this work, an efficient GPU-based algorithm for rendering arbitrarily complex cutting surfaces for reservoir models is presented. The rendering strategy is based on an efficient point location algorithm. The proposal includes a compact representation of reservoir models in the GPU memory, the use of a compact regular grid as the acceleration technique, and an accurate point location algorithm for handling hexahedral elements with non-planar faces. Computational experiments have demonstrated the effectiveness and efficiency of the proposed approach, even when applied to large models. A set of applications is discussed in the context of inspecting reservoir simulation results.

关键词： Reservoirs Oils Rendering (computer graphics) Computational modeling graphics processing units Solid modeling Numerical models

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Procedural Editing of Virtual Terrains Using 3D Bézier Curves

Procedural Editing of Virtual Terrains Using 3D Bézier Curv...

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brazilian symposium on Games and Digital Entertainment (SBGAMES)

作者： Bruno Torres do Nascimento Cesar Tadeu Pozzer Flávio Paulus Franzin Universidade Federal de Santa Maria Santa Maria Brazil

The process of manually shaping and adding features to virtual terrains is a time-consuming task prevalent during the development process of many applications, such as games and virtual simulators. We present a technique to shape virtual terrains by creating excavations and embankments to flatten and smooth the terrain along paths defined by composite cubic 3D Bézier curves. The curves are procedurally generated based on an input set of vertices. Our approach is capable of carving smooth paths on terrains for placement or rendering of roads, railways, rivers, or any other feature that follows a path. We employ spatial hashing and data structures to optimize execution on the GPU.

关键词： Roads Three-dimensional displays Table lookup graphics processing units Rendering (computer graphics) Rivers Two dimensional displays

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GPU-Based Rendering and Collision Simulation of Ground Vegetation in Large-Scale Virtual Scenarios

GPU-Based Rendering and Collision Simulation of Ground Veget...

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brazilian symposium on Games and Digital Entertainment (SBGAMES)

作者： Flavio Paulus Franzin Cesar Tadeu Pozzer Bruno Torres do Nascimento Universidade Federal de Santa Maria Santa Maria Brazil

The user's immersion is highly related to the visual aspects of virtual scenarios. Thus, it is imperative for such applications the rendering of ground vegetation with high fidelity. However, the representation of these elements requires a high storage and graphics processing, which are the bottleneck of the other approaches. We present a GPU-based solution for handling dense ground vegetation on large-scale scenarios. Our proposal includes an architecture to distribute, render, and, to deal collisions with dynamic objects. The plants are placed in the scenario based on positions computed in real-time. Collisions are encoded in vector fields, which store the deformations to be applied to the plants during the rendering. These collisions may result in temporary or permanent deformation on the plants. The rendering performance is optimized through LOD and Instancing techniques, and the memory cost is reduced compressing the vector fields. The results show that the proposed approach is capable of handling ground vegetation for large-scale scenarios, ensuring a pleasant visual with low processing and storage costs.

关键词： Rendering (computer graphics) Vegetation mapping Geometry Strain Real-time systems Visualization

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GPU Usage trends in Medical image processing

GPU Usage trends in Medical Image processing

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International symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Abdallah BENHAMIDA Miklos KOZLOVSZKY Sándor SZÉNÁSI BioTech Research Center EKIK Óbuda University Budapest Hungary John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (数字)9781728106861

ISBN: (纸本)9781728106878

graphics processing units (GPUs), mainly named as general-purpose computing on graphics processing units (GPGPUs) became a widely used technology for many computer-aided procedures in different research or industry areas that require high algorithmic computation capabilities for parallel computing tasks. In the field of bioinformatics and especially in medical image processing, GPUs play an essential role to enable high performance and computation capabilities in order to facilitate the tasks related to image processing. This paper highlights the usage of the graphics processing units in medical image processing and three-dimensional medical image processing related applications (computer-aided image processing, volume rendering, and segmentation), such as image segmentation, noise reduction or denoising, and image registration. It gives a deep understanding of the influence of GPU cards on general purpose programming GPGPU in general and on medical image processing specifically. It presents a good starting point for further applications of GPU programming in the field of biomedical applications and mainly for medical image processing that requires high-performance capabilities for medical image treatment shortly after the medical imaging activity such as in the ICU or the emergency services.

