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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211-220 订阅

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Autoencoder-based Super-Resolution Approach for Aerial Robot Navigation

Autoencoder-based Super-Resolution Approach for Aerial Robot...

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2024 brazilian symposium on Robotics (SBR), and 2024 Workshop on Robotics in Education (WRE)

作者： Jhonathan A. Oliveira Paulo L. J. Drews Felipe G. Oliveira Institute of Computing (ICOMP) Univ. Federal do Amazonas (UFAM) Manaus Brazil Computer Sciences Center (C3) Univ. Federal do Rio Grande (FURG) Rio Grande Brazil Inst. of Exact Sciences and Tech. (ICET) Univ. Federal do Amazonas (UFAM) Itacoatiara Brazil

ISBN: (数字)9798350391084

ISBN: (纸本)9798350391091

images are increasingly used in the navigation of autonomous aerial vehicles. However, processing images locally in aerial robots can be very computationally and energy-intensive. In this sense, it is increasingly common to use remote data processing for applications involving mobile robots. This paper addresses the problem of super-resolution as a strategy for remote processing of aerial images, considering the proposed algorithm as a technique that reconstructs high-resolution images from low-resolution images captured by the aerial vehicle. To this end, we propose an approach called AESR, based on Autoencoder architecture for generating high-definition images, integrating skip-connections and self-attention modules to improve the learning process. The results indicate the high performance of our approach, validating the learning process based on autoencoder. Moreover, the approach achieved high accuracy even in challenging images representing different environments. The experiments demonstrate the robustness and accuracy of our approach, overcoming state-of-the-art techniques, through the use of the quality metrics PSNR, SSIM, LPIPS, DISTS, NIQE, and BRISQUE.

关键词： Measurement Accuracy Navigation Superresolution Autoencoders Education Autonomous aerial vehicles Robustness Mobile robots image reconstruction

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Tightening Classification Boundaries in Open Set Domain Adaptation through Unknown Exploitation

Tightening Classification Boundaries in Open Set Domain Adap...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Lucas Fernando Alvarenga e Silva Nicu Sebe Jurandy Almeida Institute of Computing University of Campinas (UNICAMP) Brazil Department of Information Engineering and Computer Science University of Trento (UniTN) Italy Department of Computing Federal University of são Carlos (UFSCar) Brazil

Convolutional Neural Networks (CNNs) have brought revolutionary advances to many research areas due to their capacity of learning from raw data. However, when those methods are applied to non-controllable environments, many different factors can degrade the model's expected performance, such as unlabeled datasets with different levels of domain shift and category shift. Particularly, when both issues occur at the same time, we tackle this challenging setup as Open Set Domain Adaptation (OSDA) problem. In general, existing OSDA approaches focus their efforts only on aligning known classes or, if they already extract possible negative instances, use them as a new category learned with supervision during the course of training. We propose a novel way to improve OSDA approaches by extracting a high-confidence set of unknown instances and using it as a hard constraint to tighten the classification boundaries of OSDA methods. Especially, we adopt a new loss constraint evaluated in three different means, (1) directly with the pristine negative instances; (2) with randomly transformed negatives using data augmentation techniques; and (3) with synthetically generated negatives containing adversarial features. We assessed all approaches in an extensive set of experiments based on OVANet, where we could observe consistent improvements for two public benchmarks, the Office-31 and Office-Home datasets, yielding absolute gains of up to 1.3% for both Accuracy and H-Score on Office-31 and 5.8% for Accuracy and 4.7% for H-Score on Office-Home.

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Analysis and Mitigation of Soft-errors in GPU-accelerated Hyperspectral image Classifiers

Analysis and Mitigation of Soft-errors in GPU-accelerated Hy...

