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共 18153 条记录，以下是821-830 订阅

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Mobility Stick with Haptic Feedback for People with vision Impairments

Mobility Stick with Haptic Feedback for People with Vision I...

引用

2024 international conference on signal processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Raghul, J. Durga Devi, K. Sreya, M. Maithreye, S. SRM Institute of Science and Technology Department of Electronic and Computer Engineering Chennai Ramapuram India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Ramapuram India

ISBN: (纸本)9798331540685

In this research, haptic motor, Node MCU, ultrasonic sensors are used to address the problem associated with the current walking stick for visually disabled people. These problems include disturbing their main sense of survival which is hearing. Hence, a system containing ultrasonic sensors, haptic motor and Internet of Things has been recommended in this study. This study illustrates that the implementation of haptic motors and ultrasonic sensors can successfully address the major issues that current alternatives face and it has a positive ad significant impact on visually disabled people. Using this method, one can monitor the person's movement and whether they fell down. This system makes use of IoT it integrate the data from sensors and update it onto the website for other person monitoring. © 2024 IEEE.

关键词： Ultrasonic sensors

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TSPTQ-ViT: Two-Scaled Post-Training Quantization for vision Transformer 48

TSPTQ-ViT: Two-Scaled Post-Training Quantization for Vision ...

引用

48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Tai, Yu-Shan Lin, Ming-Guang Wu, An-Yeu Andy National Taiwan University Graduate Institute of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9781728163277

vision transformers (ViTs) have achieved remarkable performance in various computer vision tasks. However, intensive memory and computation requirements impede ViTs from running on resource-constrained edge devices. Due to the non-normally distributed values after Softmax and GeLU, post- training quantization on ViTs results in severe accuracy degradation. Moreover, conventional methods fail to address the high channel-wise variance in LayerNorm. To reduce the quantization loss and improve classification accuracy, we propose a two-scaled post-training quantization scheme for vision transformer (TSPTQ-ViT). We design the value-aware two-scaled scaling factors (V-2SF) specialized for post- Softmax and post-GeLU values, which leverage the bit sparsity in non-normal distribution to save bit-widths. In addition, the outlier-aware two-scaled scaling factors (O-2SF) are introduced to LayerNorm, alleviating the dominant impacts from outlier values. Our experimental results show that the proposed methods reach near-lossless accuracy drops ( © 2023 IEEE.

关键词： Statistics

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Enhancing Mobile robot Path Planning Through Advanced Deep Reinforcement Learning 5th

Enhancing Mobile Robot Path Planning Through Advanced Deep R...

引用

5th international conference on 3D Imaging Technologies-Multidimensional signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Xie, Hongzhi Niu, Runxin University of Science and Technology of China Anhui China Hefei Institutes of Physical Sciences Chinese Academy of Sciences Anhui China

ISBN: (纸本)9789819751839

The area of automation offers several uses for mobile robot route planning. Traditional route planning techniques, however, often struggle in contexts that are dynamic and complicated. Deep reinforcement learning, a new technique, has recently shown significant promise in the area of mobile robot route planning. In this article, we investigate a solution for mobile robot route planning in complicated situations based on enhanced deep reinforcement learning. Path search techniques that provide autonomous robot navigation in various settings are based on reinforcement learning. The suggested approach produces notable gains in both route planning accuracy and resilience, according to experimental findings in both simulation and real-world settings, offering a novel approach for effective path planning for mobile robots in complex environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep reinforcement learning

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Intelligent medical waste detection and classification system based on machine vision

Intelligent medical waste detection and classification syste...

