2018 International Conference on Audio, Language and Image Processing (ICALIP)最新文献_第4页

Offshore Oil Slicks Extraction by Landsat Data Based on eCognition Software in South China Sea 基于识别软件的南海海上浮油Landsat数据提取

2018 International Conference on Audio, Language and Image Processing (ICALIP) Pub Date : 2018-07-01 DOI: 10.1109/ICALIP.2018.8455845

Xu Xing, Jinli Shen

{"title":"Offshore Oil Slicks Extraction by Landsat Data Based on eCognition Software in South China Sea","authors":"Xu Xing, Jinli Shen","doi":"10.1109/ICALIP.2018.8455845","DOIUrl":"https://doi.org/10.1109/ICALIP.2018.8455845","url":null,"abstract":"Extraction of oil slicks from remote sensing data is a basic work for investigation of oil spill and natural oil leakage. It is time-consuming and laborious when the traditional method of artificial interpretation is applied to tens of thousands of remote sensing data. In order to improve the efficiency of oil slicks extraction, eCognition, object-oriented image analysis software, is introduced to set up a rule set which can realize the batch processing of oil slick extraction. Landsat TM data of known oil seep site from Gulf of Mexico is used as training data to set up the rule set. Following the several processes such as image segmentation, hierarchical classification, training Sample selection, feature space optimization and nearest neighbor classification, oil slicks and several other sea surface targets were extracted in the format of shape within ten minutes. The result shows that the oil extraction based on eCognition rule set is faster and more accurate than manual interpretation. When the rule set was applied to the sea area of South China Sea, there were two significant discoveries. One is that the pollution of the white tiger oil field is very serious. Oil slicks can be detected on almost all available Landsat TM data. Another is that the first natural oil and gas seep is come to light in South China Sea.","PeriodicalId":241122,"journal":{"name":"2018 International Conference on Audio, Language and Image Processing (ICALIP)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134497650","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

Chinese Image Caption Based on Deep Learning 基于深度学习的中文图像标题

2018 International Conference on Audio, Language and Image Processing (ICALIP) Pub Date : 2018-07-01 DOI: 10.1109/ICALIP.2018.8455856

Ziyue Luo, Huixian Kang, P. Yao, W. Wan

引用次数: 2

Evaluating the Effect of Transitions on the Viewing Experience for VR Video 过渡对VR视频观看体验的影响评估

2018 International Conference on Audio, Language and Image Processing (ICALIP) Pub Date : 2018-07-01 DOI: 10.1109/ICALIP.2018.8455352

Tingting Zhang, Feng Tian, Xiaofei Hou, Qirong Xie, Fei Yi

引用次数: 3

ICALIP 2018

2018 International Conference on Audio, Language and Image Processing (ICALIP) Pub Date : 2018-07-01 DOI: 10.1109/icalip.2018.8455275

引用次数: 0

ICALIP2018 Table of Contents ICALIP2018目录

2018 International Conference on Audio, Language and Image Processing (ICALIP) Pub Date : 2018-07-01 DOI: 10.1109/icalip.2018.8455739

Danbei Wang, Zhengzheng Cui, Huiming, Ding, Shuqi Yan, Zhifeng Xie, Kaiyue Li, Lizhen Shi, Qunfei Zhao, Yun Long, Yuzhang Wang, Feng Tian, Yunwen Zhu, Ruiwen Hu, Zhao, Chen, Xingxing, Yu, Guangchen, Junfeng Zhou, Zhuochen Lei, Xiaoqing Yu

引用次数: 0

Template Oriented Text Summarization via Knowledge Graph 基于知识图谱的模板文本摘要

2018 International Conference on Audio, Language and Image Processing (ICALIP) Pub Date : 2018-07-01 DOI: 10.1109/ICALIP.2018.8455241

