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

共 3049 条记录，以下是161-170 订阅

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Autoencoder Satellite image Matching for UAV Geolocation in Long-Range High-Altitude Missions

Autoencoder Satellite Image Matching for UAV Geolocation in ...

引用

brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Lucas B. V. Cordova Stephanie L. Brião Felipe G. Oliveira Rodrigo S. Guerra Paulo L. J. Drews Centro de Ciências Computacionais - C3. Universidade Federal do Rio Grande - FURG Rio Grande RS Brazil Instituto de Ciências Exatas e Tecnologia - ICET. Universidade Federal do Amazonas - UFAM Itacoatiara AM Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Vision-based geolocation is a promising way to overcome the vulnerabilities of Global Navigation Satellite System (GNSS) methods, which are subject to signal degradation, intentional interference, and environmental obstacles. This paper presents a novel approach to Unmanned Aerial Vehicle (UAV) geolocation in long-range and high-altitude missions using satellite imagery. Our method is based on the matching of encoded vector representations in embedded space, demonstrating robust performance to changes in vegetation and landscape. The neural network is used to encode satellite images of a reference map into embedding representations. image matching is performed in this embedded space using cross-correlation. We evaluated the accuracy and processing time of the proposed model by querying images along a 200 km northbound path at high altitude, covering an area larger than twenty thousand square kilometers. We also evaluated the network's generalization capability on an unknown map. Reference and query images are sourced from satellite images captured at different acquisition times to evaluate robustness due to appearance variations. The results demonstrate that the method can achieve up to 96.83% accuracy on a known map, while experiments on an unknown map averaged 90% accuracy. The processing time to match encoded images is 0.05 ms. These findings suggest the feasibility of integrating the method into more complex vision-based geolocation systems.

关键词： Training Global navigation satellite system Accuracy Geology Neural networks Vegetation mapping Autonomous aerial vehicles Robustness Vectors Satellite images

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Toward Enhancing Vehicle Color Recognition in Adverse Conditions: A Dataset and Benchmark

Toward Enhancing Vehicle Color Recognition in Adverse Condit...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Gabriel E. Lima Rayson Laroca Eduardo Santos Eduil Nascimento David Menotti Department of Informatics Federal University of Paraná Curitiba Brazil Postgraduate Program in Informatics Pontifical Catholic University of Paraná Curitiba Brazil Department of Technological Development and Quality Paraná Military Police Curitiba Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Vehicle information recognition is crucial in various practical domains, particularly in criminal investigations. Vehicle Color Recognition (VCR) has garnered significant research interest because color is a visually distinguishable attribute of vehicles and is less affected by partial occlusion and changes in viewpoint. Despite the success of existing methods for this task, the relatively low complexity of the datasets used in the literature has been largely overlooked. This research addresses this gap by compiling a new dataset representing a more challenging VCR scenario. The images - sourced from six license plate recognition datasets - are categorized into eleven colors, and their annotations were validated using official vehicle registration information. We evaluate the performance of four deep learning models on a widely adopted dataset and our proposed dataset to establish a benchmark. The results demonstrate that our dataset poses greater difficulty for the tested models and highlights scenarios that require further exploration in VCR. Remarkably, nighttime scenes account for a significant portion of the errors made by the best-performing model. This research provides a foundation for future studies on VCR, while also offering valuable insights for the field of fine-grained vehicle classification.

关键词： Deep learning image recognition image color analysis Lighting Benchmark testing Multitasking Pattern recognition Reliability Video recording License plate recognition

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Space-Time Urban Explorer: A Visual Tool for Exploring Spatiotemporal Crime and Patrolling Data

Space-Time Urban Explorer: A Visual Tool for Exploring Spati...

引用

brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Tiago Paulino Santos João Matheus Siqueira Souza Thales Vieira Luis Gustavo Nonato Institute of Computing Federal University of Alagoas (UFAL) Maceio Brazil Institute of Mathematics and Computer Sciences University of Sao Paulo (ICMC-USP) Sao Carlos Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Spatiotemporal urban data gathered by public security departments holds immense potential for in-depth analysis, enhancing decision-making in areas such as crime prevention and patrolling strategies. However, extracting meaningful patterns from vast and complex spatiotemporal datasets presents a considerable challenge in the field of big data analytics. We introduce a novel visualization-assisted tool tailored for handling massive spatiotemporal urban datasets, with a specific focus on public security data. At the core of this tool are spatial graph vertex ordering algorithms that perform dimensionality reduction on the vertices' locations. To make this tool practical for handling massive spatiotemporal datasets, we present efficient preprocessing techniques. These techniques are carefully crafted to distill and represent urban spatiotemporal datasets, ensuring efficient data exploration. The effectiveness of the proposed solution is validated through case studies using real datasets from the Military Police of the State of Alagoas - Brazil. We demonstrate the effectiveness of the proposed solutions, showcasing the versatility of the visual tool in accomplishing various relevant analytical tasks.