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IEEE Design and Diagnostics of Electronic Circuits and Systems (DDECS)

作者： Sergiu-Mohamed Abed Juan-David Guerrero-Balaguera Josie E. Rodriguez Condia Gianluca De Lucia Marco Lapegna Matteo Sonza Reorda Department of Control and Computer Engineering (DAUIN) Politecnico di Torino CASD Scuola Superiore Universitaria Rome Italy Department of Mathematics and Applications University of Naples Federico II

ISBN: (数字)9798331528010

ISBN: (纸本)9798331528027

This work assesses the reliability of a hyperspectral image classifier for edge devices under transient faults by using a fine-grain strategy based on the Hardware Injection Through Program Transformation (HITPT) technique. The results identified the most vulnerable software parts and the corruption effects due to hardware faults (from 5.1% to 100.0% of accuracy drop). Then, the results supported the adoption of a selective-hardening software mechanism (based on the Duplication with Comparison strategy) to effectively mitigate the most critical effects under limited costs.

关键词： Costs graphics processing units Reliability engineering Hardware Software Software reliability Circuit faults Transient analysis Principal component analysis Hyperspectral imaging

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A Converting Autoencoder Toward Low-latency and Energy-efficient DNN Inference at the Edge

A Converting Autoencoder Toward Low-latency and Energy-effic...

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IEEE International symposium on Parallel and Distributed processing Workshops and Phd Forum (IPDPSW)

作者： Hasanul Mahmud Peng Kang Kevin Desai Palden Lama Sushil K. Prasad Department of Computer Science University of Texas at San Antonio San Antonio Texas

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Reducing inference time and energy usage while maintaining prediction accuracy has become a significant concern for deep neural networks (DNN) inference on resource-constrained edge devices. To address this problem, we propose a novel approach based on “converting” autoencoder and lightweight DNNs. This improves upon recent work such as early-exiting framework and DNN partitioning. Early-exiting frameworks spend different amounts of computation power for different input data depending upon their complexity. However, they can be inefficient in real-world scenarios that deal with many hard image samples. On the other hand, DNN partitioning algorithms that utilize the computation power of both the cloud and edge devices can be affected by network delays and intermittent connections between the cloud and the edge. We present CBNet, a low-latency and energy-efficient DNN inference framework tailored for edge devices. It utilizes a “converting” autoencoder to efficiently transform hard images into easy ones, which are subsequently processed by a lightweight DNN for inference. To the best of our knowledge, such autoencoder has not been proposed earlier. Our experimental results using three popular image-classification datasets on a Raspberry Pi 4, a Google Cloud instance, and an instance with Nvidia Tesla K80 GPU show that CBNet achieves up to 4.8× speedup in inference latency and 79% reduction in energy usage compared to competing techniques while maintaining similar or higher accuracy.

关键词： Distributed processing Accuracy image edge detection graphics processing units Transforms Energy efficiency Inference algorithms

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image Denoising using Attention-Residual Convolutional Neural Networks 33

Image Denoising using Attention-Residual Convolutional Neura...

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

作者： Pires, Rafael G. Santos, Daniel F. S. Santos, Claudio F. G. Santana, Marcos C. S. Papa, Joao P. Sao Paulo State Univ Dept Comp Bauru SP Brazil Univ Fed Sao Carlos Dept Comp Sao Carlos SP Brazil

ISBN: (数字)9781728192741

ISBN: (纸本)9781728192741

During the image acquisition process, noise is usually added to the data mainly due to physical limitations of the acquisition sensor, and also regarding imprecisions during the data transmission and manipulation. In that sense, the resultant image needs to be processed to attenuate its noise without losing details. Non-learning-based strategies such as filter-based and noise prior modeling have been adopted to solve the image denoising problem. Nowadays, learning-based denoising techniques showed to be much more effective and flexible approaches, such as Residual Convolutional Neural Networks. Here, we propose a new learning-based non-blind denoising technique named Attention Residual Convolutional Neural Network (ARCNN), and its extension to blind denoising named Flexible Attention Residual Convolutional Neural Network (FARCNN). The proposed methods try to learn the underlying noise expectation using an Attention-Residual mechanism. Experiments on public datasets corrupted by different levels of Gaussian and Poisson noise support the effectiveness of the proposed approaches against some state-of-the-art image denoising methods. ARCNN achieved an overall average PSNR results of around 0.44dB and 0.96dB for Gaussian and Poisson denoising, respectively FARCNN presented very consistent results, even with slightly worsen performance compared to ARCNN.

