International Conference on Digital Image Processing最新文献_第7页

Multi-visual information fusion and aggregation for video action classification 面向视频动作分类的多视觉信息融合与聚合

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644312

Xuchao Gong, Zongmin Li, Xiangdong Wang

引用次数: 0

A defect detection method for plastic gears based on deep learning and machine vision 基于深度学习和机器视觉的塑料齿轮缺陷检测方法

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644273

Y. Hao, Meng Xiang, Zichao Zhu

引用次数: 0

Reference-driven undersampled MRI reconstruction using automated stopping deep image prior 使用自动停止深度图像先验的参考驱动欠采样MRI重建

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644282

Guisong Wang, Xiaofeng Du, Yanhua Qin, Yifan He

引用次数: 0

Tobacco plant disease dataset 烟草植物病害数据集

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644288

Hong Lin, Rita Tse, Su-Kit Tang, Z. Qiang, Jinliang Ou, Giovanni Pau

{"title":"Tobacco plant disease dataset","authors":"Hong Lin, Rita Tse, Su-Kit Tang, Z. Qiang, Jinliang Ou, Giovanni Pau","doi":"10.1117/12.2644288","DOIUrl":"https://doi.org/10.1117/12.2644288","url":null,"abstract":"Tobacco is a valuable plant in agricultural and commercial industry. Any disease infection to the plant may lower the harvest and interfere the operation of supply chain in the market. Image-based deep learning methods are cutting-edge technologies that can facilitate the diagnosis of diseases efficiently and effectively when large-scale dataset is available for training. However, there is not a public dataset about tobacco currently. A comprehensive dataset is appealed to take advantage of deep learning methods in tobacco cultivation urgently. In this paper, we propose to create a specific dataset for tobacco diseases, called Tobacco Plant Disease Dataset (TPDD). 2721 tobacco leaf images are taken in field. The dataset serves for two purposes: disease classification and leaf detection. For classification, we identify 12 classes and provide two types of disease annotations: 1) Whole Leaf Section; 2) Disease Fragment Section. For leaf detection, we provide two kinds of bounding box: rectangle bounding box and polygon bounding box. In addition, we conduct baseline experiments to illustrate the usefulness of TPDD: 1) using deep learning model to detect single disease and multiple diseases; 2) using YOLO-v3 and Mask-RCNN to detect leaves. We hope that the dataset could support the tobacco industry, also be a benchmark in fine-grained vision classification.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127360859","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

Measuring system for elongation at break of cable insulation sheath based on machine vision 基于机器视觉的电缆绝缘护套断裂伸长率测量系统

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643116

X. Su, Gangwei Wang, Zhiqiang Zhang, Jiale Yang, Zhijia Zhang

{"title":"Measuring system for elongation at break of cable insulation sheath based on machine vision","authors":"X. Su, Gangwei Wang, Zhiqiang Zhang, Jiale Yang, Zhijia Zhang","doi":"10.1117/12.2643116","DOIUrl":"https://doi.org/10.1117/12.2643116","url":null,"abstract":"In the production of power cables, the performance test of the cable insulation sheath is an important part. Compared with traditional testing methods, machine vision has the advantages of stable operation, high precision, and high efficiency. Because of this situation, firstly, based on machine vision theory, the structure of the old-fashioned tensile machine was reconstructed, and the whole tensile test process of the cable insulation sheath test was imaged by a CMOS camera, and the color recognition algorithm, effective area segmentation algorithm, and workpiece were proposed. The fracture judgment detection algorithm and the corrosion difference algorithm are used to calculate the distance between the marked lines and then calculate the elongation at the break of the cable material. Through systematic experiments on the same batch of cable jackets, the deviation of the elongation at break measured by visual inspection is the largest, no more than 1%. The experimental results and practical applications show that the machine vision-based visual inspection system has higher accuracy, faster efficiency, and more stable and reliable operation than the traditional inspection system.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"12342 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129415939","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

3D face alignment and face reconstruction based on image sequence 基于图像序列的三维人脸对齐与重构

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644477

Y. Wei, Biao Qiao, Hua-bin Wang, Mengxin Zhang, Shijun Liu, L. Tao

引用次数: 0

Semantic segmentation of road scene based on multi-scale feature extraction and deep supervision 基于多尺度特征提取和深度监督的道路场景语义分割

