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检索条件"任意字段=15th Brazilian Symposium on Computer Graphics and Image Processing"

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Demonstration Abstract: Accelerating Embedded Deep Learning Using DeepX

Demonstration Abstract: Accelerating Embedded Deep Learning ...

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International symposium on Information processing in Sensor Networks (IPSN)

作者： Nicholas D. Lane Sourav Bhattacharya Petko Georgiev Claudio Forlivesi Fahim Kawsar Bell Labs University of Cambridge

Deep learning has revolutionized the way sensor measurements are interpreted and application of deep learning has seen a great leap in inference accuracies in a number of fields. However, the significant requirement for memory and computational power has hindered the wide scale adoption of these novel computational techniques on resource constrained wearable and mobile platforms. In this demonstration we present DeepX, a software accelerator for efficiently running deep neural networks and convolutional neural networks on resource constrained embedded platforms, e.g., Nvidia Tegra K1 and Qualcomm Snapdragon 400.

关键词： Machine learning Computational modeling Mobile communication image recognition Neural networks computer architecture Prototypes

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Remote Sensing image Segmentation and Representation through Multiscale Analysis

Remote Sensing Image Segmentation and Representation through...

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Tutorials of the brazilian symposium on computer graphics and image processing (SIBGRAPI TUTORIALS)

作者： Jefersson Alex Dos Santos Ricardo Da Silva Torres Universidade Federal de Minas Gerais Belo Horizonte MG BR Inst. of Comput. Universidade Estadual de Campinas Campinas SP BR

Every year, new sensor technologies are being implemented to improve the acquisition of high-resolution remote sensing images (RSIs). With the large amount of data provided by these sensors, novel computational approaches are constantly required to support the decision-making process based on RSI analysis. A typical problem is the recognition of target regions for land-cover mapping. In this context, the main problems are: (1) classification methods are dependent on the segmentation quality; and (2) the selection of representative samples for training is a costly process. the samples indicated by the user are not always enough to define the best segmentation scale. Furthermore, the indication of samples can be expensive, since it often requires to visit studied places in loco. the segmentation-dependence problem has been addressed in the literature by using multiscale analysis. the training sample selection problem is, in turn, addressed mainly by employing user interaction techniques which are usually combined with pixel-based classification approaches. this work aims to introduce problems, challenges, and some state-of-the-art approaches for multiscale classification of remote sensing image. the main covered topics are arranged into four sessions: research challenges, segmentation, feature extraction, and classification.

关键词： image segmentation Feature extraction Training Remote sensing Visualization Histograms Redundancy

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Simple and Efficient Approximate Nearest Neighbor Search Using Spatial Sorting

Simple and Efficient Approximate Nearest Neighbor Search Usi...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Marcelo de Gomensoro Malheiros Marcelo Walter Center for Exact and Technological Sciences UNIVATES Lajeado Brazil Institute of Informatics UFRGS Porto Alegre Brazil

Finding the nearest neighbors of a point is a highly used operation in many graphics applications. Recently, the neighborhood grid has been proposed as a new approach for this task, focused on low-dimensional spaces. In 2D, for instance, we would organize a set of points in a matrix in such a way that their x and y coordinates are at the same time sorted along rows and columns, respectively. then, the problem of finding closest points reduces to only examining the nearby elements around a given element in the matrix. Based on this idea, we propose and evaluate novel spatial sorting strategies for the bidimensional case, providing significant performance and precision gains over previous works. We also experimentally analyze different scenarios, to establish the robustness of searching for nearest neighbors. the experiments show that for many dense point distributions, by using some of the devised algorithms, spatial sorting beats more complex and current techniques, like k-d trees and index sorting. Our main contribution is to show that spatial sorting, albeit a still scarcely researched topic, can be turned into a competitive approximate technique for the low-dimensional k-NN problem, still being simple to implement, memory efficient, robust on common cases, and highly parallelizable.

关键词： Sorting Arrays graphics processing units Standards Time complexity

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Advanced green energy system-on-chip design for portable diffusion optical tomography

Advanced green energy system-on-chip design for portable dif...

