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检索条件"任意字段=3rd International Conference on Sensors, Signal and Image Processing, SSIP 2020"

共 183 条记录，以下是1-10 订阅

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ssip 2020 - 2020 3rd international conference on sensors, signal and image processing

SSIP 2020 - 2020 3rd International Conference on Sensors, Si...

引用

3rd international conference on sensors, signal and image processing, ssip 2020

ISBN: (纸本)9781450388283

The proceedings contain 16 papers. The topics discussed include: development of the localized road damage detection model using deep neural network;high-resolution view synthesis in camera-projector systems using compressive dual imaging;multi-resolution face recognition with drones;generating stylistic images by extending neural style transfer method;quality assessment of defocused image recovery algorithms;fall detection, location and identification for elderly institution;a brief introduction to Xiaoping Ding’s new number-figure model and his thoughts on new calculus principles;evaluation of dedicated Bluetooth low energy wireless data transfer for an implantable EMG sensor;music upscaling using convolutional neural networks;automatic visual inspection of turbo vanes produced by investment casting process;and multiple paths leak localization system in water distribution network.

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Proceedings of 3rd international conference on Computer Vision and image processing 1

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丛书名： Advances in Intelligent Systems and Computing

2020年

作者： Bidyut B. Chaudhuri Masaki Nakagawa Pritee Khanna Sanjeev Kumar

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Application of image Recognition Algorithm in Positioning and Emergency Call Function of Smart Safety Helmets 24

Application of Image Recognition Algorithm in Positioning an...

引用

3rd international conference on signal processing, Computer Networks and Communications, SPCNC 2024

作者： Zhang, Dongdong Zhang, Kan Li, Yong Cui, Yichen Zheng, Ke Liu, Yafei Marketing Service Center of State Grid Henan Electric Power Company Henan Zhengzhou China

ISBN: (纸本)9798400710834

In terms of ensuring the safety of construction workers and cyclists, traditional positioning methods often rely on GPS signals, but the accuracy of GPS positioning is limited indoors or in signal-blocked areas. At the same time, the traditional way of asking for help may delay the rescue time due to cumbersome operation, noisy environment or psychological pressure in emergency situations. To address these problems, this paper first integrates high-definition cameras, sensors and wireless communication modules on the smart safety helmet (SSH). The captured image is then preprocessed by noise reduction, enhancement, cropping, etc., and key features such as facial features are identified through CNN. The extracted features are then matched and analyzed, and compared with pre-stored map data, landmark databases, etc. At the same time, CNN is used to identify safety signs in the image to determine whether the wearer is in a dangerous environment. The research results found that the average error of CNN-based positioning technology is 1.32 meters, which is significantly lower than the 3.56 meters of traditional GPS technology. In terms of the response time of the rescue function, the average response time of CNN-based technology is 500.5 milliseconds, and the rescue success rate is also significant. improve. Therefore, this study concludes that CNN can improve the positioning accuracy and rescue response speed of SSHs, improve rescue efficiency and safety, and show great potential in the field of safety. © 2024 Copyright held by the owner/author(s).

关键词： Noise abatement

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Architecture design of intelligent image sensor integrated with detection and processing 3

Architecture design of intelligent image sensor integrated w...

引用

3rd international Computing Imaging conference, CITA 2023

作者： Guofang, Zhai Songbo, Wu Yao, Yao Ruimeng, Zhang Beijing Institute of Space Mechanics and Electricity Beijing100094 China

ISBN: (纸本)9781510671409

This paper first introduces the application background of detection and processing integrated intelligent image sensor, and proposes an overall architecture of intelligent image sensor that uses hybrid stacking process for vertical interconnection. Among them, the top pixel layer mainly contains 10K×10K 5um pixel array and row and column drive array, which gives the pixel structure, simulates and analyzes the pixel noise index and effectively guides the design of the readout circuit structure. The lower chip layer mainly contains readout circuits, image signal processing ISPs, general-purpose CPU units, AI neural processing units, and on-chip SRAM. The image signal processing ISP function and the AI neural processing unit are introduced in detail, and the FPGA verification results of the AI neural processing unit are given. © 2023 SPIE.

