International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)最新文献_第4页

A new method for the extraction of shoreline based on point cloud distribution characteristics 基于点云分布特征的海岸线提取新方法

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014390

chao lv, weihua li, Jianglin Liu, jiuming li

{"title":"A new method for the extraction of shoreline based on point cloud distribution characteristics","authors":"chao lv, weihua li, Jianglin Liu, jiuming li","doi":"10.1117/12.3014390","DOIUrl":"https://doi.org/10.1117/12.3014390","url":null,"abstract":"The commonly used shoreline extraction methods need to go through the generation process of point cloud digital elevation model (DEM) with large amount of calculation and easy to introduce errors. This proposed method used the change law of the coordinate value of the point at the time of obtaining the data due to the consistent acquisition method, puts forward the algorithm, and improves the accuracy and efficiency of the algorithm, so that it can quickly and accurately extract the boundary point cloud of the coastal zone, properly process the extracted boundary point cloud and transform it into the coastline point cloud in the strict sense. Compared with the measured coastline data and the coastline data extracted by isoline tracking method, the visual and quantitative data show that the extraction effect of this method is more continuous and the accuracy is higher. It can be seen from the data table that the overall standard deviation and variance of coastline extracted by this method are reduced from 0.3726 and 0.1415 of isoline tracking method to 0.1632 and 0.0266 respectively","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"31 4","pages":"129692L - 129692L-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511372","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

Encryption and return method of inspection image of UAV converter station based on chaotic sequence 基于混沌序列的无人机换流站检测图像加密与返回方法

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014493

Jingxiang Li, Hao Lai, Yanhui Shi, Yuchao Liu, Haitao Yin

引用次数: 0

Design of intelligent security robot based on machine vision 基于机器视觉的智能安防机器人设计

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014397

Xueying Huang, Zhoulin Chang

引用次数: 0

A deep learning-based method for multiorgan functional tissue units segmentation 基于深度学习的多器官功能组织单元分割方法

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014697

Xinmei Feng, Zihao Hao, Shunli Gao, Gang Ma

{"title":"A deep learning-based method for multiorgan functional tissue units segmentation","authors":"Xinmei Feng, Zihao Hao, Shunli Gao, Gang Ma","doi":"10.1117/12.3014697","DOIUrl":"https://doi.org/10.1117/12.3014697","url":null,"abstract":"Functional tissue region segmentation is the segmentation and example description of tissue epithelium, glandular cavity, fiber and other tissues in the image, which helps to accelerate the understanding of the relationship between cells and tissues in the world. By better understanding the relationship between cells, researchers will have a deeper understanding of cell functions that affect human health. Based on convolutional neural networks, we combine the structural advantages of UNet and EficientNet to create an organ tissue segmentation model. The model fuses the UNet structure with the EficientNet structure, and extracts features with the help of the pre-trained EficientNet optimal structure to improve the ability of feature learning. At the same time, the fusion of multi-scale features in the network is realized through the jump connection, and the segmentation accuracy of the model is improved. We compare our model with other models using the metrics of dice similarity efficiency. our Unet2.5D (ConvNext+ Se_resnet101) owns the highest DSC 0.702 among these models, which is 0.052, 0.024, 0.052 higher than Unet(ResNet50), Unet(Se_Resnet101), Unet(ResNet101) respectively.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"88 3","pages":"129691Y - 129691Y-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511876","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

Multi-level image detail enhancement based on guided filtering 基于引导滤波的多级图像细节增强技术

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014387

Xiangrui Tian, Yinjun Jia, Tong Xu, Jie Yin, Yihe Chen, Jiansen Mao

{"title":"Multi-level image detail enhancement based on guided filtering","authors":"Xiangrui Tian, Yinjun Jia, Tong Xu, Jie Yin, Yihe Chen, Jiansen Mao","doi":"10.1117/12.3014387","DOIUrl":"https://doi.org/10.1117/12.3014387","url":null,"abstract":"Image blur and detail information loss are caused by various factors such as imaging environment and hardware performance, therefore a multi-level image detail enhancement method based on guided filtering is proposed. Firstly, the input image is iteratively filtered by using the guided filter, to obtain background images with different smoothness; then the background image is subtracted from the original image to obtain detail images with different levels; finally, a dynamic saturation function is used to adjust the weights of detail images, which are superimposed with the original image to obtain the enhanced image. The proposed method is compared with the existing enhancement algorithms using open dataset. The experimental results show that, compared with other enhancement methods, the proposed method in this paper achieves a better enhancement effect, the enhanced image has clear edges, and the visual effect is suitable. Compared with other methods, the objective indicators of information entropy, average gradient, and spatial frequency are improved on average. 1.39%, 27.9%, and 19.3%.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"13 s4","pages":"1296918 - 1296918-8"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640410","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

Research on system capability assessment algorithms 系统能力评估算法研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014565

Lanlan Gao, Yijing Liu, Jian Le, Kai Qiu

引用次数: 0

Health assessment of marine gas turbine propulsion system under cross-working conditions based on transfer learning 基于迁移学习的交叉工作条件下船用燃气轮机推进系统健康评估

