A Class of Algorithms for Fast Digital Image Registration
IEEE Transactions on Computers
The Sample Tree: A Sequential Hypothesis Testing Approach to 3D Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Sequential Hierarchical Scene Matching
IEEE Transactions on Computers
Efficient and reliable template set matching for 3D object recognition
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
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This paper considers the problem of detecting the local similarity between templates in a given class and a given image using a hierarchically ordered sequential decision rule. When the given set consists of a large number of templates and the number of locations in the image matching any of the templates is small, it is wasteful to examine each of the templates at every location in the image for a match. Instead, it is proposed that the set of templates be partitioned and a ``representative template'' be defined for each of the partitions. Several levels of partitioning are defined. Elimination of mismatching locations and termination of computation can take place at each, level of detection. Each level of testing is over a more restrictive subset of the template class than the previous level. The paper presents a general formulation of this approach and gives criteria for selecting representative templates, the ordering of components of a template vector for error evaluation, and the threshold sequences to be used in deciding about a ``match.'' Suboptimal solutions are given satisfying these criteria. Illustrative examples are provided showing recognition of linear features in test patterns and photographs obtained by aerial and spaceborne sensors.