关键词： Dimensionality reduction Law enforcement Visual analytics Prevention and mitigation Decision making Data visualization Public security Spatiotemporal phenomena Data mining Socioeconomics

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Superpixel Segmentation: From Theory to Applications*

Superpixel Segmentation: From Theory to Applications*

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Isabela Borlido Felipe Belém Leonardo Melo Taylla M. Theodoro Ilan F. Silva Zenilton K. G. do Patrocínio Alexandre X. Falcão Silvio Jamil F. Guimarães Computer Science Department (DCC) Image and Multimedia Data Science Laboratory (IMScience) Pontifical Catholic University of Minas Gerais (PUC Minas) Belo Horizonte Brazil Laboratory of Image Data Science (LIDS) Institute of Computing (IC) University of Campinas (UNICAMP) Campinas Brazil

Superpixel segmentation consists of partitioning images into regions composed of similar and connected pixels. Its methods have been widely used in many computer vision applications since it allows for reducing the workload, removing redundant information, and preserving regions with meaningful features. Due to the rapid progress in this area, the literature fails to catch up on more recent works among the compared ones and to categorize the methods according to all existing strategies. We revisit the recent and popular literature according to our taxonomy and evaluate 20 strategies based on nine criteria: connectivity, compactness, delineation, control over the number of superpixels, color homogeneity, robustness, running time, stability, and visual quality. Thus, this document presents a proposal for a tutorial to be presented at SIBGRAPI 2023.

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ESNet: An Efficient Real-time Semantic Segmentation Network

ESNet: An Efficient Real-time Semantic Segmentation Network

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International symposium on Parallel and Distributed processing with Applications, ISPA

作者： Renping Xie Cong He Ming Tao Kai Ding School of Computer Science and Technology Dongguan University of Technology Dongguan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Efficient image segmentation algorithms are critical in computer vision, as they maintain high processing speeds while handling large amounts of data and providing practical solutions in resource-limited environments. While existing classical segmentation methods achieve good results, their real-time performance can be further improved. To address this issue, we propose an efficient segmentation network (ESNet) to capture extensive contextual information and improve segmentation fineness, which are two main issues in semantic segmentation, while ensuring real-time capability. Then, we first introduce an efficient context module (ECM) that effectively enlarges the effective receptive field (ERF) of the model and improves its performance in integrating contextual information. Subsequently, a skip connection with a simple feature fusion module (SFFM) is designed to provide rich detail information, thereby improving segmentation fineness. The efficacy of ESNet was evaluated on the PASCAL VOC2012 semantic segmentation dataset against several state-of-the-art methods. ESNet achieves 71.4 FPS at a resolution of 1024×2048 on a single NVIDIA GeForce RTX 3090 GPU and achieves 62.42% mIoU at a resolution of 320×480 with an extremely simple training recipe, striking a fine balance between segmentation accuracy and inference speed.

关键词： Training Distributed processing computer vision Accuracy Semantic segmentation Computational modeling graphics processing units Rendering (computer graphics) Real-time systems Context modeling

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A Conservative Sharpening Filter for Interpolated images 33

A Conservative Sharpening Filter for Interpolated Images

引用

33rd SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Emmendorfer, Leonardo R. Fed Univ Rio Grande Ctr Computat Sci BR-96201900 Rio Grande RS Brazil

ISBN: (纸本)9781728192741

Interpolation methods are computationally efficient and rather effective for magnification from low-resolution images in order to generate higher resolution images. However, blur and other undesirable visual artifacts are often produced during the process. This work evaluates a sharpening filter that enhances the quality of interpolated images by preserving information from the original low-resolution images. The novel filter was shown to achieve statistically significant increments in both the PSNR and SSIM of magnified RGB images using two image sets from the literature.

关键词： image enhancement

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SpikeMotion: A Transformer Framework for High - Throughput Video Segmentation on FPGA

SpikeMotion: A Transformer Framework for High - Throughput V...

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Midwest symposium on Circuits and Systems (MWSCAS)

作者： Uchechukwu Leo Udeji Martin Margala Electrical and Computer Engineering department University of Massachusetts Lowell Lowell USA Computing and Informatics department University of Louisiana at Lafayette Lafayette USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

Transformers have emerged at the forefront in the training and inference of diverse machine learning tasks, encompassing video processing, image generation and classification, and natural language processing (NLP). Despite their increasing prevalence, a comprehensive framework to efficiently implement them has been lacking. This study introduces a transformer-based framework which accelerates image processing of UNETR (U-shaped neural network transformer) model for video segmentation task using the cityscapes dataset. Given the large size of images in the dataset we incorporate hyperattention and mixed precision in our design. Our model is trained on Google A1OO GPU accelerator and profiled. Finally, our design is implemented on FPGA to take advantage of the reconfigurable and high-throughput characteristics of system-on-chips (SoC) for image processing. Our results indicate improvements compared to existing research in this domain.

