Proceedings of the 2019 3rd International Conference on Advances in Image Processing最新文献

Grammar Inference with Multiparameter Genetic Model 基于多参数遗传模型的语法推理

Proceedings of the 2019 3rd International Conference on Advances in Image Processing Pub Date : 2019-11-08 DOI: 10.1145/3373419.3373444

P. Grachev

引用次数: 1

Improved U-Net for Guidewire Tip Segmentation in X-ray Fluoroscopy Images 改进U-Net用于x射线透视图像导丝尖端分割

Proceedings of the 2019 3rd International Conference on Advances in Image Processing Pub Date : 2019-11-08 DOI: 10.1145/3373419.3373449

Shuai Guo, Songyuan Tang, Jianjun Zhu, Jingfan Fan, Danni Ai, Hong Song, P. Liang, Jian Yang

{"title":"Improved U-Net for Guidewire Tip Segmentation in X-ray Fluoroscopy Images","authors":"Shuai Guo, Songyuan Tang, Jianjun Zhu, Jingfan Fan, Danni Ai, Hong Song, P. Liang, Jian Yang","doi":"10.1145/3373419.3373449","DOIUrl":"https://doi.org/10.1145/3373419.3373449","url":null,"abstract":"In percutaneous coronary intervention (PCI), physicians use a guidewire tip to implant stents in vessels with stenosis. Given the small scale and low signal-to-noise ratio of guidewire tips in X-ray fluoroscopy images, physicians experience difficulty in recognizing and locating the tip. The automatic segmentation of the guidewire tip can ease navigation when the physicians implant stents for PCI. In this paper, we propose an end-to-end convolutional neural network-based method for guidewire tip segmentation. The network framework is derived from U-Net, and two specific designs involving reduced dense block and connectivity supervision are embedded in the framework to improve the accuracy and robustness of guidewire tip segmentation. Experiments are performed on clinical data. The proposed method achieves mean sensitivity, F1-score, Jaccard index, Hausdorff distance of 92.95%, 91.35%, 84.14%, and 0.531 mm on testing data, respectively. In addition, the segmentation time is 0.02 s/frame, which can satisfy the requirements for clinical intra-practice.","PeriodicalId":352528,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Advances in Image Processing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130985915","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: 9

The identification of Impervious Area from Sentinel-2 Imagery Using A Novel Spectral Spatial Residual Convolution Neural Network 基于空间残差卷积神经网络的Sentinel-2影像不透水区域识别

Proceedings of the 2019 3rd International Conference on Advances in Image Processing Pub Date : 2019-11-08 DOI: 10.1145/3373419.3373459

Zhiwen Zhang, Linlin Xu, Qixin Liu

{"title":"The identification of Impervious Area from Sentinel-2 Imagery Using A Novel Spectral Spatial Residual Convolution Neural Network","authors":"Zhiwen Zhang, Linlin Xu, Qixin Liu","doi":"10.1145/3373419.3373459","DOIUrl":"https://doi.org/10.1145/3373419.3373459","url":null,"abstract":"With the rapid increasing of urban areas, impervious surfaces play an important role as an indicator of urban development and the change of the city's environment. Due to the wide variety of materials of impervious surfaces, it is an arduous task to draw impervious surfaces. Fortunately, the Sentinel-2 satellite provides accessible multi-spectral imagery with a high spatial resolution to solve this problem. However, huge volumes of Sentinel-2 imagery produced every 5 days need a fast and accurate classifier for impervious mapping. In this paper, a novel spectral spatial residual convolution neural network (SSRCNN) has been designed to deal with the massive imagery for impervious classification with high speed and accuracy. Compared to typical algorithms, deep learning methods are more suitable in this task. The CNN demonstrates great success in image classification. In this study, a comparison between CNN and SSRCNN has been done, and the result shows that the SSRCNN model outperforms the CNN model by about 0.74 percent in terms of overall classification accuracy (OA). The use of the NVIDIA 1080Ti graphics processing unit (GPU) can improve the computational efficiency of the SSRCNN model.","PeriodicalId":352528,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Advances in Image Processing","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131185975","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

Path Planning and Simulation of Helicopter Power Patrolling 直升机动力巡逻路径规划与仿真

Proceedings of the 2019 3rd International Conference on Advances in Image Processing Pub Date : 2019-11-08 DOI: 10.1145/3373419.3373426

Zhi-wei Xing, Z. Tan, Fuqiang Xin

引用次数: 1

Petroleum Production Forecasting Based on Machine Learning 基于机器学习的石油产量预测

Proceedings of the 2019 3rd International Conference on Advances in Image Processing Pub Date : 2019-11-08 DOI: 10.1145/3373419.3373421

