Non-wildcard matching beats the interpretation tree

Probably the best known control algorithm for high-level model matching in computer vision is the Interpretation Tree expansion algorithm, popularized and extended by Grimson and Lozano-Perez. This algorithm has been shown to have a high computational complexity, particularly when being applied to matching problems with large numbers of features. This paper introduces a non-wildcard variation on this algorithm that has an improvement of about 4-10 in performance over the standard Interpretation Tree algorithm.

Cootes. Locating Overlapping Flexible Shapes Using Geometrical Constraints

In an earlier paper [1] we have proposed a shape representation called the CLD (Chord Length Distribution) which possesses many of the often-quoted desirable properties of a shape representation. It also captures shape variability and complements an object location method using belief updating which integrates low-level evidence and shape constraints. Promising results on synthetic and real rigid objects were given. This paper describes a development to the original definition which makes the location method robust with respect to clutter. We give experimental results which demonstrate the performance of the revised scheme on a class of flexible shapes, both singly and overlapping. We are currently engaged in a research project [see acknowledgements] concerned with automated 2-D inspection of complex (industrial) assemblies. In common with many machine vision applications we seek to exploit object shape and other geometrical constraints to assist in locating objects in scenes and evaluating interpretations with respect to expected appearance. To this end we need suitabl

Model-Based Image Interpretation Using Genetic Algorithms

We describe the application of genetic algorithms in model-based image interpretation

0 Adaptive Segmentation of MRI data

Note: As near as I can tell, this is a recreation from my sources of the paper that appeared in