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Dynamic Sign Language Recognition Based on Convolutional Neural Networks and Texture Maps

Dynamic Sign Language Recognition Based on Convolutional Neu...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Edwin Escobedo Lourdes Ramirez Guillermo Camara Department of Computer Science Federal University of Ouro Preto Ouro Preto MG Brazil Department of Computer Science National University of Trujillo Trujillo Peru

Sign language recognition (SLR) is a very challenging task due to the complexity of learning or developing descriptors to represent its primary parameters (location, movement, and hand configuration). In this paper, we propose a robust deep learning based method for sign language recognition. Our approach represents multimodal information (RGB-D) through texture maps to describe the hand location and movement. Moreover, we introduce an intuitive method to extract a representative frame that describes the hand shape. Next, we use this information as inputs to two three-stream and two-stream CNN models to learn robust features capable of recognizing a dynamic sign. We conduct our experiments on two sign language datasets, and the comparison with state-of-the-art SLR methods reveal the superiority of our approach which optimally combines texture maps and hand shape for SLR tasks.

关键词： Assistive technology Gesture recognition Skeleton Feature extraction image color analysis Shape image segmentation

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On Modeling Context from Objects with a Long Short-Term Memory for Indoor Scene Recognition

On Modeling Context from Objects with a Long Short-Term Memo...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Camila Laranjeira Anisio Lacerda Erickson R. Nascimento Universidade Federal de Minas Gerais (UFMG) Brazil

Recognizing indoor scenes is still regarded an open challenge on the computer Vision field. Indoor scenes can be well represented by their composing objects, which can vary in angle, appearance, besides often being partially occluded. Even though Convolutional Neural Networks are remarkable for image-related problems, the top performances on indoor scenes are from approaches modeling the intricate relationship of objects. Knowing that Recurrent Neural Networks were designed to model structure from a given sequence, we propose representing an image as a sequence of object-level information in order to feed a bidirectional Long Short-Term Memory network trained for scene classification. We perform a Many-to-Many training approach, such that each element outputs a scene prediction, allowing us to use each prediction to boost recognition. Our method outperforms RNN-based approaches on MIT67, an entirely indoor dataset, while also improved over the most successful methods through an ensemble of classifiers.

关键词： Feature extraction Training Proposals Search problems image recognition Recurrent neural networks Semantics

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A Novel Approach on Segmentation of AgNOR-Stained Cytology images Using Deep Learning

A Novel Approach on Segmentation of AgNOR-Stained Cytology I...

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Annual IEEE symposium on computer-Based Medical Systems

作者： João Gustavo Atkinson Amorim Luiz Antonio Buschetto Macarini André Victória Matias Allan Cerentini Fabiana Botelho De Miranda Onofre Alexandre Sherlley Casimiro Onofre Aldo Von Wangenheim Federal University of Santa Catarina Florianopolis Brazil Automation and Systems Department Federal University of Santa Catarina Florianópolis Brazil Department of Informatics and Statistics Federal University of Santa Catarina Florianópolis Brazil Brazilian Institute for Digital Convergence - INCoD Federal University of Santa Catarina Florianópolis Brazil

ISBN: (数字)9781728194295

ISBN: (纸本)9781728194301

Cervical cancer is the second most common cancer type in women. This is a deadly disease that could benefit from early detection methods. Cytology is a possible, noninvasive, alternative to biopsy for diagnosis of malignant lesions, where information about the cells can be obtained. This process consists of staining the slides being able to visualize the nuclei of the cells and then be properly diagnosed. In this work, silver-stained slides, a method known as Argyrophilic Nucleolar Organizer Regions (AgNOR), is employed. This method shows great potential for the diagnosis of the malignancy of lesions. However, it has not yet been much explored, mainly using computational methods. In this work, we propose the use of Convolutional Neural Networks on silver stained images to measure AgNOR quantitative image information. We will present the entire process, starting from pre-processing, passing through neural network training and results evaluation. The best results we obtained were 0.87 of Mean Intersection Over Union (mIoU) and 0.99 of Dice Similarity Coefficient (DSC) using an U-Net model with a ResNet18 as its backbone. The code and the dataset employed in this work are publicly available.

关键词： Cancer Lesions Training image segmentation Machine learning Satellites Software

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