Abstract
Multiview Matching Algorithm for Processing Mobile Sequence Images
Highlights
Photogrammetry and geometric computer vision are closely related disciplines
On the condition of error constraints based on minimizing algebra and the homologous matched points of the three views, the trifocal tensor over the three images is estimated through the fundamental matrix using the random sample consensus (RANSAC) algorithm
Image data processing mainly included camera lens distortion correction, Harris feature point extraction, image matching based on correlation coefficient, fundamental matrix fitting by RANSAC, and estimation of the trifocal tensor
Summary
Photogrammetry and geometric computer vision are closely related disciplines. Many studies have shared interest in these two disciplines for many similar goals, such as point feature detection (Forstner and Gulch 1987), relative orientation (Philip 1996; Nister 2004), perspective n-point (PnP) problems (Masry 1981; Lepetit et al 2009), and bundle adjustment (Triggs et al 2000). Photogrammetry uses the collinearity equations of Cartesian coordinate representation of the central projection in Euclidean geometry, and computer vision applies the projection equations of homogeneous coordinate representation of the central projection in projective geometry. The nonlinearity of the collinearity equations requires linearity and iterative optimization and good initial values of exterior orientation (EO) parameters from a global navigation satellite system and inertial measuring unit (GNSS/IMU) system. All the parameters in collinearity equations have physical meanings. The linearity of the projection equation permits linear matrix operations using linear algebra, the linearity of the camera matrix or fundamental matrix does not require the initial values, and those parameters of the matrix are not physically interpretable
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