Pin Wu, Quan Zhou, Zhidan Lei, Weijian Qiu, Xiaoqiang Li

{"title":"Template Oriented Text Summarization via Knowledge Graph","authors":"Pin Wu, Quan Zhou, Zhidan Lei, Weijian Qiu, Xiaoqiang Li","doi":"10.1109/ICALIP.2018.8455241","DOIUrl":"https://doi.org/10.1109/ICALIP.2018.8455241","url":null,"abstract":"People are flooded with massive semi-structured and unstructured texts in their daily work life. The fast-paced lifestyle has forced us to get more focused information from these large amounts of text more quickly. So people urgently need a technology that can automatically extract abstracts from text. The traditional extractive automatic abstract method can only extract keywords or key sentences. Although the current popular sequence-to-sequence extraction methods have greatly improved compared with the traditional methods, they cannot be combined with the background information to obtain higher level abstraction. Therefore, we propose a method based on knowledge graph technology to automatically extract abstract texts. This method can not only obtain higher-level extraction from the text, but also can select template and question and answer to obtain a personalized abstract. We experimented on the CNN DAILYMAIL dataset. The results show that the abstract obtained by this method can reflect more textual information, and more in line with human reading habits, and can achieve personalized extraction, and can obtain close to the best ROUGE index results.","PeriodicalId":241122,"journal":{"name":"2018 International Conference on Audio, Language and Image Processing (ICALIP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126109682","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: 11

Outdoor Navigation with Handheld Augmented Reality 手持增强现实户外导航

2018 International Conference on Audio, Language and Image Processing (ICALIP) Pub Date : 2018-07-01 DOI: 10.1109/ICALIP.2018.8455285

Lianyao Wu, Xiaoqing Yu

引用次数: 4

Post-Secondary Filtering Improvement of GSC Beamforming Algorithm GSC波束形成算法的二次后滤波改进

2018 International Conference on Audio, Language and Image Processing (ICALIP) Pub Date : 2018-07-01 DOI: 10.1109/ICALIP.2018.8455622

Lu Zhang, Mingjiang Wang, Qiquan Zhang, Hu Chen

引用次数: 0

Region-Based Convolutional Neural Networks for Profiled Fiber Recognition 基于区域的卷积神经网络用于轮廓纤维识别

2018 International Conference on Audio, Language and Image Processing (ICALIP) Pub Date : 2018-07-01 DOI: 10.1109/ICALIP.2018.8455689

Zhao Chen, Xinxin Wang, Yuxin Zheng, Yan Wan

{"title":"Region-Based Convolutional Neural Networks for Profiled Fiber Recognition","authors":"Zhao Chen, Xinxin Wang, Yuxin Zheng, Yan Wan","doi":"10.1109/ICALIP.2018.8455689","DOIUrl":"https://doi.org/10.1109/ICALIP.2018.8455689","url":null,"abstract":"Profiled fiber recognition using electron-microscopic (EM) images is one of the main tasks in textile quality inspections. However, many existing methods require fiber samples being located and segmented from their EM image before fed to classifiers manually. Delicate spatial patterns such as sample overlap and shape variability among the same kind of fibers cannot be dealt with by classic pattern recognition methods. As an advanced object detection method, region-based convolutional neural networks (R-CNN) shows great potentials in profiled fiber recognition. Adapting the framework of R-CNN, we carry out the task in three major steps: hierarchical segmentation by selective search, automatic generation of fiber samples by self-designed selection rules and cascade classification by CNN along with some other classifiers. Within the system, the selection rules of region proposals recommended by the selective search method are tailored to spatial features of fiber samples, which are fed to a fine-tuned deep-structured classification network to recognize multiple shapes of fibers. The experimental results indicate the proposed method outperforms the backpropagation network (BP) and the support vector machine (SVM), especially when cascade classification is employed.","PeriodicalId":241122,"journal":{"name":"2018 International Conference on Audio, Language and Image Processing (ICALIP)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127193516","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

An Efficient 3D Reconstruction System for Chinese Ancient Architectures 一种高效的中国古建筑三维重建系统

2018 International Conference on Audio, Language and Image Processing (ICALIP) Pub Date : 2018-07-01 DOI: 10.1109/ICALIP.2018.8455527

Hao Xu, Yanjun Jin, W. Wan

引用次数: 1