检索结果-内蒙古大学图书馆

5th International Conference on image processing and machine vision, IPMV 2023

ISBN: (纸本)9781450397926

The proceedings contain 16 papers. The topics discussed include: performance evaluation of recent object detection models for traffic safety applications on edge;tracking of artillery shell using optical flow;action recognition with non-uniform key frame selector;a view direction-driven approach for automatic room mapping in mixed reality;automatic gait gender classification using convolutional neural networks;deep 3D-2D convolutional neural networks combined with Mobinenetv2 for hyperspectral image classification;attention based BiGRU-2DCNN with hunger game search technique for low-resource document-level sentiment classification;strategies of multi-step-ahead forecasting for chaotic time series using autoencoder and LSTM neural networks: a comparative study;semi-supervised defect segmentation with uncertainty-aware pseudo-labels from multi-branch network;and security analysis of visual based share authentication and algorithms for invalid shares generation in malicious model.

关键词：

来源：评论

学校读者我要写书评

暂无评论

IMPLICIT CHANNEL LEARNING FOR machine LEARNING applications IN 6G WIRELESS NETWORKS

IMPLICIT CHANNEL LEARNING FOR MACHINE LEARNING APPLICATIONS ...

引用

IEEE International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Elbir, Ahmet M. Shi, Wei Mishra, Kumar Vijay Papazafeiropoulos, Anastasios K. Chatzinotas, Symeon Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust Luxembourg Luxembourg Carleton Univ Sch Informat Technol Ottawa ON Canada US DEVCOM Army Res Lab Adelphi MD USA Univ Hertfordshire Hatfield Herts England

ISBN: (纸本)9798350302615

With the deployment of the fifth generation (5G) wireless systems gathering momentum across the world, possible technologies for 6G are under active research discussions. In particular, the role of machine learning (ML) in 6G is expected to enhance and aid emerging applications such as virtual and augmented reality, vehicular autonomy computer vision and internet of everything. This will result in large segments of wireless data traffic comprising image, video and speech. The ML algorithms process these for classification/recognition/estimation through the learning models located on cloud servers. This requires wireless transmission of data from edge devices to the cloud server. Channel estimation, handled separately from recognition step, is critical for accurate learning performance. Toward combining the learning for both channel and the ML data, we introduce implicit channel learning to perform the ML tasks without estimating the wireless channel. Here, the ML models are trained with channel-corrupted datasets in place of nominal data. Without channel estimation, the proposed approach exhibits approximately 60% improvement in image and speech classification tasks for diverse scenarios such as millimeter wave and IEEE 802.11p vehicular channels.

关键词： machine learning channel estimation artificial intelligence wireless communications

来源：评论

学校读者我要写书评

暂无评论

International Conference on Internet of Everything and Quantum Information processing, IEQIP 2023

International Conference on Internet of Everything and Quant...

引用

International Conference on Internet of Everything and Quantum Information processing, IEQIP 2023

ISBN: (纸本)9783031619281

The proceedings contain 31 papers. The special focus in this conference is on Internet of Everything and Quantum Information processing. The topics include: Revolutionizing Agriculture: A Mobile App for Rapid Plant Disease Prediction and Sustainable Food Security;EMG Based Human machine Integration for IoT Based Instruments;medrack: Bridging Trust and Technology for Safer Drug Supply Chain Using Ethereum and IoT;a Review on Tuberculosis Pattern Detection Based on Various machine Learning Techniques;sensor Based Hand Gesture Identification for Human machine Interface;an Improved Detection System Using Genetic Algorithm and Decision Tree;a Detailed Analysis of Colorectal Polyp Segmentation with U-Network;a Review on Internet of Things (IoT): Parkinson’s Disease Monitoring Device;machine Learning-Based Prediction of Temperature Rise in Squirrel Cage Induction Motor (SCIM);quantum Many-Body Problems: Quantum machine Learning applications;Experimental Study on the Impact of Airborne Dust Deposition on PV Modules Using Internet of Things;bidirectional Converter with Time Utilization-Based Tariff Investigation and IoT Monitoring of Charging Parameters Based on G2V and V2G Operations;predictive Analysis of Telecom Customer Churn Using machine Learning Techniques;baker’s Map Based Chaotic image Encryption in Military Surveillance Systems;Cyber Security Investigation of GPS-Spoofing Attack in Military UAV Networks;ioT Based Enhanced Safety Monitoring System for Underground Coal Mines Using LoRa Technology;ioT Based Hydroponic System for Sustainable Organic Farming;predicting Stride Length from Acceleration Signals Using Lightweight machine Learning Algorithms;unveiling Hate: Multimodal Perspectives and Knowledge Graphs;vision-Based Toddler Activity Recognition: Challenges and applications;automated W-Sitting Posture Detection in Toddlers.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Building Detection Using Very High Resolution SAR images with Multi-Direction Based on Weighted-Morphological Indexes 12

