Automated selection of corresponding point candidates for image registration with artificial neuralnet using rotation invariant moments as the input data
スポンサーリンク
概要
- 論文の詳細を見る
Accuracy of image registration is severely affected by that of corresponding control point (CCP) selection in remote sensing or GIS (Geographic Information System) . In this paper, a new automated system for CCP candidate selection from target images is proposed. In the system, first, grayscale image within a quasicircular field of view (FOV) is transformed into binary one after intensity modification for the several extreme intensity pixels. Next, the binary image is transformed into a rotation invariant intermediate representation. Finally, the system determines whether the central pixel of the FOV is appropriate as the CCP by using well-trained 3-layer feedforward artificial neuralnet. Pseudo Zernike moments are employed as the intermediate representation. Consequently, without selection accuracy deterioration, we achieve quite fewer training patterns, shorter training time, and higher noise tolerance in comparison with conventional neuralnet-based systems.
- 社団法人 日本写真測量学会の論文
社団法人 日本写真測量学会 | 論文
- Landform monitoring of Tottori Sand Dune using aerial photographs.
- One Projector-One Camera System on Short Range Photogrammetry
- タイトル無し
- A Study on Environmental Evaluation Method Using the Satellite Image-Radiation quantity adjustment of multi-temporal data-.:-Radiation quantity adjustment of multi-temporal data-
- タイトル無し