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IEEE International symposium on Consumer Electronics (ISCE)

作者： Shih Kang Shih-Yang Wu Wai-Chi Fang Department of Electronics Engineering and Institute of Electronics National Chiao Tung University Hsinchu Taiwan

In recent years, the rapid development of semiconductor manufacturing technology has made possible the large scale design and integration of many system level applications into a single chip. In this paper, we propose a portable computed tomography (CT) system to provide the general public easy access to a basic type of health check-up outside the hospital setting. the designed system employs diffuse optical tomography (DOT) technology, a technology which has found many successful applications in the field of biomedicine, such as breast cancer detection and observation of oxygenated hemoglobin distribution in the brain. In this paper, we develop a low-cost continuous wave near-infrared (CW-NIR) DOT system, paving the way for practical solutions in portable biomedical system-on-a-chip (SoC) applications. In addition, we demonstrate image reconstruction improvements associated with the two-dimensional (2D) post-processing technique employed in the proposed system.

关键词： image reconstruction US Department of Transportation System-on-a-chip Biomedical optical imaging computer architecture Optical imaging Absorption

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3D Face Reconstruction from Video Using 3D Morphable Model and Silhouette

3D Face Reconstruction from Video Using 3D Morphable Model a...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Christian Baumberger Mauricio Reyes Mihai Constantinescu Radu Olariu Edilson De Aguiar thiago Oliveira Santos University of Bern Switzerland Inselspital Switzerland Universidade Federal do Espirito Santo Vitoria ES BR

ISBN: (纸本)9781479942572

Capitalizing on the advances of 3D face reconstruction from computer graphics and computer vision, this work presents a system to enable precise and robust 3D facial reconstruction from video in order to improve current approaches for doctor-patient communication tools. the reconstructed face can be used for emulation of aesthetic procedures in 3D. the proposed 3D face reconstruction algorithm uses a statistical shape model as well as information from facial landmarks and silhouette to iteratively model the face of a subject moving the face from front to left. the presented 3D active shape model approach enables the spatio-temporal tracking of facial landmarks and pose estimation based on a few initial facial landmarks defined in the first frame. Silhouette information extracted from key frames allows for better face reconstruction. the proposed methods were rigorously evaluated with experiments using real and artificial data. Results showed that the proposed method can detect facial landmarks with an approximate median error of 5% intraocular distance, can estimate pose with a median error below 5 degrees, and that silhouette information can improve reconstruction accuracy in all facial regions, specially on the cheeks.

关键词： Shape Face three-dimensional displays Estimation Solid modeling image reconstruction Cameras

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Comparison of Face Classification with Single and Multi-model base on CNN

Comparison of Face Classification with Single and Multi-mode...

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International Joint symposium on Artificial Intelligence and Natural Language processing (iSAI-NLP)

作者： Sarin Watcharabutsarakham Supphachoke Suntiwichaya Chanchai Junlouchai Apichon Kitvimorat Image Processing and Understanding Research Team National Electronics and Computer Technology Center (NECTEC) Pathum Thani Thailand Leveraging Technology Solutions Section National Electronics and Computer Technology Center (NECTEC) Pathum Thani Thailand

ISBN: (数字)9781665415545

ISBN: (纸本)9781665446969

Since the coronavirus disease 2019 (COVID-19) outbreak has spread across the country, our research applies to remind the people to wear a face mask when we go outside because a facial image detection and classification method will be used to authentication and authorization. this paper has shown that our created models based on CNN can detect the face mask-wearing, glasses-wearing, and gender with comparison two models. We training model with mix public datasets such as WIDER FACE, AFW, and MAFA. Moreover, we use VGG-Face to pre-train the model for the advance detection rate.

关键词： COVID-19 Deep learning Training Face recognition Computational modeling Faces Tuning

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Finger Spelling Recognition from Depth Data Using Direction Cosines and Histogram of Cumulative Magnitudes

Finger Spelling Recognition from Depth Data Using Direction ...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Edwin Escobedo Guillermo Camara Federal University of Ouro Preto Ouro Preto MG BR

In this paper, we propose a new approach for finger spelling recognition using depth information captured by Kinect sensor. We only use depth information to characterize hand configurations corresponding to alphabet letters. First, we use depth data to generate a binary hand mask which is used to segment the hand area from background. then, the major hand axis is determined and aligned with Y axis in order to achieve rotation invariance. Later, we convert the depth data in a 3D point cloud. the point cloud is divided into sub regions and in each one, using direction cosines, we calculated three histograms of cumulative magnitudes Hx, Hy and Hz corresponding to each axis. Finally, these histograms were concatenated and used as input to our Support Vector Machine (SVM) classifier. the performance of this approach is quantitatively and qualitatively evaluated on a dataset of real images of American Sign Language (ASL) hand shapes. the dataset used is composed of 60000 depth images. According to our experiments, our approach has an accuracy rate of 99.37%, outperforming other state-of-the-art methods.

