检索结果-内蒙古大学图书馆

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Parth C. Upadhyay Lokesh Karanam John A. Lory Guilherme N. DeSouza Vision-Guided and Intelligent Robotics (ViGIR) Laboratory Department of Electrical Engineering and Computer Science (EECS) Division of Plant Sciences University of Missouri Columbia MO

ISBN: (纸本)9781728190495

Plant-derived crop residue on soil surface provides many important advantages including preventing erosion and conserving soil moisture. In that sense, making accurate determination on the percent of crop residue cover using RGB images can be a fundamental tool in protecting the soil. In our research, we approach the determination of such percentages as a classification problem, and in this paper, we compare two of these approaches. Both approaches relied on support vector machines (SVM) as the classifier of choice, and the same set of features, which were selected in our previous studies on the same topic. In this paper we developed a SVM ensemble with a hierarchical structure and compared it against a single, multi-class SVM classifier. In the SVM ensemble framework, four two-class SVMs and one five-class SVM were combined in sequence to better separate adjacent levels of residue cover. The rationale of the ensemble was to allow each of the two-class SVMs to find the hyperplanes that maximize the margin between the corresponding two consecutive classes. Then, based on the distance of the samples to these hyperplanes, probabilistic estimates of the data-point belonging to the class were computed and added as extra inputs for the last SVM. In order to enhance the performance of the ensemble, other considerations such as the use of Grid Search method for optimizing the hyperparameters were employed in the tuning of the SVMs. Numerical experiments were conducted over a dataset of 4,400 images, which were collected from 88 locations in 40 row crop fields in five Missouri counties between mid-April and early July in 2018 and 2019. The images were collected using a camera mounted on a tripod, with a spatial resolution of 0.014 cm pixel- 1 GSD (Ground Sampling Distance). The experiments highlighted the better performance of the proposed hierarchical ensemble classifier, which achieved a cross-validation accuracy of 86.3 % vs an accuracy of 80.4 % for the single SVM, whil

关键词： Support vector machines Measurement Training Search methods Soil moisture Crops Probabilistic logic

来源：评论

学校读者我要写书评

暂无评论

High throughput event filtering: The interpolation-based DIF algorithm hardware architecture

引用

Microprocessors and Microsystems 2025年

作者： Marcin Kowalczyk Tomasz Kryjak Embedded Vision Systems Group Department of Automatic Control and Robotics Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Krakow Poland

In recent years, there has been rapid development in the field of event vision. It manifests itself both on the technical side, as better and better event sensors are available, and on the algorithmic side, as more and more applications of this technology are proposed and scientific papers are published. However, the data stream from these sensors typically contains a significant amount of noise, which varies depending on factors such as the degree of illumination in the observed scene or the temperature of the sensor. We propose a hardware architecture of the Distance-based Interpolation with Frequency Weights(DIF) filter and implement it on an FPGA chip. To evaluate the algorithm and compare it with other solutions, we have prepared a new high-resolution event dataset, which we are also releasing to the community. Our architecture achieved a throughput of 403.39 million events per second (MEPS) for a sensor resolution of 1280 × 720 and 428.45 MEPS for a resolution of 640 × 480. The averagevalues of the Area Under the Receiver Operating Characteristic (AUROC) index ranged from 0.844 to 0.999, depending on the dataset, which is comparable to the state-of-the-art filtering solutions, but with much higher throughput and better operation over a wide range of noise levels.

关键词： Dataset Denoising DVS FPGA High-throughput

来源：评论

学校读者我要写书评

暂无评论

Using Taint Analysis and Reinforcement Learning (TARL) to Repair Autonomous Robot Software

Using Taint Analysis and Reinforcement Learning (TARL) to Re...

