結合像素差異法與SURF之景深量測系統

dc.contributor許陳鑑zh_TW
dc.contributorChen-Chien Hsuen_US
dc.contributor.author蔡宗翰zh_TW
dc.contributor.authorZong-Han Caien_US
dc.date.accessioned2019-09-03T10:49:15Z
dc.date.available2015-8-23
dc.date.available2019-09-03T10:49:15Z
dc.date.issued2012
dc.description.abstract本文提出一種結合像素差異法與SURF演算法之景深量測系統。方法是使用單一相機,利用不同拍攝距離所產生的影像畫面,結合SURF演算法匹配兩張影像,接著用ICF演算法移除較差的SURF匹配點,以完成自動選擇參考點。藉由在不同拍攝距離時,目標物特徵點於影像畫面中所產生像素值的差異,實現對於目標物之距離量測,並進一步利用影像畫面中各個目標物特徵點之距離資訊,以平滑內插處理後繪製出景深圖。zh_TW
dc.description.abstractThis paper presents a method for depth measurement based on Speeded Up Robust Features (SURF) and pixel number variation of CCD Images. A single camera is used to capture two images in different photographing distances, where speeded up robust features in the images are extracted and matched. To remove mismatches from given putative point correspondences, an Identifying point correspondences by Correspondence Function (ICF) method is adopted in order to automatically select better reference points required by the pixel number variation method. Based on the displacement of the camera at two photographing distances, feature points of the objects in the images are used to determine the distance measurement of the target objects. After that, we use the obtained distance information of the feature points of the target objects to construct the depth map by using smooth interpolation.en_US
dc.description.sponsorship電機工程學系zh_TW
dc.identifierGN0698750308
dc.identifier.urihttp://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22GN0698750308%22.&%22.id.&
dc.identifier.urihttp://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/95837
dc.language中文
dc.subject距離量測zh_TW
dc.subject景深zh_TW
dc.subjectSURFzh_TW
dc.subject影像zh_TW
dc.subject數位相機zh_TW
dc.subject影像式量測系統zh_TW
dc.subjectdistance measurementen_US
dc.subjectdepthen_US
dc.subjectSURFen_US
dc.subjectimageen_US
dc.subjectdigital camerasen_US
dc.subjectimage-based measuring systemsen_US
dc.title結合像素差異法與SURF之景深量測系統zh_TW
dc.titleDepth Measurement Based on Pixel Number Variation and Speeded Up Robust Features (SURF)en_US

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