Fast Object Recognition in Noisy Images Using Simulated Annealing

A fast simulated annealing algorithm is developed for automatic object recognition. The normalized correlation coefficient is used as a measure of the match between a hypothesized object and an image. Templates are generated on-line during the search by transforming model images. Simulated annealing reduces the search time by orders of magnitude with respect to an exhaustive search. The algorithm is applied to the problem of how landmarks, for example, traffic signs, can be recognized by an autonomous vehicle or a navigating robot. The algorithm works well in noisy, real-world images of complicated scenes for model images with high information content.

High Optical Quality Nanoporous GaN Prepared by Photoelectrochemical Etching

Nanoporous GaN films are prepared by UV assisted electrochemical etching using HF solution as an electrolyte. To assess the optical quality and morphology of these nanoporous films, micro-photoluminescence (PL), micro-Raman scattering, scanning electron microscopy (SEM), and atomic force microscopy (AFM) techniques have been employed. SEM and AFM measurements revealed an average pore size of about 85-90 nm with a transverse dimension of 70-75 nm. As compared to the as-grown GaN film, the porous layer exhibits a substantial photoluminescence intensity enhancement with a partial relaxation of compressive stress. Such a stress relaxation is further confirmed by the red shifted E₂(TO) phonon peak in the Raman spectrum of porous GaN.