Full-Text Search:
Home|Journal Papers|About CNKI|User Service|FAQ|Contact Us|中文
《Acta Geodaetica et Cartographica Sinica》 2018-06
Add to Favorite Get Latest Update

Machine Vision Special Issue:Building Match Graph Using Deep Convolution Feature for Structure from Motion

WAN Jie;Alper YILMAZ;The Institute of Remote Sensing and Geographic Information System,the School of Earth and Space Sciences,Peking University,Beijing Key Laboratory of Spatial Information Integration and 3S Engineering Application;Department of Civil,Environment and Geodetic Engineering,Ohio State University;  
Image matching in an unordered image dataset is quite time-consuming for structure from motion(SfM)due to image matching by comparing features and large number of matches between all image pairs.To reduce matching times,deep convolution feature(DCF)is proposed to create image match graph in this paper.Firstly,the convolutional feature map of an image is extracted using the VGG-16 convolutional neural network trained on ImageNet.Then,the sum pooling is used to process the feature map.Finally,the vector is normalized and used to represent the image.The similarities between an image and all other images is calculated by calculating the distances between these feature vectors.Thus,the match graph is constructed by selecting the top10 images with highest similarities.The experiment results showed that the proposed DCF can create the match graph effectively,find the potential image pairs.On the Urban and South Building datasets,the results of the SfM reconstruction based on the match graph created by the proposed DCF are almost the same as those of the exhaustive matching,but the number of matches are reduced by 97.4% and 92.1%,respectively.At the same time,the match graph created by the proposed DCF is obviously better than the match graph crated by the DBoW3 in the most advanced SLAM system.
【Fund】: 国家自然科学基金(41571432)~~
【CateGory Index】: P237
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
©2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reserved