关键词： Noise reduction Training image denoising Convolutional neural networks Optimization Estimation Neural networks

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Edge Rendering Architecture for multiuser XR Experiences and E2E Performance Assessment

Edge Rendering Architecture for multiuser XR Experiences and...

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IEEE International symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Inhar Yeregui Daniel Mejías Guillermo Pacho Roberto Viola Jasone Astorga Mario Montagud Fundaci´on Vicomtech Basque Research and Technology Alliance San Sebastián Spain Department of Communications Engineering University of the Basque Country Bilbao Spain i2CAT Foundation and Universitat de València Barcelona Spain

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

Holographic communications are gaining ground among emerging eXtended-Reality (XR) applications due to their potential to revolutionize human communication. However, these technologies are characterized by higher requirements in terms of Quality of Service (QoS), such as high transmission data rates, very low latency, and high computation capacity, challenging current achievable capabilities. In this context, computation offloading techniques are being investigated, where resource-intensive computational tasks, like rendering XR experiences, are shifted from user devices to a separate processor, specifically an Edge Computing instance. This paper introduces an Edge Rendering architecture for multiuser XR experiences, implements it on top of widely employed XR and Web technologies, and proposes a method based on image and audio processing to evaluate its performance in terms of end-to-end media streaming latency, inter-device, and intra-media synchronization when employing different access networks.

关键词： Performance evaluation Extended reality image edge detection Quality of service computer architecture Streaming media Rendering (computer graphics)

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A comparison of graph-based semi-supervised learning for data augmentation 33

A comparison of graph-based semi-supervised learning for dat...

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

作者： de Oliveira, Willian Dihanster G. Penatti, Otavio A. B. Berton, Lilian Univ Fed Sao Paulo Inst Sci & Technol Sao Jose Dos Campos SP Brazil Samsung R&D Inst Campinas SP Brazil

ISBN: (纸本)9781728192741

In supervised learning, the algorithm accuracy usually improves with the size of the labeled dataset used for training the classifier. However, in many real-life scenarios, obtaining enough labeled data is costly or even not possible. In many circumstances, Data Augmentation (DA) techniques are usually employed, generating more labeled data for training machine learning algorithms. The common DA techniques are applied to already labeled data, generating simple variations of this data. For example, for image classification, image samples are rotated, cropped, flipped or other operators to generate variations of input image samples, and keeping their original labels. Other options are using Neural Networks algorithms that create new synthetic data or to employ Semi-supervised Learning (SSL) that label existing unlabeled data. In this paper, we perform a comparison among graph-based semi-supervised learning (GSSL) algorithms to augment the labeled dataset. The main advantage of using GSSL is that we can increase the training set by adding nonannotated images to the training set, therefore, we can benefit from the huge amount of unlabeled data available. Experiments are performed on five datasets for recognition of handwritten digits and letters (MNIST and EMINIST), animals (Dogs vs Cats), clothes (MNIST-Fashion) and remote sensing images (brazilian Coffee Scenes), in which we compare different possibilities for DA, including the GSSL, Generative Adversarial Networks (GANs) and traditional image Transformations (IT) applied on input labeled data. We also evaluated the impact of such techniques on different convolutional neural networks (CNN). Results indicate that, although all DA techniques performed well, GSSL was more robust to different image properties, presenting less accuracy variation across datasets.

关键词： data augmentation GANs image classification image transformation machine learning semi supervised learning

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Deep Learning-Based COVID-19 Diagnostics of Low-Quality CT images 14th

Deep Learning-Based COVID-19 Diagnostics of Low-Quality CT I...

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14th brazilian symposium on Bioinformatics (BSB)

作者： Ferber, Daniel Vieira, Felipe Dalben, Joao Ferraz, Mariana Sato, Nicholas Oliveira, Gabriel Padilha, Rafael Dias, Zanoni Univ Estadual Campinas Inst Comp Campinas SP Brazil

ISBN: (纸本)9783030918132;9783030918149

Mass testing of the population is among the most effective measures to combat the COVID-19 pandemic. Among existing diagnostic methods, deep learning-based solutions have the potential to be affordable, quick and accurate. However, these techniques often rely on highquality datasets, which are not always available in medical scenarios. In this work, we use convolutional neural networks to diagnose COVID19 on computed tomography images from the COVIDx-CT dataset [6]. The available scans often present noisy artifacts, originated from sensorand capturing-related errors, that can negatively impact the performance of the model if left untreated. In this sense, we explore several preprocessing strategies to reduce their impact and obtain a more accurate method. Our best model, a ResNet50 fine-tuned with preprocessed images, obtained 97.84% accuracy when prompted with a single image and 99.50% when processing multiple images from the same patient. In addition to achieving high accuracy, interpretability experiments show that the network correctly learned features from the lung and chest area.

