Mick Turner

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We introduce a theoretical framework for estimating the matching performance of binary correlation matrices acting as hetero-associative memories. The framework is applicable to non-recursive, fully-connected systems with binary (0,1) Hebbian weights and hard-limited threshold. It can handle both full and partial matching of single or multiple data items in(More)
We propose a new relaxation scheme for graph matching in computer vision. The main distinguishing feature of our approach is that matching is formulated as a process of eliminating unlikely candidates rather than finding the best match directly. Bayesian development leads to a robust algorithm which can be implemented in a fast and efficient manner on a(More)
We describe a technique for matching a single, learned elastic model of the shape of normal chromosomes to chromosomal images. Our model has a hierarchical organisation , with increasingly coarse shape descriptions at higher levels. A problem of finding the model description most likely to have generated an image is reduced to one of matching the locations(More)