An object-image metric is an extension of standard metrics in that it is constructed for matching and comparing configurations of object features to configurations of image features. For the generalized weak perspective camera, it is invariant to any affine transformation of the object or the image. Recent research in the exploitation of the object-image metric suggests new approaches to Automatic Target Recognition (ATR). This paper explores the object-image metric and its limitations. Through a series of experiments, we specifically seek to understand how the object-image metric could be applied to the image registration problem-an enabling technology for ATR.

In this paper we propose to jointly segment and register objects of interest in layered images. Layered imaging refers to imageries taken from different perspectives and possibly by different sensors. Registration and segmentation are therefore the two main tasks which contribute to the bottom level, data alignment, of the multisensor data fusion hierarchical structures. Most exploitations of two layered images assumed that scanners are at very high altitudes and that only one transformation ties the two images. Our data are however taken at mid-range and therefore requires segmentation to assist us examining different object regions in a divide-and-conquer fashion. Our approach is a combination of multiphase active contour method with a joint segmentation-registration technique (which we called MPJSR) carried out in a local moving window prior to a global optimization. To further address layered video sequences and tracking objects in frames, we propose a simple adaptation of optical flow calculations along the active contours in a pair of layered image sequences. The experimental results show that the whole integrated algorithm is able to delineate the objects of interest, align them for a pair of layered frames and keep track of the objects over time.

Abstract

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