Wei Liu, W. Liu, Jianwei Gu

{"title":"Petroleum Production Forecasting Based on Machine Learning","authors":"Wei Liu, W. Liu, Jianwei Gu","doi":"10.1145/3373419.3373421","DOIUrl":"https://doi.org/10.1145/3373419.3373421","url":null,"abstract":"Reservoir numeric simulation is the most commonly used method for oilfield petroleum production forecasting, but its accuracy is based on accurate geological models and high-quality history matching. In order to overcome the shortcomings of numeric simulation requires, like time consuming, high cost, and lot of data required, an machine learning method was adopted and trained for predicting oilfield production using static and dynamic developing parameters. Since the traditional BP neural networks cannot accurately capture the time correlation between data, a long short-term memory model was used to establish production prediction model that can consider the trends and context correlations of production data. Mean Decrease Impurity method was first conducted to analyze the relative importance of predictor variables. Relative unimportant features then can be excluded according to their relative importance. The dimension reduction of predictor variables was combined with production data to train and optimize LSTM network. Thereby predictive model for production prediction was established after the training. The actual oilfield data was used to verify the proposed approach and conducting application effect analysis. The results show that the predicted production computed by LSTM network is highly consistent with the actual production, which can accurately reflect the dynamic variation of production.","PeriodicalId":352528,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Advances in Image Processing","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132867890","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: 12

Research on Tourism Bus Route Optimization Based on Ant Colony Algorithm 基于蚁群算法的旅游巴士路线优化研究

Proceedings of the 2019 3rd International Conference on Advances in Image Processing Pub Date : 2019-11-08 DOI: 10.1145/3373419.3373435

Mingyan Li, Qiuli Qin, Kun Fan

引用次数: 1

Single Color Image Dehazing Based on Two Fast Variational Models 基于两种快速变分模型的单色图像去雾

Proceedings of the 2019 3rd International Conference on Advances in Image Processing Pub Date : 2019-11-08 DOI: 10.1145/3373419.3373453

Zhuzhu Gao, Weibo Wei, Zhenkuan Pan, Shengnan Zhao, Shuai Li

引用次数: 1

An Improved Method of Detecting Infrared Weak and Small Targets Based on Deep Learning 一种基于深度学习的红外弱小目标检测改进方法

Proceedings of the 2019 3rd International Conference on Advances in Image Processing Pub Date : 2019-11-08 DOI: 10.1145/3373419.3373450

Tianwei Yang, Jungang Yang, W. An

引用次数: 1

Rotation & Viewpoint Angle Prediction in Capsule Network 胶囊网络中的旋转与视角预测

Proceedings of the 2019 3rd International Conference on Advances in Image Processing Pub Date : 2019-11-08 DOI: 10.1145/3373419.3373463

Husein Sulianto

引用次数: 3

Understanding Data Correlations in Continuous Casting Systems for Autonomous Fixed Weight Cutting 了解自主定重切割连铸系统中的数据相关性

Proceedings of the 2019 3rd International Conference on Advances in Image Processing Pub Date : 2019-11-08 DOI: 10.1145/3373419.3373427

Haodi Ping, Yongcai Wang, Haoran Feng, Lifeng Qiao, Wenping Chen, Deying Li

{"title":"Understanding Data Correlations in Continuous Casting Systems for Autonomous Fixed Weight Cutting","authors":"Haodi Ping, Yongcai Wang, Haoran Feng, Lifeng Qiao, Wenping Chen, Deying Li","doi":"10.1145/3373419.3373427","DOIUrl":"https://doi.org/10.1145/3373419.3373427","url":null,"abstract":"Continuous casting is the process whereby molten metal is solidified and cut into fixed weight billets. The key requirement is to cut the billets into fixed weight, so that the subsequent rolling steps can roll the billets into high quality fixed diameter, fixed length mills while avoiding wasting or insufficiency of the metal materials. To accomplish this goal, existing casting systems exploit camera systems to measure the cutting length and to control the flame cutter to cut the hot billet, which is called Length-based Cutting using Weight Feedback control (LCWF) approach. However, LCWF approach still provide unsatisfactory cutting performances in production, because the billet weight depends not only on the cutting length, but also on the billet temperature, density, cutting errors, and the billet dragging speed etc. To further improve the cutting weight accuracy, a data driven approach is necessary to investigate how the various features in the continuous casting system impact the cutting errors. In this paper, data mining on real datasets collected from Tangshan Iron company is conducted. We mine data features and data correlations with the cutting errors. Suggestions on how to improve the cutting accuracy using online learning approach are also provided.","PeriodicalId":352528,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Advances in Image Processing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129483709","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