The problem of accurately registering an aerial video image to geo-referenced imagery has become more important in recent years. To achieve high efficiency, we propose a guided hierarchical searching scheme to augment the current geo-registration framework. The algorithm consists of three major steps. In the first step, the reference image and video frames are projected to a common coordinate system based on the telemetry. In the second step, feature points are extracted in the video and reference, and the coarse searching for the best match was performed on a feature points image pyramid, where the comparison process is only applied to the regions with feature points. In the final step, the precise transformation parameters are estimated using the Levenberg-Marquardt techniques, which results in a precise alignment of the video and reference. Compared with conventional blind comparison based on the normalized cross-correlation measure, the proposed approach differes because it applies a feature-based hierarchical searching scheme to quickly lead the matching process to the most likely protion in the reference and speed up the matching process. Experimental results that evaluate the developed approach using real world aerial video will be presented. The obtained results demonstrate that our proposed method can be used in a complete geo-registration system to provide accurate registration of the video frames.
This paper presents a new hybrid and hierarchical algorithm for aligning two partially overlapping aerial images. This computationally efficient approach produces accurate results even when large rotation and translation have occurred between two images. The first step of the approach is coarse matching where transformation parameters are estimated using the partial Hausdorff distance measure for maximally feature consensus. For feature extraction, it applies a modified phase congruency model to effectively locate feature points of local curvature discontinuity, structural boundaries, and other prominent edges. Our proposed coarse matching doesn't require explicit feature correspondence, and the partial Hausdorff distance measure can tolerate well the presence of outliers and feature extraction errors. In the second step, the pairwise matching of the feature points detected from both images is performed, where the initial estimate obtained in the first step is used to dramatically facilitate the determination of feature point correspondence. This two-step approach compensates deficiencies in each step and it is computationally efficient. The first step dramatically decreases the size of the search range for correspondence establishment in the second step and no direct pairwise feature matching is required in the first step. Experiment results demonstrate the robustness of our proposed algorithm using real aerial photos.
Edge contour extraction plays an important role in computer vision because edge contours are relatively invariant to the changes of illumination conditions, sensor characteristics, etc. In particular, edge contours can be used as matching primitives for correspondence determination, an important step in video geo-registration. In this paper, we present a new approach for edge contour extraction based on a three-step procedure that using a RCBS-based scheme, inherently more accurate results can be produced, even though the edge model used for edges is relatively simple. We also present recursive filters that can efficiently smooth splines by approximating a signal with a complete set of coefficients subject to certain regularization constraints. We demonstrate our method on both synthetic and real images.