Image matching for 3D photogrammetric reconstruction

Ivan Detchev, Ayman Habib, Jiann Yeou Rau

研究成果: Conference contribution

摘要

Image matching or the identification of conjugate points appearing in two or more photographs is an integral processing task in photogrammetry. More specifically, three-dimensional photogrammetric reconstruction, i.e. computing the object space coordinates of certain points of interest, would be impossible without having these points matched in the image space of at least two photographs. Observing a point of interest in only one photograph is simply not enough, because the range from the image point to the object point is inherently unknown. Thus, image matching is applied to satellite scenes and aerial photography for the generation of digital terrain models or building models, and also in close range photogrammetry for the reconstruction of objects and surfaces. The human brain has incredible capabilities and manual image matching, i.e. one performed by a human operator, is very accurate and reliable; however, it is labour intensive, time consuming, and can become quite tedious very quickly. This is why the topic of fully automated image matching, i.e. one performed solely by a computer, has been addressed in photogrammetric research for a long time. While the problem of automatically matching signalized targets has been satisfactorily solved, the matching of natural or non-signalized features still requires some human interaction. This paper will attempt to summarize the methodology of performing image matching of ordered photographs in a controlled environment, i.e. the interior and the exterior orientation parameters for the involved cameras and photographs are known. Example results from using a sophisticated commercial software package called CLOse RAnge MAtching (CLORAMA) and from a simple in-house matching program will be shown. The former method is based on least squares matching and requires very high resolution imagery when homogenous texture is present, while the latter method is based on the straightforward normalized cross-correlation matching and requires a pattern to be projected in order to create artificial texture.

原文English
主出版物標題32nd Asian Conference on Remote Sensing 2011, ACRS 2011
頁面247-252
頁數6
出版狀態Published - 2011 十二月 1
事件32nd Asian Conference on Remote Sensing 2011, ACRS 2011 - Tapei, Taiwan
持續時間: 2011 十月 32011 十月 7

出版系列

名字32nd Asian Conference on Remote Sensing 2011, ACRS 2011
1

Other

Other32nd Asian Conference on Remote Sensing 2011, ACRS 2011
國家Taiwan
城市Tapei
期間11-10-0311-10-07

指紋

Image matching
Photogrammetry
Textures
Aerial photography
Software packages
Brain
Cameras
Satellites
Personnel
Processing

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

引用此文

Detchev, I., Habib, A., & Rau, J. Y. (2011). Image matching for 3D photogrammetric reconstruction. 於 32nd Asian Conference on Remote Sensing 2011, ACRS 2011 (頁 247-252). (32nd Asian Conference on Remote Sensing 2011, ACRS 2011; 卷 1).
Detchev, Ivan ; Habib, Ayman ; Rau, Jiann Yeou. / Image matching for 3D photogrammetric reconstruction. 32nd Asian Conference on Remote Sensing 2011, ACRS 2011. 2011. 頁 247-252 (32nd Asian Conference on Remote Sensing 2011, ACRS 2011).
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abstract = "Image matching or the identification of conjugate points appearing in two or more photographs is an integral processing task in photogrammetry. More specifically, three-dimensional photogrammetric reconstruction, i.e. computing the object space coordinates of certain points of interest, would be impossible without having these points matched in the image space of at least two photographs. Observing a point of interest in only one photograph is simply not enough, because the range from the image point to the object point is inherently unknown. Thus, image matching is applied to satellite scenes and aerial photography for the generation of digital terrain models or building models, and also in close range photogrammetry for the reconstruction of objects and surfaces. The human brain has incredible capabilities and manual image matching, i.e. one performed by a human operator, is very accurate and reliable; however, it is labour intensive, time consuming, and can become quite tedious very quickly. This is why the topic of fully automated image matching, i.e. one performed solely by a computer, has been addressed in photogrammetric research for a long time. While the problem of automatically matching signalized targets has been satisfactorily solved, the matching of natural or non-signalized features still requires some human interaction. This paper will attempt to summarize the methodology of performing image matching of ordered photographs in a controlled environment, i.e. the interior and the exterior orientation parameters for the involved cameras and photographs are known. Example results from using a sophisticated commercial software package called CLOse RAnge MAtching (CLORAMA) and from a simple in-house matching program will be shown. The former method is based on least squares matching and requires very high resolution imagery when homogenous texture is present, while the latter method is based on the straightforward normalized cross-correlation matching and requires a pattern to be projected in order to create artificial texture.",
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Detchev, I, Habib, A & Rau, JY 2011, Image matching for 3D photogrammetric reconstruction. 於 32nd Asian Conference on Remote Sensing 2011, ACRS 2011. 32nd Asian Conference on Remote Sensing 2011, ACRS 2011, 卷 1, 頁 247-252, 32nd Asian Conference on Remote Sensing 2011, ACRS 2011, Tapei, Taiwan, 11-10-03.

Image matching for 3D photogrammetric reconstruction. / Detchev, Ivan; Habib, Ayman; Rau, Jiann Yeou.

32nd Asian Conference on Remote Sensing 2011, ACRS 2011. 2011. p. 247-252 (32nd Asian Conference on Remote Sensing 2011, ACRS 2011; 卷 1).

研究成果: Conference contribution

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Detchev I, Habib A, Rau JY. Image matching for 3D photogrammetric reconstruction. 於 32nd Asian Conference on Remote Sensing 2011, ACRS 2011. 2011. p. 247-252. (32nd Asian Conference on Remote Sensing 2011, ACRS 2011).