Building Detection Using Very High Resolution SAR Images wit...

引用

12th Iranian/2nd International Conference on machine vision and image processing, MVIP 2022

作者： Amjadipour, Fateme Ghassemian, Hassan Imani, Maryam Tarbiat Modares University Image Processing and Information Analysis Lab Tehran Iran

ISBN: (纸本)9781665412162

Today, technological advancement in production of radar images can be seen with high spatial resolution and also the availability of these images' significant growth in interpretation and processing of high-resolution radar images. The building extraction from urban areas is one of the most challenging applications in VHR SAR image, which is used to estimate the population and urban development. Detection of individual buildings in the urban context is highly considered by researchers due to complexity of interpreting radar images in these fields. On the other hand, one of the main issues in the complexity of the scatters received from buildings is change in direction of the building relative to the horizon, which is correlated with the look angle. Other influential parameters are geometric distortions, which include layover and shadow effects. In some cases, the effect of shadow is an auxiliary parameter in detection of these targets that increases accuracy of the detection. In this paper, we intend to extract the building from high spatial resolution SAR images using fuzzy fusion of two morphological indicators, SI and DI, which represent the shadow and bright area, respectively. Due to the effect of SAR imaging geometry on ground targets, different sizes and directions of structural elements were applied to the image. The use of indicators weights with different sizes is proposed in this work. The Detection Ratio of experiment of TerraSAR-X image has a result of 95.3%. © 2022 IEEE.

关键词： Morphology

来源：评论

学校读者我要写书评

暂无评论

RASHT: A Partially Reconfigurable Architecture for Efficient Implementation of CNNs

引用

IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2022年第7期30卷 860-868页

作者： Darbani, Paria Rohbani, Nezam Beitollahi, Hakem Lotfi-Kamran, Pejman Iran Univ Sci & Technol Sch Comp Engn Tehran *** Iran Inst Res Fundamental Sci IPM Sch Comp Sci Tehran 193955531 Iran

Convolutional neural networks (CNNs) are widely used in machine learning (ML) applications such as image processing. CNN requires heavy computations to provide significant accuracy for many ML tasks. Therefore, the efficient implementations of CNNs to improve performance using limited resources without accuracy reduction is a challenge for ML systems. One of the architectures for the efficient execution of CNNs is the array-based accelerator, that consists of an array of similar processing elements (PEs). The array accelerators are popular as high-performance architecture using the features of parallel computing and data reuse. These accelerators are optimized for a set of CNN layers, not for individual layers. Using the same accelerator dimension size to compute all CNN layers with varying shapes and sizes leads to the resource underutilization problem. We propose a flexible and scalable architecture for array-based accelerator that increases resource utilization by resizing PEs to better match the different shapes of CNN layers. The low-cost partial reconfiguration improves resource utilization and performance, resulting in a 23.2% reduction in computational times of GoogLeNet compared to the state-of-the-art accelerators. The proposed architecture decreases the on-chip memory access rate by 26.5% with no accuracy loss.

关键词： Computer architecture Convolutional neural networks Arrays Resource management System-on-chip Computational modeling Very large scale integration Array accelerator convolutional neural network (CNN) image processing and computer vision machine learning (ML) reconfigurable hardware

来源：评论

学校读者我要写书评

暂无评论

Optimizing image Classification Using Bag of Features and Support Vector machines 4

Optimizing Image Classification Using Bag of Features and Su...