关键词： image sensors

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Designing of an Autonomous System for Electric Vehicle 3

Designing of an Autonomous System for Electric Vehicle

引用

3rd international conference on Robotics, Electrical and signal processing Techniques, ICREST 2023

作者： Mridul, Towfiq Mahmud Sagor, Abidur Rahman Mridha, Faisal Ahmad Bhuiyan, Md. Mosaraf Hosen Hasan, Md. Jahid Rahman, Md. Abdur American International University-Bangladesh Department of Electrical and Electronics Engineering Dhaka Bangladesh Independent University Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Faculty of Engineering Dhaka Bangladesh

ISBN: (纸本)9798350346435

The creation of an autonomous vehicle has two major goals: to minimize the amount of labor that must be performed manually and to provide the automobile with superior intelligence. In order to turn a standard automobile into an autonomous vehicle that can drive itself around a predetermined map (in this case, a university campus), several modifications need to be made. The outcomes of this project will also contribute to the creation of a more technologically advanced campus atmosphere. Additional responsibilities include taking control of the horn system and being in charge of the headlights while also receiving the response signal from the station. In order to guarantee that everything functions correctly, we utilized components such as the Raspberry Pi, light sensors, motors, Arduino Uno, ultrasonic sensors, buzzers, and cameras. A temporary model is built to ensure that this project is successful right from the start. The training model trains the deep learning approach using images captured by a camera in manual mode, which enables the vehicle to operate in autonomous mode using the multilayered neural network that was taught. The final prototype utilizes image processing methods to track lanes. A few image processing technologies, such as intelligent edge detection, provide autonomous movement capabilities. These algorithms were built and tested on the vehicle. The system operated without a hitch, and the data gathered was within reasonable bounds of expectations. As well as demonstrating how the autonomous system of an electric car works. © 2023 IEEE.

关键词： Cameras

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Lidar 3D object semi-Automatic annotation tool 3

Lidar 3D object semi-Automatic annotation tool

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3rd international conference on Advanced Algorithms and signal image processing, AASIP 2023

作者： Yu, Simin Zhang, Xinchen Dept. College of Physical Science and Technology Central China Normal University CCNU WuHan China

ISBN: (纸本)9781510668522

The environment sensing module is a prerequisite for autonomous driving. The selection of on-board sensors and a good 3D object detection algorithm determine the performance of the environment sensing module. The LIDAR point cloud 3D object semi-Automatic annotation tool uses the detection algorithm in the open source framework MMDtection3D to complete the pre-labeling, and then enters the joint point cloud image labeling tool 3D-BAT to complete the manual correction after the data processing module, and the output labeling results can be supplied to the deep learning based 3D object detection algorithm training after the format conversion. The self-built data acquisition vehicle equipped with 6 cameras and 1 LIDAR collected more than 4000 frames of point cloud data, and the semi-Automatic annotation tool completed the annotation of more than 2000 frames of point cloud data. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Object detection

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A text-driven image style transfer model based on CLIP and SCBAM 3

A text-driven image style transfer model based on CLIP and S...

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3rd international conference on Advanced Algorithms and signal image processing, AASIP 2023

作者： Wu, Haodong Geng, Guohua Zhao, Yanting Wang, Xiaolei Li, Qihang School of Information Science and Technology Northwest University Xi'an710127 China

ISBN: (纸本)9781510668522

To address the issues of low image quality and inadequate detail features encountered in current zero-shot style transfer algorithms, we propose a new text-driven image style transfer model. The model first uses CLIP (Contrastive Languageimage Pre-Training) model to convey the semantic information of text conditions. In addition, We designed a lightweight network, which can quickly express texture information according to text conditions, minimize the similarity cosine distance between the transferred image and the text conditions by CLIP model, and finally obtain the style transfer image. Furthermore, we introduce dual attention mechanism, identity consistency loss, content and style feature loss to make the translated image more vivid and realistic. Extensive experimental results demonstrate that our approach enables the transfer of multiple styles based on text conditions, achieving a broader, more realistic, and faster style transfer compared to existing methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Semantics

