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Applications of Computer Vision, IEEE Workshop on (1996)
Sarasoto, FL
Dec. 2, 1996 to Dec. 4, 1996
ISBN: 0-8186-7620-5
pp: 8
Benoit Huet , University of York
Edwin R. Hancock , University of York
This paper aims to develop simple statistical methods for indexing into an aerial image database using a cartographic model. The images contained in the database are of urban and semi-urban areas. The cartographic model represents a road network known to appear in a subset of the images contained within the database. There are known to be severe imaging distortions present and the data cannot be recovered by applying a simple Euclidean transform to the model. We effect the cartographic indexing into the database using pairwise histograms of the angle differences and the cross ratios of the lengths of line segments extracted from the raw aerial images. We investigate several alternative ways of performing histogram comparison. Our conclusion is that the Matusita and Bhattacharyya distances provide significant performance advantages over the L2 norm employed by Swain and Ballard. Moreover, a sensitivity analysis reveals that the angle-difference histogram provides the most discriminating index of line-structure; it is robust both to image distortion an to the variable quality of input line-segmentation.
Cartographic indexing, image databases, pairwise geometric histogram, statistical distance measures.

B. Huet and E. R. Hancock, "Cartographic Indexing into a Database of Remotely Sensed Images," Applications of Computer Vision, IEEE Workshop on(WACV), Sarasoto, FL, 1996, pp. 8.
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