引用

IEEE Symposium on Security and Privacy Workshops (SPW)

作者： D. Lyons S. Zahra Robotics & Computer Vision Laboratory Fordham University New York USA

ISBN: (数字)9781728193465

ISBN: (纸本)9781728193472

It is important to be able to establish formal performance bounds for autonomous systems. However, formal verification techniques require a model of the environment in which the system operates; a challenge for autonomous systems, especially those expected to operate over longer timescales. This paper describes work in progress to automate the monitor and repair of ROS-based autonomous robot software written for an apriori partially known and possibly incorrect environment model. A taint analysis method is used to automatically extract the dataflow sequence from input topic to publish topic, and instrument that code. A unique reinforcement learning approximation of MDP utility is calculated, an empirical and non-invasive characterization of the inherent objectives of the software designers. By comparing design (a-priori) utility with deploy (deployed system) utility, we show, using a small but real ROS example, that it's possible to monitor a performance criterion and relate violations of the criterion to parts of the software. The software is then patched using automated software repair techniques and evaluated against the original off-line utility.

关键词： Uncertainty Reinforcement learning Maintenance engineering Software Monitoring Autonomous robots Formal verification

来源：评论

学校读者我要写书评

暂无评论

Three-dimensional Terrain Aware Autonomous Exploration for Subterranean and Confined Spaces

Three-dimensional Terrain Aware Autonomous Exploration for S...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Héctor Azpúrna Mario F. M. Campos Douglas G. Macharet Computer Vision and Robotics Laboratory (VeRLab) Universidade Federal de Minas Gerais MG Brazil Instituto Tecnológico Vale (ITV) Ouro Preto Brazil

Despite the advances in autonomous navigation and motion planning, there are still several challenges to overcome, especially for confined or underground spaces. Confined scenarios present challenges such as lack of global or accurate external localization, uneven and slippery terrains, and multilevel stages. Exploring and mapping unknown unstructured environments is a fundamental step into the safe and efficient accomplishment of real-world tasks such as search and rescue missions or the autonomous inspection of dangerous areas. This paper proposes a novel three-dimensional autonomous exploration method for ground robots that considers the terrain traversability combined with the frontier expected information gain as a metric for the next best frontier selection in GPS-denied, confined spaces. Safe paths for navigation and frontier extraction are calculated iteratively from multiple 3D map representations such as octrees and meshes. Results in realistic simulated underground scenarios from the DARPA subterranean challenge demonstrate the technique's feasibility, achieving a more reliable and faster exploration rate over competing approaches.

关键词： Location awareness Three-dimensional displays Navigation Conferences Octrees Inspection Extraterrestrial measurements

来源：评论

学校读者我要写书评

暂无评论

ROSANA: Robot for Social Interaction in Unstructured Dynamic Environments

ROSANA: Robot for Social Interaction in Unstructured Dynamic...

引用

IEEE Latin American robotics Symposium, LARS

作者： Pedro V. V. Paiva Murillo R. Batista Marcos V. Cruz Germano B. Filho Josué J. G. Ramos Robotics and Computer Vision Lab - Renato Archer IT Center Campinas Brazil

ISBN: (数字)9780738111537

ISBN: (纸本)9781665419857

Socially Interactive Robots must have the ability to interact with humans in a socially appropriate manner. Such a task requires a high level of perception of the robotic agent. A high degree of perception requires considerable computational power, something challenging to achieve in mobile robotics. Robots need to balance operating time, batteries (autonomy and weight), embedded sensors, and processing hardware. One way to overcome the mobile agents' perception limitations is to move the data processing capabilities to external processing units. In this article, we present the initial results of the ROSANA platform, a socially interactive robot for public environments capable of interacting with humans in the wild. ROSANA has a high level of visual perception, thanks to an architecture based on cloud services. We named this approach as Perception as a Service - PPaaS. Case studies exemplify the integration between the mobile agent and the cloud in the ROSANA architecture like detection, interaction with humans, and route planning considering humans in unstructured and dynamic environments.

关键词： Robots Robot sensing systems Cloud computing computer architecture Navigation Task analysis Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Estimating Human Body Orientation using Skeletons and Extreme Gradient Boosting

Estimating Human Body Orientation using Skeletons and Extrem...

