We propose a contextual framework for 2D image matching and registration using an ensemble feature. Our system is beneficial for registering image pairs that have captured the same scene but have large visual discrepancies between them. It is common to encounter challenging visual variations in image sets with artistic rendering differences or in those collected over a period of time during which the lighting conditions and scene content may have changed. Differences between images may also be caused using a variety of cameras with different sensors, focal lengths, and exposure values. Local feature matching techniques cannot always handle these difficulties, so we have developed an approach that builds on traditional methods to consider linear and histogram of gradient information over a larger, more stable region. We also present a technique for using linear features to estimate corner keypoints, or pseudo corners, that can be used for matching. Our pipeline follows this unique matching stage with homography refinement methods using edge and gradient information. Our goal is to increase the size of accurate keypoint match sets and align photographs containing a combination of man-made and natural imagery. We show that incorporating contextual information can provide complimentary information for scale invariant feature transform and boost local keypoint matching performance, as well as be used to describe corner feature points.

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Registering 2D and 3D data is a rapidly growing research area. Motivating much of this work is the fact that 3D range scans and 2D imagery provide different, but complementing information about the same subject. Combining these two perspectives leads to the creation of accurate 3D models that are texture mapped with high resolution color information. Imagery can even be obtained on different days and in different seasons and registered together to show how a scene has changed with time. Finding correspondences among data captured with different cameras and containing content and temporal changes can be a challenging task. We address these difficulties by presenting a contextual approach for finding 2D matches, performing 2D-3D fusion by solving the projection matrix of a camera directly from its relationship to highly accurate range scan points, and minimizing an energy function based on gradient information in a 3D depth image.