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mahantesh, K. Navyashree, K.S. Nairy, Devika S. Asha, R. Anshitha, B. Bengaluru India Sjb Institute of Technology Visvesvaraya Technological University Department of Ece Bengaluru India

ISBN: (纸本)9798350352931

image categorization is a fundamental task in computer vision, with applications in domains such as object recognition, medical imaging, and autonomous systems. Traditional approaches frequently fail to balance accuracy, computing efficiency, and scalability, particularly when dealing with big and complex datasets. This work presents a novel picture classification strategy that combines the Bag of Features (BoF) model with Support Vector machines (SVM). The BoF model describes images by extracting local visual characteristics (such as SIFT, SURF, or ORB) from image patches and quantizing them into visual words to create a histogram representation. SVM, a powerful machine learning classifier, is used to classify these histograms, utilizing its capacity to handle high-dimensional, sparse data. Experiments using common image classification datasets show that the BoF-SVM system greatly outperforms previous methods, resulting in higher classification accuracy and lower processing costs. Furthermore, it has superior generalization to previously unseen data and is more resistant to noise and picture changes. The suggested BoF-SVM system produces promising results for boosting both accuracy and efficiency in image classification tasks, with room for further optimization in more complicated and diversified applications © 2024 IEEE.

关键词： image classification

来源：评论

学校读者我要写书评

暂无评论

Spatial Quality Assessment of Pansharpened images Based on Gray Level Co-Occurrence Matrix 12

Spatial Quality Assessment of Pansharpened Images Based on G...

引用

12th Iranian/2nd International Conference on machine vision and image processing, MVIP 2022

作者： Aghapour Maleki, Shiva Ghassemian, Hassan Tarbiat Modares University Image Processing and Information Analysis Laboratory Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9781665412162

Assessing the quality of pansharpened images is a critical issue in order to obtain a quantitative score to represent the quality and compare the performance of different fusion methods. Most of the introduced metrics for pansharpened image quality assessment, evaluate the spectral content of the image, while in different applications of remote sensing like detection and identification of image objects, spatial quality has an important role. In the current study, a new index for spatial quality assessment is introduced that extracts gray level co-occurrence matrix (GLCM) from distorted and reference images and compares the similarities of these features. The tempere image database 2013 (TID2013) that provides reference and different types of distorted images with subjective scores of each image is used as the desired database. To solve the high computational complexity of obtaining GLCM features, the fast GLCM method is employed. In this way, 16 different features are extracted. To select the features that have the most consistency with the human visual system (HVS), the forward floating search method is used as a feature selection method and five features are obtained as the final features to form the desired index. Experimental results show the efficiency of the proposed method in determining the spatial quality of fused images compared with that of the available quality assessment metrics. © 2022 IEEE.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Impact of Hybrid [CPU-GPU] Architecture on machine Learning-based image-to-image Translation Using HiDT

Impact of Hybrid [CPU-GPU] Architecture on Machine Learning-...

引用

2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Kantharaju, V. Chandrashekhar, B.N. Niranjanamurthy, M. Murthy, S.V.N. Bms Institute of Technology and Management Department of Ai & Ml Bengaluru India Amity University Amity School of Engineering and Technology Department of Cse Bangalore India S J C Institute of Technology Department of Cse Karnataka Chikkaballapur India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359688