引用

2023 international conference on Image, signal processing, and Pattern Recognition, ISPP 2023

作者： Cai, Zhaoxin Bian, Tianhu Bai, Chunyu Li, Pengxu Sun, Changping School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9781510666351

Since the global eruption of COVID-19, there have been issues with the global medical management system. The classification and treatment of medical waste has brought a huge burden to medical staff. Traditional manual classification has problems such as the heavy workload, difficult identification, and susceptibility to infection. This paper designs an intelligent medical waste detection and classification system based on machine vision. Based on deep learning models such as YOLOv5 and YOLO-resnet18, the data is trained and optimized to obtain a target detection model suitable for medical waste. Through experimental tests, the system has fast detection speed and high accuracy, and the accuracy of medical waste detection and classification reaches 90% and 99% respectively. © 2023 SPIE.

关键词： Computer vision

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Masked Image Pretraining on Language Assisted Representation

Masked Image Pretraining on Language Assisted Representation

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Hou, Zejiang Kung, Sun-Yuan Princeton University United States

ISBN: (纸本)9798350368741

Self-attention based transformer models have been dominating many computer vision tasks in the past few years. Their superb model qualities heavily depend on the excessively large labeled image datasets. In order to reduce the reliance on large labeled datasets, reconstruction based masked autoencoders are gaining popularity, which learn high quality transferable representations from unlabeled images. For the same purpose, recent weakly supervised image pretraining methods explore language supervision from text captions accompanying the images. In this work, we propose Masked Image pretraining on Language Assisted representatioN, dubbed as MILAN. Instead of predicting raw pixels or low level features, our pretraining objective is to reconstruct the image features with substantial semantic signals that are obtained using caption supervision. Moreover, to accommodate our reconstruction target, we propose a more efficient prompting decoder architecture and a semantic aware mask sampling mechanism, which further advance the transfer performance of the pretrained model. Experimental results demonstrate that MILAN delivers higher accuracy than the previous works. When the masked autoencoder is pretrained and finetuned on ImageNet-1K dataset with an input resolution of 224×224, MILAN achieves a top-1 accuracy of 85.4% on ViT-Base, surpassing previous state-of-the-arts by 1%. In the downstream semantic segmentation task, MILAN achieves 52.7 mIoU using ViT-Base on ADE20K dataset, outperforming previous masked pretraining results by 4 points. © 2025 IEEE.

关键词： masked image pretraining vision transformer

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Defending Against Universal Patch Attacks by Restricting Token Attention in vision Transformers 48

Defending Against Universal Patch Attacks by Restricting Tok...

引用

48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Yu, Hongwei Chen, Jiansheng Ma, Huimin Yu, Cheng Ding, Xinlong University of Science and Technology Beijing Beijing China Tsinghua University Beijing China

ISBN: (纸本)9781728163277

Previous works reveal that similar to CNNs, vision transformers (ViT) are also vulnerable to universal adversarial patch attacks. In this paper, we empirically reveal and mathematically explain that the shallow tokens in the transformer and the attention of the network can largely influence the classification result. Adversarial patches usually produce large feature norm for the corresponding shallow token vectors which can attract the attention anomalously. Inspired by this, we propose a restriction operation on the attention matrix, which effectively reduces the influence of the patch region. Experiments on ImageNet validate that our proposal can effectively improve ViT's robustness towards white-box universal patch attacks while maintaining satisfactory classification accuracy for clean samples. © 2023 IEEE.

关键词： Image enhancement

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Land Drone System

Land Drone System

引用

international conference on Image and signal processing and their Applications (ISPA)

作者： Fadi Abd Eladhim Zidi BenElmir Okba Dept.Electrical Engineering University of Biskra Biskra Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Technology develops day by day, which is sometimes surprising. Our vision is a new generation of automated, intelligent, independent, flexible, compliant, and interconnected systems that work seamlessly with humans in agricultural greenhouses. This work consists of studying, designing, and building an automatically and manually controlled robot, which operates in greenhouses as an almost complete substitute for humans. The project consists of three parts:•Weather station part: collects information on the outside climate and sends it to the master unit. Master unit: collects climate information from the agricultural greenhouse and automates the agricultural greenhouse, as well as storing the data in a database.•robot part: The robot part has three modes of movement, moving automatically with the possibility of avoiding obstacles, on the other hand following the line, in the third manually via the web page.•Artificial Intelligence part: It detects and identifies vegetables and fruit. Finally, we produced a prototype robot to test the performance of our system

关键词： Temperature Greenhouses Soil signal processing Sensor systems Temperature control Sensors

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Multi-Modal Food Classification in a Diet Tracking System with Spoken and Visual Inputs 48

Multi-Modal Food Classification in a Diet Tracking System wi...