引用

IEEE Latin American robotics Symposium, LARS

作者： Pedro Victor Vieira de Paiva Murillo Rehder Batista Josue Junior Guimarães Ramos Robotics and Computer Vision Lab Renato Archer IT Center Campinas Brazil

ISBN: (数字)9780738111537

ISBN: (纸本)9781665419857

Human body orientation is a valuable information for social robots due to its use in social path planning and person (or group) approaching. Multi-sensory is an alternative for orientation estimation but it is not available for in-the-wild applications. To estimate orientation using only a single image, several computer vision techniques have demonstrated insufficient accuracy. We propose combining 2D skeleton information with extreme gradient boosting algorithm to detect orientation. We obtain person's skeleton using the OpenPose deep architecture, and extract its distances and angle features. These attributes are used to train a gradient boosting learning system by XGBoost. To evaluate predictions considering real situations based on a single camera, the TUD Multiview Pedestrian dataset is used. We compared the proposed approach against various state-of-the-art methods and our results indicate better classification performance. Furthermore, we prove that our method is viable for body orientation estimation on real-life scenarios by presenting case studies on simulated scenes.

关键词： Skeleton Estimation Two dimensional displays Boosting Mathematical model Feature extraction Three-dimensional displays

来源：评论

学校读者我要写书评

暂无评论

Distilled Transformers with Locally Enhanced Global Representations for Face Forgery Detection

arXiv

引用

arXiv 2024年

作者： Zhang, Yaning Li, Qiufu Yu, Zitong Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China School of Computing and Information Technology Great Bay University Guangdong Dongguan523000 China

Face forgery detection (FFD) is devoted to detecting the authenticity of face images. Although current CNN-based works achieve outstanding performance in FFD, they are susceptible to capturing local forgery patterns generated by various manipulation methods. Though transformer-based detectors exhibit improvements in modeling global dependencies, they are not good at exploring local forgery artifacts. Hybrid transformer-based networks are designed to capture local and global manipulated traces, but they tend to suffer from the attention collapse issue as the transformer block goes deeper. Besides, soft labels are rarely available. In this paper, we propose a distilled transformer network (DTN) to capture both rich local and global forgery traces and learn general and common representations for different forgery faces. Specifically, we design a mixture of expert (MoE) module to mine various robust forgery embeddings. Moreover, a locally-enhanced vision transformer (LEVT) module is proposed to learn locally-enhanced global representations. We design a lightweight multi-attention scaling (MAS) module to avoid attention collapse, which can be plugged and played in any transformer-based models with only a slight increase in computational costs. In addition, we propose a deepfake self-distillation (DSD) scheme to provide the model with abundant soft label information. Extensive experiments show that the proposed method surpasses the state of the arts on five deepfake datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Geometry Constrained Weakly Supervised Object Localization 16th

Geometry Constrained Weakly Supervised Object Localization

引用

16th European Conference on computer vision, ECCV 2020

作者： Lu, Weizeng Jia, Xi Xie, Weicheng Shen, Linlin Zhou, Yicong Duan, Jinming Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science University of Birmingham Birmingham United Kingdom Department of Computer and Information Science University of Macau Zhuhai China

ISBN: (纸本)9783030585730

We propose a geometry constrained network, termed GC-Net, for weakly supervised object localization (WSOL). GC-Net consists of three modules: a detector, a generator and a classifier. The detector predicts the object location defined by a set of coefficients describing a geometric shape (i.e. ellipse or rectangle), which is geometrically constrained by the mask produced by the generator. The classifier takes the resulting masked images as input and performs two complementary classification tasks for the object and background. To make the mask more compact and more complete, we propose a novel multi-task loss function that takes into account area of the geometric shape, the categorical cross-entropy and the negative entropy. In contrast to previous approaches, GC-Net is trained end-to-end and predict object location without any post-processing (e.g. thresholding) that may require additional tuning. Extensive experiments on the CUB-200-2011 and ILSVRC2012 datasets show that GC-Net outperforms state-of-the-art methods by a large margin. Our source code is available at https://***/lwzeng/GC-Net. © 2020, Springer Nature Switzerland AG.

