International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)最新文献_第6页

Research on 3D monitoring method of tree barrier based on satellite remote sensing fusion of transmission tower features 基于输电塔特征卫星遥感融合的树障三维监测方法研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014411

Sihang Zhang, Xiaojun Dou, Zhi Yang, Chang Liu, Bin Zhao, Te Li, Shaohua Wang, Xiao Tan, Gang Qiu

{"title":"Research on 3D monitoring method of tree barrier based on satellite remote sensing fusion of transmission tower features","authors":"Sihang Zhang, Xiaojun Dou, Zhi Yang, Chang Liu, Bin Zhao, Te Li, Shaohua Wang, Xiao Tan, Gang Qiu","doi":"10.1117/12.3014411","DOIUrl":"https://doi.org/10.1117/12.3014411","url":null,"abstract":"With the construction of city and the continuous expansion of power grid assets, transmission lines are increasingly radiating and expanding from urban areas to suburbs, mountainous areas, and even unmanned areas. Overhead transmission lines exposed in the wild are often susceptible to the impact of tree barriers. When the trees around the transmission line grow to a certain height, causing the distance between the wires and the trees to be too small, it can cause the wires to discharge from the trees, leading to accidents such as short circuits and trips. Therefore, the investigation of tree barriers is a highly concerned issue for various provincial companies. Based on the advantages of satellite remote sensing, such as wide coverage and unrestricted environmental conditions, this article proposes a three-dimensional monitoring method for tree obstacle based on satellites remote sensing images that integrates tower features. The effectiveness of the proposed method in this paper is verified by conducting experiments on a 500 kV transmission line in Chongqing and comparing it with unmanned aerial vehicle monitoring methods.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"60 3","pages":"1296911 - 1296911-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Airlight estimation in underwater image restoration 水下图像修复中的气光估计

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014363

Jinlei Chu, Zhanying Zhang, Dongsheng Yu, Weikai Fang, Yi Cai, Chidong Xu

引用次数: 0

Target allocation method based on multi-objective particle swarm optimization algorithm 基于多目标粒子群优化算法的目标分配方法

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014410

Qing Liu, Yunzheng Liu, Dexian Zeng

引用次数: 0

Face detection based on Yolov5 基于 Yolov 的人脸检测5

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014517

Jiahang Liu

引用次数: 0

An illumination adaptive underwater image enhancement method 一种光照自适应水下图像增强方法

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014373

Ruohan Zheng, Jianming Miao, Haosu Zhang, Xinyu Liu, Dongxu Tan

{"title":"An illumination adaptive underwater image enhancement method","authors":"Ruohan Zheng, Jianming Miao, Haosu Zhang, Xinyu Liu, Dongxu Tan","doi":"10.1117/12.3014373","DOIUrl":"https://doi.org/10.1117/12.3014373","url":null,"abstract":"In underwater imagery, issues such as non-uniform illumination, blurriness, and low contrast are prevalent, significantly impacting the quality of captured images. In recent years, numerous researchers have delved into underwater image processing. Due to the intricacies of underwater environments, low-light images have different requirements compared to well-illuminated ones. However, existing algorithms often struggle to address the non-uniform illumination issues stemming from various lighting conditions in underwater settings. They also lack the capability to adaptively enhance underwater images with varying brightness. To tackle these challenges, we propose an adaptive illumination enhancement method for underwater images. This algorithm offers the capability to adaptively enhance underwater images suffering from detail blurriness based on their original brightness. Furthermore, it dynamically adjusts the parameters of the gamma function using the image's illumination component to augment color contrast. Experimental results demonstrate that our approach outperforms other algorithms, as evidenced by superior scores in UIQM metric. It effectively addresses edge blurriness and non-uniform illumination issues prevalent in underwater images captured under varying lighting conditions.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"60 11","pages":"129691V - 129691V-8"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reversible data hiding in encrypted image based on adaptive difference prediction and block subdivision 基于自适应差分预测和区块细分的加密图像可逆数据隐藏技术

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014480

Xuesheng Zhang, Jing Wang

{"title":"Reversible data hiding in encrypted image based on adaptive difference prediction and block subdivision","authors":"Xuesheng Zhang, Jing Wang","doi":"10.1117/12.3014480","DOIUrl":"https://doi.org/10.1117/12.3014480","url":null,"abstract":"Reversible Data Hiding in Encrypted Images (RDHEI) embeds information while protecting the content of images from being leaked, allowing users to decrypt image content, extract embedded information, and losslessly recover the original content based on the key types they possess. It is a recent hot research area at the intersection of information hiding and encrypted computation, aiming to ensure both data security and the ability to hide information within images. However, inadequate utilization of image blocks in RDHEI results in a low embedding capacity of additional data. For this reason, this paper proposes a RDHEI based on adaptive difference prediction and block subdivision. At first, divide the image into equally sized blocks, and these blocks are encrypted to conceal the content of the image. For data hider, using adaptive the most significant bit(MSB) prediction to classify the available and unavailable blocks. Based on adaptive MSB prediction(AMP), adaptive difference prediction(ADP) is used to subdivide the unavailable blocks to vacate more room for data embedding. When receiver receives the embedded encrypted image, the embedded data or image can be decrypted according to its own key possession. Experimental results show that the proposed method has a significant effect on improving the embedding capacity.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"60 9","pages":"129690R - 129690R-8"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A prediction model for grain yield in Henan province based on BP neural network 基于 BP 神经网络的河南省粮食产量预测模型

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014503

Jun Xu, Yaru Yuan

引用次数: 0

Application of binocular structured light 3D measurement technology in refrigerator volume measurement 双目结构光三维测量技术在冰箱容积测量中的应用

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014414

Pengli Cheng

引用次数: 0

Comparative research on path planning algorithms for autonomous mobile robots based on ROS 基于 ROS 的自主移动机器人路径规划算法比较研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014664

Siyu Wang

引用次数: 0

Research on selective disassembly sequence planning based on graph model 基于图模型的选择性拆卸序列规划研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014520

dongmei Liu, Binfeng D. Lin, Yongfeng Li, V. Tarelnyk

引用次数: 0