关键词： Training image segmentation Neural networks Memory management graphics processing units Machine learning Transformers

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Distributed Intelligent Video Surveillance for Early Armed Robbery Detection Based on Deep Learning

Distributed Intelligent Video Surveillance for Early Armed R...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Sergio Fernandez-Testa Edwin Salcedo Centro de Investigación Desarrollo e Innovación en Ingeniería Mecatrónica Universidad Católica Boliviana “San Pablo” La Paz Bolivia

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Low employment rates in Latin America have contributed to a substantial rise in crime, prompting the emergence of new criminal tactics. For instance, “express robbery” has become a common crime committed by armed thieves, in which they drive motorcycles and assault people in public in a matter of seconds. Recent research has approached the problem by embedding weapon detectors in surveillance cameras; however, these systems are prone to false positives if no counterpart confirms the event. In light of this, we present a distributed IoT system that integrates a computer vision pipeline and object detection capabilities into multiple end-devices, constantly monitoring for the presence of firearms and sharp weapons. Once a weapon is detected, the end-device sends a series of frames to a cloud server that implements a 3DCNN to classify the scene as either a robbery or a normal situation, thus minimizing false positives. The deep learning process to train and deploy weapon detection models uses a custom dataset with 16,799 images of firearms and sharp weapons. The best-performing model, YOLOv5s, optimized using TensorRT, achieved a final mAP of 0.87 running at 4.43 FPS. Additionally, the 3DCNN demonstrated 0.88 accuracy in detecting abnormal situations. Extensive experiments validate that the proposed system significantly reduces false positives while autonomously monitoring multiple locations in real-time.

关键词： YOLO Deep learning Training computer vision Accuracy Tensors Weapons Cameras Video surveillance Servers

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Detection of Elderly Falls in Video Streams Using Skeleton Key Points and Transformer Networks

Detection of Elderly Falls in Video Streams Using Skeleton K...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Matteo Reis Yunevda E. León Rojas Bernardo B. Gatto Juan G. Colonna Universidade Federal do Amazonas MTI Ltd

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Falls among elderly individuals can lead to significant physical injuries, increased medical costs, and a diminished quality of life. In aging societies, particularly in countries such as Japan, Germany, and Italy, the need for effective fall detection systems is critical. Detecting falls automatically using computer vision techniques presents a notable challenge in the healthcare domain. This study proposes the development of a fall detection system utilizing a Transformer layer with self-attention mechanisms. Our algorithm processes a continuous stream of video frames and classifies whether the accumulated sequence of 44 frames from the video is a fall or not. We employ the MediaPipe Pose framework to extract skeleton key points for person detection. The network is then trained to learn the sequence of movements from these skeleton key points, enabling it to distinguish various types of movements and identify the temporal patterns associated with falls. To further enhance the efficiency and compactness of the model, we incorporate Fnet and MLP-Mixer architectures, which reduce the computational complexity while maintaining high performance. Our proposed model was trained on the NTU RGB+D dataset and demonstrated the capability to differentiate falls from everyday actions with 99.4% accuracy with 1,863 parameters. We also created a baseline by training the state-of-the-art model on the same dataset as ours, achieving an accuracy of 96.3% with 1,714,177 parameters.

关键词： computer vision Accuracy Computational modeling Neural networks Streaming media Transformers Skeleton Older adults Fall detection Computational complexity

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Graph Feature Embeddings for Patient Re-Identification from Chest X-Ray images

Graph Feature Embeddings for Patient Re-Identification from ...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： João Renato Ribeiro Manesco Danilo Jodas Maurício José Grapíggia Zanella Marcel Koenigkam Santos João Paulo Papa School of Sciences São Paulo State University (UNESP) Bauru SP Brazil Bauru Medical School University of São Paulo (USP) Bauru SP Brazil

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Patient re-identification in medical imaging facilitates longitudinal studies, monitors treatment, and ensures patient privacy. Accurate patient re-identification enables clinicians to track patient progress, compare new imaging results with historical data, and ensure that the correct treatment plans are followed without compromising patient confidentiality. However, identifying similar patients presents significant challenges when dealing with low-quality images, like chest X-ray images, especially when the presence of medical equipment obscures key anatomical features. This paper introduces a Graph Matching Network-based approach for patient re-identification using chest X-ray data. By representing such images as graphs, where nodes correspond to key anatomical landmarks and edges represent spatial relationships, the Graph Matching Network can effectively model the complex dependencies within the images. In addition, we integrate superpixel as a representative feature extraction approach in a robust strategy to describe the images as a graph model. Our method is evaluated on a large-scale dataset of chest X-ray images, demonstrating its superior performance compared to other methods. Experimental results show that our approach improves the precision of patient matching by integrating a novel loss function based on the cosine distance of the graph embedding representation, enhancing its robustness against common challenges such as variations in image quality, patient posture, and imaging equipment.

关键词： image quality graphics Privacy image edge detection Feature extraction Robustness Biomedical monitoring X-ray imaging Monitoring Biomedical imaging

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