关键词： Geometry

来源：评论

学校读者我要写书评

暂无评论

Development of Hard Hat Wearing Monitoring System Using Deep Neural Networks with High Inference Speed

Development of Hard Hat Wearing Monitoring System Using Deep...

引用

2020 International Russian Automation Conference, RusAutoCon 2020

作者： Filatov, Nikolay Maltseva, Natalia Bakhshiev, Aleksandr Russian State Scientific Center for Robotics and Technical Cybernetics Laboratory of Computer Vision Systems Saint-Petersburg Russia Peter the Great St. Petersburg Polytechnic University Saint-Petersburg Russia

ISBN: (纸本)9781728161303

Personal protection equipment is essential for safe working conditions on a construction sites and other industrial enterprises. However, there is a number of employee who violate safety precautions, thus, taking additional risks. To avoid accidents strict supervision should be applied. In this work an automatic hard hat wearing detection system from surveillance cameras is developed. To save computational resources small neural networks with high inference speed are applied. For the training of neural networks, a dataset consisting of several industrial scenes and 1478 images was composed. Frame based precision of SqueezeDet neural network for object detection was increased by 9% with the use of additional MobileNets classifier without significant decrease in operating speed. The overall system achieved 0.75 F1-score on a test dataset. Therefore, the developed hard hat wearing monitoring systems is suitable for enhancing on-site safety of workers. © 2020 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Challenges of deep learning methods for COVID-19 detection using public datasets

引用

Informatics in Medicine Unlocked 2022年 30卷 100945页

作者： Hasan, Md. Kamrul Alam, Md. Ashraful Dahal, Lavsen Roy, Shidhartho Wahid, Sifat Redwan Elahi, Md. Toufick E. Martí, Robert Khanal, Bishesh Department of Electrical and Electronic Engineering (EEE) Khulna University of Engineering & Technology (KUET) Khulna 9203 Bangladesh Nepal Applied Mathematics and Informatics Institute for Research (NAAMII) Nepal Computer Vision and Robotics Institute University of Girona Spain

Since the COVID-19 pandemic, several research studies have proposed Deep Learning (DL)-based automated COVID-19 detection, reporting high cross-validation accuracy when classifying COVID-19 patients from normal or other common Pneumonia. Although the reported outcomes are very high in most cases, these results were obtained without an independent test set from a separate data source(s). DL models are likely to overfit training data distribution when independent test sets are not utilized or are prone to learn dataset-specific artifacts rather than the actual disease characteristics and underlying pathology. This study aims to assess the promise of such DL methods and datasets by investigating the key challenges and issues by examining the compositions of the available public image datasets and designing different experimental setups. A convolutional neural network-based network, called CVR-Net (COVID-19 Recognition Network), has been proposed for conducting comprehensive experiments to validate our hypothesis. The presented end-to-end CVR-Net is a multi-scale-multi-encoder ensemble model that aggregates the outputs from two different encoders and their different scales to convey the final prediction probability. Three different classification tasks, such as 2-, 3-, 4-classes, are designed where the train–test datasets are from the single, multiple, and independent sources. The obtained binary classification accuracy is 99.8% for a single train–test data source, where the accuracies fall to 98.4% and 88.7% when multiple and independent train–test data sources are utilized. Similar outcomes are noticed in multi-class categorization tasks for single, multiple, and independent data sources, highlighting the challenges in developing DL models with the existing public datasets without an independent test set from a separate dataset. Such a result concludes a requirement for a better-designed dataset for developing DL tools applicable in actual clinical settings. The datas

关键词： Chest computed tomography and X-ray Convolutional neural networks COVID-19 disease Ensemble classifier

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：