摘要
This paper presents a robust image feature that can be used to automatically establish match correspondences between aerial images of suburban areas with large view variations. Unlike most commonly used invariant image features, this feature is view variant. The geometrical structure of the feature allows predicting its visual appearance according to the observer’s view. This feature is named 2EC (2 Edges and a Corner) as it utilizes two line segments or edges and their intersection or corner. These lines are constrained to correspond to the boundaries of rooftops. The description of each feature includes the two edges’ length, their intersection, orientation, and the image patch surrounded by a parallelogram that is constructed with the two edges. Potential match candidates are obtained by comparing features, while accounting for the geometrical changes that are expected due to large view variation. Once the putative matches are obtained, the outliers are filtered out using a projective matrix optimization method. Based on the results of the optimization process, a second round of matching is conducted within a more confined search space that leads to a more accurate match establishment. We demonstrate how establishing match correspondences using these features lead to computing more accurate camera parameters and fundamental matrix and therefore more accurate image registration and 3D reconstruction.
This paper presents a robust image feature that can be used to automatically establish match correspondences between aerial images of suburban areas with large view variations. Unlike most commonly used invariant image features, this feature is view variant. The geometrical structure of the feature allows predicting its visual appearance according to the observer’s view. This feature is named 2EC (2 Edges and a Corner) as it utilizes two line segments or edges and their intersection or corner. These lines are constrained to correspond to the boundaries of rooftops. The description of each feature includes the two edges’ length, their intersection, orientation, and the image patch surrounded by a parallelogram that is constructed with the two edges. Potential match candidates are obtained by comparing features, while accounting for the geometrical changes that are expected due to large view variation. Once the putative matches are obtained, the outliers are filtered out using a projective matrix optimization method. Based on the results of the optimization process, a second round of matching is conducted within a more confined search space that leads to a more accurate match establishment. We demonstrate how establishing match correspondences using these features lead to computing more accurate camera parameters and fundamental matrix and therefore more accurate image registration and 3D reconstruction.
