Outliers/Partial Overlap

When registering two (or more) data sets, there are usually 3D data points or other features that do not have any correspondence between the data sets. The two main causes of this are: 1) regions of the data where there is no overlap (a natural consequence of extending the data description by incrementally fusing partially overlapping data) and 2) noise outliers (79,51,10,18,62). Hence, great effort has been made to identify outliers and partial overlap based on techniques such as high dimensional distance measurement (24,63,37), orientation consistency (79), interpoint distance (20), boundary points removal (57,72), threshold (62), and motion properties (43,56,42). It has been shown that the identification of outliers and partial overlap are essential steps to the estimation of motion parameters. Moreover, these two steps are often interweaved and affect each other especially when no exact information is available about the distribution of points, occlusion, appearance and disappearance of points. This implies that assuming image data as a black or grey box, the development of techniques to register such data is still a challenging task and prone to local minimum convergence as pointed out above. Future research may focus on the use of structural data information, motion properties, special information on objects, or special imaging configurations for a more accurate estimation of image correspondences.