ASA conference

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Hessian-based affine adaptation of salient local image features

Affine covariant local image features are a powerful tool for many applications, including matching and calibrating wide baseline images. Local feature extractors that use a saliency map to locate features require adaptation processes in order to extract affine covariant features. The most effective extractors make use of the second moment matrix (SMM) to iteratively estimate the affine shape of local image regions. This paper shows that the Hessian matrix can be used to estimate local affine shape in a similar fashion to the SMM. The Hessian matrix requires significantly less computation effort than the SMM, allowing more efficient affine adaptation. Experimental results indicate that using the Hessian matrix in conjunction with a feature extractor that selects features in regions with high second order gradients delivers equivalent quality correspondences in less than 17% of the processing time, compared to the same extractor using the SMM

Genetic markers on chromosome 7.

Chromosome 7 is frequently associated with chromosome aberrations, rearrangements, and deletions. It also contains many important genes, gene families, and disease loci. This brief review attempts to summarise these and other interesting aspects of chromosome 7. With the rapid accumulation of cloned genes and polymorphic DNA fragments, this chromosome has become an excellent substrate for molecular genetic studies