关键词： Histograms Assistive technology three-dimensional displays Gesture recognition Support vector machines Accuracy image segmentation

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Real-time extraction of local phase features from volumetric medical image data

Real-time extraction of local phase features from volumetric...

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IEEE International symposium on Biomedical Imaging

作者： Alborz Amir-khalili Antony J. Hodgson Rafeef Abugharbieh Department of Electrical and Computer Engineering Biomedical Signal and Image Computing Laboratory USA Department of Mechanical Engineering University of British Columbia Canada

ISBN: (纸本)9781467364560

We present a novel real-time implementation of local phase feature extraction from volumetric image data based on 3D directional (log-Gabor) filters. We achieve drastic performance gains without compromising the signal-to-noise ratio by pre-computing the filters and adaptive noise estimation parameters, and streamlining the remainder of the computations to efficiently run on a multi-processor graphic processing unit (GPU). We validate our method on clinical ultrasound data and demonstrate a 15-fold speedup in computation time over state-of-the art methods, which could potentially facilitate a wide range of practical applications for real-time image-guided procedures.

关键词： graphics processing units Feature extraction Noise Real-time systems Ultrasonic imaging MATLAB Bones

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Semi-supervised Learning for Relevance Feedback on image Retrieval Tasks

Semi-supervised Learning for Relevance Feedback on Image Ret...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Daniel Carlos Guimarães Pedronette Rodrigo T. Calumby Ricardo da S. Torres State Univ. of Sao Paulo (UNESP) Brazil Recod Lab - Institute of Computing University of Campinas (UNICAMP) Campinas SP Brazil University of Feira de Santana (UEFS) Feira de Santana BA Brazil Recod Lab. - Inst. of Comput. Universidade Estadual de Campinas Campinas SP BR

ISBN: (纸本)9781479942572

Relevance feedback approaches have been established as an important tool for interactive search, enabling users to express their needs. However, in view of the growth of multimedia collections available, the user efforts required by these methods tend to increase as well, demanding approaches for reducing the need of user interactions. In this context, this paper proposes a semi-supervised learning algorithm for relevance feedback to be used in image retrieval tasks. the proposed semi-supervised algorithm aims at using both supervised and unsupervised approaches simultaneously. While a supervised step is performed using the information collected from the user feedback, an unsupervised step exploits the intrinsic dataset structure, which is represented in terms of ranked lists of images. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors and different datasets. the proposed approach was also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of the proposed approach.

关键词： image retrieval Shape image color analysis Visualization Semisupervised learning Educational institutions Feature extraction

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Improved Head-Shoulder Human Contour Estimation through Clusters of Learned Shape Models

Improved Head-Shoulder Human Contour Estimation through Clus...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Julio Cezar Silveira Jacques Soraia Raupp Musse Pontificia Universidade Catolica do Rio Grande do Sul Porto Alegre RS BR PUCRS Faculdade de Informática Porto Alegre Brazil

In this paper we propose a clustering-based learning approach to improve an existing model for human head-shoulder contour estimation. the contour estimation is guided by a learned head-shoulder shape model, initialized automatically by a face detector. A dataset with labeled data is used to create the head-shoulder shape model and to quantitatively analyze the results. In the proposed approach, geometric features are firstly extracted from the learning dataset. then, the number of shape models to be learned is obtained by an unsupervised clustering algorithm. In the segmentation stage, different graphs with an omega-like shape are built around the detected face, related to each learned shape model. A path with maximal cost, related to each graph, defines a initial estimative of the head-shoulder contour. the final estimation is given by the path with maximum average energy. Experimental results indicate that the proposed technique outperformed the original model, which is based on a single shape model, learned in a more simple way. In addition, it achieved comparable accuracy to other state-of-the-art models.

关键词： Shape Feature extraction Computational modeling Face Estimation Neck image segmentation

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