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644695

Longfei Wang, Chunman Yan

{"title":"Semantic segmentation of road scene based on multi-scale feature extraction and deep supervision","authors":"Longfei Wang, Chunman Yan","doi":"10.1117/12.2644695","DOIUrl":"https://doi.org/10.1117/12.2644695","url":null,"abstract":"Aiming at the problems of inaccurate segmentation edges, poor adaptability to multi-scale road targets, prone to false segmentation and missing segmentation when segmenting road targets with various and changeable occlusions in the traditional U-Net model, a semantic segmentation model of road scene based on multi-scale feature extraction and deep supervision module is proposed. Firstly, the dual attention module is embedded in the U-Net encoder, which can make the model have the ability to capture the context information of channel dimension and spatial dimension in the global range, and enhance the road features; Secondly, before upsampling, the feature map containing high-level semantic information is input into ASPP module to obtain road features of different scales; Finally, the deep supervision module is introduced into the upsampling part to learn the feature representation at different levels and retain more road detail features. Experiments are carried out on CamVid dataset and Cityscapes dataset. The results show that our Network can effectively segment road targets with different scales, and the segmented road contour is more complete and clear, which improves the accuracy of semantic segmentation while ensuring a certain segmentation speed.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122639747","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: 2

Cluster-based point cloud attribute compression using inter prediction and graph Fourier transform 基于聚类的点云属性压缩，采用内部预测和图傅里叶变换

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644218

Jiaying Liu, Jin Wang, Longhua Sun, Jie Pei, Qing Zhu

{"title":"Cluster-based point cloud attribute compression using inter prediction and graph Fourier transform","authors":"Jiaying Liu, Jin Wang, Longhua Sun, Jie Pei, Qing Zhu","doi":"10.1117/12.2644218","DOIUrl":"https://doi.org/10.1117/12.2644218","url":null,"abstract":"With the rapid development of 3D capture technologies, point cloud has been widely used in many emerging applications such as augmented reality, autonomous driving, and 3D printing. However, point cloud, used to represent real world objects in these applications, may contain millions of points, which results in huge data volume. Therefore, efficient compression algorithms are essential for point cloud when it comes to storage and real-time transmission issues. Specially, the attribute compression of point cloud is still challenging owing to the sparsity and irregular distribution of corresponding points in 3D space. In this paper, we present a novel point cloud attribute compression scheme based on inter-prediction of blocks and graph Laplacian transforms for attributes residual. Firstly, we divide the entire point cloud into adaptive sub-clouds via K-means based on the geometry to acquire sub-clouds, which enables efficient representation with less cost. Secondly, the sub-clouds are divided into two parts, one is the attribute means of the sub clouds, another is the attribute residual by removing the means. For the attribute means, we use inter-prediction between sub-clouds to remove the attribute redundancy, and the attribute residual is encoded after graph Fourier transforming. Experimental results demonstrate that the proposed scheme is much more efficient than traditional attribute compression schemes.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115594123","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: 1

Automatic heart segmentation based on convolutional networks using attention mechanism 基于注意机制的卷积神经网络自动心脏分割

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643378

Guodong Zhang, Yu Liu, Wei Guo, Wenjun Tan, Zhaoxuan Gong, M. Farooq

引用次数: 0

Radio frequency interference suppression based on two-dimensional frequency domain notch for P-band ultra-wideband SAR 基于二维频域陷波的p波段超宽带SAR射频干扰抑制

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643264

Kang Liang, Hongtu Xie, Xinqiao Jiang, Xiao Hu, Kaipeng Chen, Guoqian Wang

{"title":"Radio frequency interference suppression based on two-dimensional frequency domain notch for P-band ultra-wideband SAR","authors":"Kang Liang, Hongtu Xie, Xinqiao Jiang, Xiao Hu, Kaipeng Chen, Guoqian Wang","doi":"10.1117/12.2643264","DOIUrl":"https://doi.org/10.1117/12.2643264","url":null,"abstract":"P-band ultra-wideband synthetic aperture radar (UWB SAR) not only has the characteristics of the high-resolution imaging, but also has the well capability of the foliage penetrating, which is potential of detecting and imaging the concealed target under the vegetation. However, there are a lot of the radio, television and mobile communication signals in the P-band, which are called as the radio frequency interference (RFI) signals. These RFI signals are mixed with target echo signals, which will cause the serious interference in the P-band UWB SAR imaging. The traditional notch method is easy to implement the RFI suppression, so it has been widely used. However, the traditional notch method is to notch each pulse echo individually, which has a high computational complexity. At the same time, the RFI suppression of each pulse echo separately will always lead to a large amount of the residual interference, so the traditional notch method has the poor RFI suppression effect. Based on the traditional notch method, this paper proposes an RFI suppression method based on the two-dimensional frequency domain (2DFD) notch, which can realize one-time processing of all echo pulses so that improve the efficiency of the RFI suppression. Meanwhile, because the bandwidth of the RFI signal is much smaller than that of the SAR echo signal, converting the received SAR echo signal to the 2DFD can further concentrate the energy of the RFI signals, so it has the better RFI suppression effect. The simulation results show that the proposed RFI suppression method based on the 2DFD notch can not only improve the efficiency of the RFI suppression but also have the better effect of the RFI suppression.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"617 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115827208","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