2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)最新文献

Research on Product Style Design Based on Genetic Algorithm 基于遗传算法的产品风格设计研究

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177467

Cheng Yang, Lei Kong

引用次数: 1

Application of CEM Algorithm in the Field of Tunnel Crack Identification CEM算法在隧道裂缝识别中的应用

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177491

Bingqing Niu, Hongtao Wu, Ying Meng

引用次数: 1

Real-Time Measurement of Thread Number of Rail Fastener 钢轨紧固件螺纹数的实时测量

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177448

X. Luo, Ke-bin Jia, Pengyu Liu, Daoquan Xiong, Xiuchen Tian

引用次数: 0

3D Object Recognition Method Based on Improved Canny Edge Detection Algorithm in Augmented Reality 增强现实中基于改进Canny边缘检测算法的三维目标识别方法

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177488

Tianhang Gao, Zhenhao Yang

引用次数: 2

Deep Learning Approach in Gregg Shorthand Word to English-Word Conversion Gregg速记词到英语单词转换的深度学习方法

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177452

Dionis A. Padilla, Nicole Kim U. Vitug, Julius Benito S. Marquez

{"title":"Deep Learning Approach in Gregg Shorthand Word to English-Word Conversion","authors":"Dionis A. Padilla, Nicole Kim U. Vitug, Julius Benito S. Marquez","doi":"10.1109/ICIVC50857.2020.9177452","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177452","url":null,"abstract":"Shorthand or Stenography has been used in a variety of fields of practice, particularly by court stenographers. To record every detail of the hearing, a stenographer must write fast and accurate In the Philippines, the stenographers still used the conventional way of writing shorthand, which is by hand. Transcribing shorthand writing is time-consuming and sometimes confusing because of a lot of characters or words to be transcribed. Another problem is that only a stenographer can understand and translate shorthand writing. What if there is no stenographer available to decipher a document? A deep learning approach was used to implement and developed an automated Gregg shorthand word to English-word conversion. The Convolutional Neural Network (CNN) model used was the Inception-v3 in TensorFlow platform, an open-source algorithm used for object classification. The training datasets consist of 135 Legal Terminologies with 120 images per word with a total of 16,200 datasets. The trained model achieved a validation accuracy of 91%. For testing, 10 trials per legal terminology were executed with a total of 1,350 handwritten Gregg Shorthand words tested. The system correctly translated a total of 739 words resulting in 54.74% accuracy.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"35 1","pages":"204-210"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82796768","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: 10

Remote Sensing Scene Classification with Dual Attention-Aware Network 基于双注意感知网络的遥感场景分类

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177460

Yue Gao, Jun Shi, Jun Li, Ruoyu Wang

{"title":"Remote Sensing Scene Classification with Dual Attention-Aware Network","authors":"Yue Gao, Jun Shi, Jun Li, Ruoyu Wang","doi":"10.1109/ICIVC50857.2020.9177460","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177460","url":null,"abstract":"Remote sensing scene classification is of great importance to remote sensing image analysis. Most existing methods based on Convolutional Neural Network (CNN) fail to discriminate the crucial information from the complex scene content due to the intraclass diversity. In this paper, we propose a dual attention-aware network for remote sensing scene classification. Specifically, we use two kinds of attention modules (i.e. channel and spatial attentions) to explore the contextual dependencies from the channel and spatial dimensions respectively. The channel attention module intends to capture the channel-wise feature dependencies and further exploit the significant semantic attention. On the other hand, the spatial attention module aims to concentrate the attentive spatial locations and thus discover the discriminative parts inside the scene. The outputs of two attention modules are finally integrated as the attention-aware feature representation for improving classification performance. Experimental results on RSSCN7 and AID benchmark datasets show the effectiveness and superiority of the proposed methods for scene classification in remote sensing imagery.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"109 1","pages":"171-175"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88648111","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: 7

Air Quality Inference with Deep Convolutional Conditional Random Field 基于深度卷积条件随机场的空气质量推断

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177449

Zhe Luo, You Yu, Daijun Zhang, Shijie Feng, H. Yu, Yongxin Chang, Wei Shen

引用次数: 0

A Method to Improve the Lining Images Quality in Complex Tunnel Scenes 一种提高复杂隧道场景中衬砌图像质量的方法

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177462

Ying Meng, Hongtao Wu, Bingqing Niu

引用次数: 0

Multi-scale Fast Detection of Objects in High Resolution Remote Sensing Images 高分辨率遥感图像中目标的多尺度快速检测

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177484

Longwei Li, Jiangbo Xi, Wandong Jiang, Ming Cong, Ling Han, Yun Yang

{"title":"Multi-scale Fast Detection of Objects in High Resolution Remote Sensing Images","authors":"Longwei Li, Jiangbo Xi, Wandong Jiang, Ming Cong, Ling Han, Yun Yang","doi":"10.1109/ICIVC50857.2020.9177484","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177484","url":null,"abstract":"Objects detection in high resolution (HR) remote sensing images plays an important role in modern military, national defense, and commercial field. Because of a variety of object types and different sizes, it is difficulty to realize the rapid detection of multi-scale high resolution remote sensing objects, and provides support for succeeding decision making responses. This paper proposes a multi-scale fast detection method of remote sensing image objects with deep learning model, named YOLOv3. The COCO data model is used to establish the high resolution remote sensing image set based on the NWPU data. The proposed model can realize automatic learning of object features, which has good properties on generalization and robustness. It can also overcome the deficiency of traditional object detection method needing manual feature design for different objects. The experimental results show that the average detection accuracy of objects with different sizes in high resolution remote sensing images can reach 93.50%, which demonstrates that the proposed method can achieve rapid detection of different types of multi-scale objects.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"18 1","pages":"5-10"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84101658","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

Dictionary Learning for Visual Tracking with Dimensionality Reduction 基于降维的视觉跟踪字典学习

2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2020-07-01 DOI: 10.1109/ICIVC50857.2020.9177445

Jun Wang, Yuanyun Wang, Shaoquan Zhang, Chenguang Xu, Chengzhi Deng

{"title":"Dictionary Learning for Visual Tracking with Dimensionality Reduction","authors":"Jun Wang, Yuanyun Wang, Shaoquan Zhang, Chenguang Xu, Chengzhi Deng","doi":"10.1109/ICIVC50857.2020.9177445","DOIUrl":"https://doi.org/10.1109/ICIVC50857.2020.9177445","url":null,"abstract":"Recently, visual tracking has seen much progress in either accuracy or speed. However, due to drastic illumination variation, partial occlusion, scale variation and out-of-plane rotation, visual tracking remains a challenging task. Dealing with complicated appearance variations is an open issue in visual tracking. Existing trackers represent target candidates by a combination of target templates or previous tracking results under some constraints. When a drastic appearance variation occurs or some appearance variations occur simultaneously, such target representations are not robust. In this paper, we present a discriminative dictionary learning based target representation. A target candidate is represented via a linear combination of atoms in a learnt dictionary. The online dictionary learning can learn the appearance variations in tracking processing. So, the learnt dictionary can cover all of kinds of appearance variations. Based on this kind of target representation, a novel tracking algorithm is proposed. Extensive experiments on challenging sequences in popular tracking benchmark demonstrate competing tracking performances against some state-of-the-art trackers.","PeriodicalId":6806,"journal":{"name":"2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC)","volume":"7 1","pages":"251-255"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85812513","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