With the wide availability of digital cameras and high quality smart phone cameras the world is awash in digital images. These cameras are most often uncalibrated meaning that one does not have knowledge of the internal camera parameters, such as focal length and optical center, nor
information about the external camera parameters which describe the location of the camera relative to the imaged scene. In the absence of additional information about the scene one cannot parse out many common types of geometric information. For instance, one cannot calculate distances between
points or angles between lines. One can, however, determine the lesser known projective shape of a scene. Projective shape data do not lie in Euclidean space [Inline formula]. This presents special challenges since the overwhelming majority of statistical methods are for data in [Inline formula].
Furthermore, we consider 3D projective shapes of contours extracted from digital camera images which lie in an infinite-dimensional projective shape space and as such presents an especially novel environment for doing statistics. We develop a novel nonparametric hypothesis testing method for
the mean change from matched sample contours. Our methodology is applied to the two-sample problem for 3D projective shapes of contours extracted from digital camera images.
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