A good similarity measure is the key to robust template matching. In this paper, we present a Similarity-Transform invariant Best-Buddies Similarity (SiTi-BBS) to deal with the template matching with obvious geometric distortion. Similar to the BBP, SiTi-BBS still adopts Best-Buddies Pair (BBP) to vote. However, differing from the classic BBS acquiring the point pair via bidirectional matching in xyRGB space, SiTi-BBS takes only the color information (RGB components) to acquire BBPs, while the position information (xy components) of each BBP is employed to calculate the geometric distortion between the template and matching window. To further improve the robustness of template matching, we novelly take advantage of the interval voting to accommodate the case where the two images do not strictly satisfy the similarity transformation. Therefore, SiTi-BBS, to a certain extent, can be applied to the affine and perspective transformation. In this way, the highest number of votes is taken as the similarity measure between the two images. Mathematical analysis indicates that the proposed method is capable of dealing with the case of obvious geometric distortion between images. Furthermore, the test results of simulated and real challenging images show the outstanding performance of the proposed similarity measure for template matching.
Micro satellites have been widely used in the target monitoring and tracking. Aimed to reduce the ground operator's workload, a target location method based on homography and scene matching is proposed in this paper. For the first time satellite flies over target area, it needs the operator to take a frame as a reference image and extract the target area which is regarded as plane scene. When the satellite scan the area again, we take a frame as a real-time image and calculate the homography induced by the plane. Then rectify the reference image with the homography to reduce distortion between the two images. Finally, locate target on the real-time image using matching method. A significant-feature-point auxiliary positioning method is also proposed to adapt to target area without obvious features. It adopts affine model to calculated target location on the real-time image. Simulation experimental results show accuracy and practical value for engineering of the proposal method.
To simultaneously perform 3D measurement and camera attitude estimation, an efficient and robust method based on trifocal tensor is proposed in this paper, which only employs the intrinsic parameters and positions of three cameras. The initial trifocal tensor is obtained by using heteroscedastic errors-in-variables (HEIV) estimator and the initial relative poses of the three cameras is acquired by decomposing the tensor. Further the initial attitude of the cameras is obtained with knowledge of the three cameras’ positions. Then the camera attitude and the interested points’ image positions are optimized according to the constraint of trifocal tensor with the HEIV method. Finally the spatial positions of the points are obtained by using intersection measurement method. Both simulation and real image experiment results suggest that the proposed method achieves the same precision of the Bundle Adjustment (BA) method but be more efficient.