关键词： COVID-19 diagnostic CT image analysis Deep Learning

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BoxPolypSAM: Leveraging SAM in Box-Supervised Polyp Segmentation

BoxPolypSAM: Leveraging SAM in Box-Supervised Polyp Segmenta...

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IEEE International symposium on Biomedical Imaging

作者： Yinqi Cai De-jun Fan Feng Gao Mingzhi Mao Weijun Sun Guanbin Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou China The Sixth Affiliated Hospital Sun Yat-sen University Guangzhou China School of Software Engineering Sun Yat-sen University Zhuhai China Guangdong University of Technology Guangzhou China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Automatic polyp segmentation from colonoscopy images is crucial for colon cancer diagnosis. Nonetheless, training fully supervised polyp segmentation models requires timeconsuming pixel-level annotations. Instead, box annotations of polyps are more attainable. To reduce the burden of annotation, we propose BoxPolypSAM, a box-supervised polyp segmentation model based on the Segment Anything Model (SAM). We leverage a box-supervised projection loss to confine predicted masks within ground-truth boxes and an augmentation consistency loss to reduce the variability of predicted masks. For adapting SAM, we propose a model architecture AutoPromptSAM, which creates a dense prompt from the image encoder's features for automatic segmentation and employs efficient fine-tuning techniques to transfer the capabilities of the SAM model. Moreover, we distill the model to a lightweight model for quicker inference and less GPU memory cost, which also improves performance. Experimental results on six datasets demonstrate our proposed model and the distilled model show excellent performance in polyp segmentation without relying on any pixel-level labels.

关键词： Training image segmentation Adaptation models Costs Annotations Biological system modeling Memory management graphics processing units Colonoscopy Colorectal cancer

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Detecting Alzheimer's Disease based on Structural Region Analysis using a 3D Shape Descriptor 33

Detecting Alzheimer's Disease based on Structural Region Ana...

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

作者： Duarte, Kaue T. N. Gobbi, David G. Frayne, Richard de Carvalho, Marco A. G. Univ Estadual Campinas Sch Technol BR-13484332 Limeira SP Brazil Univ Calgary Hotchkiss Brain Inst Calgary Image Proc & Anal Ctr Dept Radiol Calgary AB T2N 2T9 Canada Univ Calgary Hotchkiss Brain Inst Calgary Image Proc & Anal Ctr Dept Clin Neurosci Calgary AB T2N 2T9 Canada

ISBN: (纸本)9781728192741

Alzheimer's disease (AD) is a common neurodegenerative dementia that affects older people. Changes in behavior and cognition are the most common characteristics of this disease and are associated with changes in brain structure. Techniques focusing on brain shape have been recently proposed to quantify and understand these changes. One challenge when examining AD is that each anatomical region may have a unique role in and time course for brain deterioration, requiring a whole-brain method that is capable of individual (or regional) analyses at different disease stages. We propose to apply the scale-invariant heat kernel signature descriptor to magnetic resonance brain images in order to evaluate regional shape features across different brain regions. We measured the shape feature similarity in 500 subjects, equally divided across five progressive, disease-based stages. The shape analysis provided a complementary perspective to whole-brain analysis, due to the capability of identifying how different structures degenerate at different rates in the brain. In total, a group of 99 distinct brain regions belonging to cortical and deep gray matter were analyzed across the five disease stages. Preliminary assessment of shape-based analysis of key brain regions demonstrated that SIHKS was predictive of disease stage and disease progression.

关键词： alzheimer brain analysis disease progression image processing image segmentation magnetic resonance imaging shape descriptor

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