image-to-image translation is the process of transforming an image from one domain to another, where the goal is to learn the mapping between an input image and an output image. This task has been generally performed by using a training set of aligned image pairs on fewer cores-based CPU-based architecture, which mainly aims to transfer images from a source domain to a target domain while preserving the content representations by consuming more execution time. Due to its broad range of applications in numerous computer vision and image processing problems, including image synthesis, segmentation, style transfer, restoration, and pose estimation, GPU-based image-to-image has attracted growing attention and made enormous progress in recent years. It can be utilized for a variation of principles, including photo enhancement, object transformation, season transfer, and collection style transfer. Only CPU and only GPU-based architecture are difficult in order to speed up the image processing task, especially during re-rendering the same scene under various illuminations characteristic for day, night, or dawn. To address this issue, in this work, we are proposing the Hybrid CPU-GPU-based architecture with HiDT technology for implementing the image translation works at tremendous speed. On the hybrid CPU-GPU-based architecture, it is possible to train a multi-domain image-to-image translation model with HiDT on variable size of dataset unaligned images without domain labels using this technology when it is integrated into an application. The speed of the mentioned application can be achieved by using emerging technologies such as pix2pixHD and HiDT on hybrid architecture, where pix2pixHD is a deep learning-based technique for high-resolution photorealistic image-to-image translation, and it is implemented in PyTorch. This article represents Impact of Hybrid Architecture on machine Learning-based image-toimage Translation Using HiDT. © 2024 IEEE.

关键词： Training Knowledge engineering image segmentation image resolution image synthesis Pose estimation Lighting

来源：评论

学校读者我要写书评

暂无评论

TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization

TruFor: Leveraging all-round clues for trustworthy image for...

引用

IEEE/CVF Conference on Computer vision and Pattern Recognition (CVPR)

作者： Guillaro, Fabrizio Cozzolino, Davide Sud, Avneesh Dufour, Nicholas Verdoliva, Luisa Univ Federico II Naples Naples Italy Google Res Mountain View CA USA

ISBN: (纸本)9798350301298

In this paper we present TruFor, a forensic framework that can be applied to a large variety of image manipulation methods, from classic cheapfakes to more recent manipulations based on deep learning. We rely on the extraction of both high-level and low-level traces through a transformer-based fusion architecture that combines the RGB image and a learned noise-sensitive fingerprint. The latter learns to embed the artifacts related to the camera internal and external processing by training only on real data in a self-supervised manner. Forgeries are detected as deviations from the expected regular pattern that characterizes each pristine image. Looking for anomalies makes the approach able to robustly detect a variety of local manipulations, ensuring generalization. In addition to a pixel-level localization map and a whole-image integrity score, our approach outputs a reliability map that highlights areas where localization predictions may be error-prone. This is particularly important in forensic applications in order to reduce false alarms and allow for a large scale analysis. Extensive experiments on several datasets show that our method is able to reliably detect and localize both cheapfakes and deepfakes manipulations outperforming state-of-the-art works. Code is publicly available at https://***/TruFor/.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Design of vision-guided Gripping System for 6DOF Robots Combined with Dexterous Hands 7

Design of Vision-guided Gripping System for 6DOF Robots Comb...

引用

7th International Conference on Robotics, Control and Automation Engineering, RCAE 2024

作者： Wang, Chengwen Wan, Guoyang Li, Hanqi Li, Xuna Zheng, Da Teng, Mingyao Anhui University of Engineering Dept. School of Electrical Engineering Wuhu China

ISBN: (纸本)9798350355642

In the robot application system incorporating dexterous hand, a vision-based robot grasping system is proposed to address the lack of robustness of dexterous hand in grasping fixed attitude objects. First, a 6DOF robot grasping system based on machine vision is constructed using dexterous hand, depth camera and 6DOF collaborative robot, which realizes accurate grasping under vision guidance;second, to solve the problem of vision system's poor localization accuracy due to the loss of image information and features caused by image noise, occlusion and complex background in the process of image processing, a pooling layer and attention mechanism to enhance the feature extraction ability;moreover, an optimized dexterous hand grasping strategy is proposed through exhaustive grasping action design and analysis, which effectively improves the robustness of the system. The experimental results show that the accuracy of the target detection model reaches 87% through the localization measurement of the experimental objects, which is 2.1% higher than the original method, and the grasping success rate of the robotic system equipped with dexterous hand and depth camera is improved by 3.5%. These results validate the feasibility of the robotic grasping system incorporating dexterous hands in practical applications and significantly enhance the robustness of the system. © 2024 IEEE.

关键词： Collaborative robots

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：