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014466

Congao Tan, Shijie Shi

引用次数: 0

Deep learning-based 3D target detection algorithm 基于深度学习的 3D 目标检测算法

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014381

Chunbao Huo, Ya Zheng, Zhibo Tong, Zengwen Chen

{"title":"Deep learning-based 3D target detection algorithm","authors":"Chunbao Huo, Ya Zheng, Zhibo Tong, Zengwen Chen","doi":"10.1117/12.3014381","DOIUrl":"https://doi.org/10.1117/12.3014381","url":null,"abstract":"Automated vehicle driving requires a heightened awareness of the surrounding environment, and detecting targets is a crucial element in reducing the risk of traffic accidents. Target detection is essential for autonomous driving. In this paper, we improve the CenterPoint 3D target detection algorithm by introducing a self-calibrating convolutional network into the 2D backbone network of the original algorithm. This enhancement improves network extraction speed and feature extraction capability. Additionally, we improve the two-stage refinement module of the original algorithm by extracting feature points from the multi-scale feature map rather than the single-scale feature map. This approach reduces the loss of small target feature information, and we build a data enhancement module to increase the number of training samples and improve the network model’s robustness. We validate the algorithm on the KITTI dataset and analyze domestic data visualizations. Our results show that the bird’s-eye view mAP detection accuracy of the algorithm when the target is a vehicle has improved by 1.68%, and the 3D target mAP detection accuracy has improved by 1.02% compared with the original algorithm.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"54 2","pages":"129690V - 129690V-8"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511434","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

Ship and course detection in remote sensing images based on key-point extraction 基于关键点提取的遥感图像中的船舶和航线探测

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014532

Tao Zhang, Xiaogang Yang, Ruitao Lu, Qi Li, Wenxin Xia, Shuang Su, Bin Tang

{"title":"Ship and course detection in remote sensing images based on key-point extraction","authors":"Tao Zhang, Xiaogang Yang, Ruitao Lu, Qi Li, Wenxin Xia, Shuang Su, Bin Tang","doi":"10.1117/12.3014532","DOIUrl":"https://doi.org/10.1117/12.3014532","url":null,"abstract":"Remote sensing image ship target detection and course discrimination is one of the important supports for building a maritime power. Since ship target in remote sensing images are generally in strips, the IOU score is very sensitive to the angle of bounding box. Moreover, the angle of the ship is a periodic function, this discontinuity will cause performance degeneration. Meanwhile, methods generally use oriented bounding boxes as anchors to handle rotated ship target and thus introduce excessive hyper-parameters such as box size, aspect ratios. Aiming at the problem of complex calculation of anchor frame traversal mechanism and discontinuity of angle regression caused by increasing angle attribute in ship target detection of remote sensing image, a ship target heading detection method based on ship head point is proposed. The discontinuous angle regression problem is transformed into a continuous key point estimation problem, and the ship target detection and heading recognition are unified. Second, CA attention mechanism is added to the feature extraction network to enhance the attention to the ship target and predict the center point of the ship target. The offset and target width at the center point are regressed. Then, return the heading point and offset to obtain the accurate heading point position. Next, the rotation angle of the ship is determined according to the coordinates of the center point and the ship head point. Combined with the predicted width and height of the ship, the rotation frame detection of the ship target is completed. Finally, the center point and the bow point are connected to determine the course of the ship target. The effectiveness of the proposed method is verified on the RFUE and open source HRSC2016 datasets, respectively, and it also has good robustness in complex environments.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"59 6","pages":"129691N - 129691N-13"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511350","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

Research on digital remodeling of structural features of cigarette filter rod based on 3D digital twin technology 基于三维数字孪生技术的卷烟滤嘴杆结构特征数字化重塑研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014423

Ke Huang, Guangyuan Yang, Shenghua Zhang, Zheming Li, Hu Li, Xuebin Jiang, Wenting Liu, Wenfeng Liu, Bo Wang, Xin Yan, Weiguo Lin

{"title":"Research on digital remodeling of structural features of cigarette filter rod based on 3D digital twin technology","authors":"Ke Huang, Guangyuan Yang, Shenghua Zhang, Zheming Li, Hu Li, Xuebin Jiang, Wenting Liu, Wenfeng Liu, Bo Wang, Xin Yan, Weiguo Lin","doi":"10.1117/12.3014423","DOIUrl":"https://doi.org/10.1117/12.3014423","url":null,"abstract":"(1) Background: To improve the quality control ability of cigarette characteristic cigarette filter rods, build the filter rod structure database of cigarette products, and accelerate the Digital transformation of filter rod platform technology research and development, this study proposed a research method for digital remodeling of cigarette structure.; (2) Methods: Firstly, image acquisition of the cigarette filter rod end face is carried out by combining a color area-array camera, telecentric lens, and coaxial light source to form an area-array camera testing environment; By combining a line scanning camera with a coaxial light source and a point light source, the surface of the cigarette filter is photographed and the point light source transmittance is detected; Simultaneously combining 3D laser camera scanners to establish the contour of the target; (3) Results: By using the above methods, the detection images of four types of filter rods were processed to obtain HSV image curves, grayscale images, and color histograms. These images were used for 3D model reconstruction and 15 3D feature maps were obtained; (4) Conclusions: The reconstructed 15 3D images can accurately distinguish four different filter rods, and this method can provide a reference for real-time detection during cigarette filter rod processing.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"91 3","pages":"1296920 - 1296920-8"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511869","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