引用

48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Gowda, Shivani Hu, Yifan Korpusik, Mandy Loyola Marymount University Computer Science Department Los AngelesCA United States

ISBN: (纸本)9781728163277

In this paper, we present multi-modal approaches to diet tracking. As health and well-being become increasingly important, mobile applications for diet tracking attract much interest. However, these applications often require users to log their meals based on relatively unreliable memory recall, thereby underestimating nutritional intake and, thus, undermining the efforts of nutrition tracking. To accurately record dietary intake, there is an increasing need for image computational methods. We investigated multi-modal transfer learning approaches on a novel, food-specific image-text dataset, specifically a vision-and-Language Transformer that achieves a held-out test set Micro-F1 score of 77.70% and Macro-F1 score of 51.43% for 696 food categories. We aim to give other researchers new insight into the process of developing domain-specific, multi-modal deep learning models with small datasets. © 2023 IEEE.

关键词： Convolutional Neural Network Long Short-Term Memory Transfer Learning Transformer vision-and-Language

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A method for estimating the contribution of an object to scene semantics 4

A method for estimating the contribution of an object to sce...

引用

4th international conference on Computer vision and Information Technology, CVIT 2023

作者： Lin, Zhiyuan Zhu, Feng Wang, Jianyu Wang, Qun Zhao, Pengfei Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Faculty of Robot Science and Engineering Northeastern University Shenyang110169 China

ISBN: (纸本)9781510672864

The amount of semantic contribution of each object in an image to understand the scene is different. Understanding the relative importance of objects in a scene for scene semantics is critical for various computer vision applications, such as scene recognition and image captioning. In this paper, we refer to the contribution of an object to scene semantics as the degree of its gist and propose a method for Estimating the degree of the Gist of an Instance (EGoI). In the EGoI method, an object gist degree is estimated by the semantic features comparison strategy and the semantic distance comparison strategy. In the first strategy, the image is represented as a scene graph first, then the aggregation features and the node features of different graph node combinations are calculated to estimate the instance gist that is not in the combination of the nodes. In the second strategy, the captions of the complete and incomplete images are generated, then the semantic distance of these captions is used to estimate the instance gist deleted in the scene. Different strategies for estimating the gist degree of instances are tested in the experiments. The results show that the proposed method can effectively quantify the contribution of an instance to scene semantics. Among these strategies, the method that compares semantic features has better discriminative power for the gist degree of various instances in the scene. © 2024 SPIE.

关键词： Semantics

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Application of Computer 3D Image vision Algorithm in Intelligent Image Recognition System 5

Application of Computer 3D Image Vision Algorithm in Intelli...

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2023 5th international conference on Artificial Intelligence and Computer Applications, ICAICA 2023

作者： Li, Yuan Yu, Xin Modern Finance Industry School Shandong Institute of Commerce and Technology Shandong Jinan China

ISBN: (纸本)9798350323313

In this paper, the 3D space imaging model of machine vision is constructed. Starting from the traditional machine vision image processing algorithm flow, the image denoising process and target tracking process are optimized. The method uses the camera to collect the image and video information of the measured object, and transmits it to the controller. The controller corrects the signal obtained by the wireless sensor in the database to reproduce the position of the measured object and the 3D image. A real-time tracking method of motion trajectory based on computer vision is presented. The object autonomous capture, 3D position and motion trajectory tracking. Simulation experiments show that this method is quite different from conventional image processing methods. This method has the advantages of small computation, fast running speed and good real-time performance. It meets the needs of embedded image processing. © 2023 IEEE.

关键词： Image recognition

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