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End-to-end Multi-source image Phase Retrieval 3

End-to-end Multi-source Image Phase Retrieval

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3rd IEEE international conference on Computer Systems, ICCS 2023

作者： Zhao, Nan Song, Tianyang Guo, Yina Taiyuan University of Science and Technology Shanxi China

ISBN: (纸本)9798350313666

The phase information of multi-source hybrid images is prone to confusion during image propagation. Since traditional phase retrieval algorithms require a large number of assumptions that cannot be executed by obtaining amplitude, which makes phase retrieval of multi-source mixed images with phase distortion challenging. To address these phase retrieval problems, inspired by dual learning, we propose an end-to-end phase retrieval algorithm based on Multi-source image Phase Retrieval via dual Generative Adversarial Networks (MPrdGAN). By introducing the Least Square Error in the loss function of the network, the algorithm improves the performance of the model to prevent overfitting and ensures the phase retrieval of multi-source mixed images. Experimental results show that the MPrdGAN algorithm reduces the image restoration error, so that the correlation of restored images can reach more than 94.5%, which is widely used in various images such as face and remote sensing. © 2023 IEEE.

关键词： Generative adversarial networks

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Sparse signal recovery for multispectral demosaicking using pseudo-panchromatic image 3

Sparse signal recovery for multispectral demosaicking using ...

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3rd international conference on image processing and Intelligent Control, IPIC 2023

作者： Liao, Ronghao Zhou, Shifu Wu, Guangyuan Jinan250353 China Shandong Publicity public opinion Research Center Jinan250353 China

ISBN: (纸本)9781510668140

Single-sensor multispectral imaging technology has been widely used in computer vision, mechanical diagnosis, cultural history protection and other industries due to its convenience and low cost. Single-sensor multispectral imaging can only generate a single mosaic image, so an efficient method is needed to convert mosaic images into multispectral images. Based on the concept of pseudo-panchromatic image, a 9-band multispectral imaging system is designed in this paper. We directly estimate the pseudo-panchromatic image from the mosaic image, and use the correlation between the pseudo-panchromatic image and each channel to generate a multispectral image by guided filtering and residual interpolation. The experimental results show that the multispectral images obtained by our method are superior to the other two methods in objective and subjective evaluation. © 2023 SPIE.

关键词： Costs

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A small target detection algorithm for aerial image with improved YOLOv5 3

A small target detection algorithm for aerial image with imp...

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3rd international conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2024

作者： Xue, Xianli Jiao, Weidong Zhang, Siyuan Qu, Wenkang Tianjin Key Laboratory of Intelligent Signal and Lmage Processing Civil Aviation University of China TIANJIN300300 China

ISBN: (纸本)9781510680449

The problem of complex backgrounds, dramatic changes in the scale of objects and the presence of a large number of dense small targets in the UAV aerial images. In this study, a detection algorithm for small targets is proposed based on the YOLOv5s algorithm. Firstly, a new BottleNeck: C3LK module is designed for the C3 module using depthwise separable convolution to construct a large convolutional kernel on the YOLOv5 backbone network, which improves the ability to capture global information and compensates for the lack of local information for detecting small targets. Secondly, a small target detection layer and two feature fusion layers are added. At the same time, In order to better detect objects of different scales in complex backgrounds, a module for multi-scale feature extraction is designed by combining dilated convolution and attention mechanism. The experimental results show that the proposed algorithm improved 5.4% over YOLOv5s in terms of mAP@0.5 and over YOLOv7-tiny. The method meet the requirement of real-time detection in aerial image small target detection, and improve the detection performance in small target scenes. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Feature extraction

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