Pose estimation in automated visual inspection using genetic algorithm.

In this paper, we propose a genetic algorithm based approach to determine the pose of an object in Automated Visual Inspection having three degrees of freedom. We have investigated the effect of noise at 20 dB SNR and also mismatch resulting from incorrect correspondences between the object space points and the image space points, on the estimation of pose parameters. The maximum error in translation parameters is less than 0.45 cm and rotational error is less than 0.2 degree at 20 dB SNR. The error in parameter estimation is insignificant upto 7 pairs of mismatched points out of 24 points in object space and the results skyrockets when 8 or more pairs of points are mismatched. We have compared our result with that obtained by least square technique and it shows that GA based method outperform the gradient based technique when the number of vertices of the object to be inspected is small. These results have clearly established the robustness of GA in estimating the pose of an object with small number of vertices in automated visual inspection.

Pose estimation in automated visual inspection using ANN.

In this paper, we present an accurate and robust pose estimator of rigid, polyhedral objects, based on Artificial Neural Networks (ANN), as suitable for Automated Visual Inspection (AVI) applications. The estimator is novel in the sense that it is trained with different poses of the objects having dimensional deviations within its tolerance range and is therefore robust with respect to within tolerance dimensional errors. The estimation accuracy is scalable and our computer simulation experiments in the existing configurations of ANNs have shown an accuracy better than 4% of the placement error. The ANN based pose estimator offers several advantages over the classical implementations.

Vanadium(V) complexes of alpha-hydroxycarboxylic acids in aqueous solution.

51V NMR and IR spectroscopic studies of the complexes formed between vanadate and the alpha-hydroxylic acid ligands, (S)-2-hydroxypropanoic acid (L-(+)-lactic acid), 2-hydroxy-2-methylpropanoic acid, and 2-ethyl-2-hydroxybutanoic acid were carried out for aqueous 1 M ionic strength (NaCl) solutions. Three major products in V to L stoichiometries of 1:1, 2:2, and 3:2 were identified from vanadate and ligand concentration studies, while a pH variation study allowed charge states to be determined. At pH 7.06, the formation constants for the predominant reactions were (26 +/- 1) M (-1), (V + L <= => VL); (6.8 +/- 0.4) x 10(3) M(-1), (2VL <= => V(2)L(2)); and (3.5 +/- 0.3) x 10(3) M(-1), (V(2)L(2) + V <= => V(3)L(2)). Dissolution studies of various crystalline products were carried out for aqueous, nonaqueous, and mixed solvent systems. These studies combined with information available from X-ray structural studies provided a basis for the assignment of solution state structures. Pentacoordinate vanadium in a trigonal-bipyramidal geometry was proposed for the both the 1:1 and 2:2 complexes when in aqueous solution. Observed changes in (51)V chemical shift patterns were consistent with a cis fusion in octahedral coordination for the central vanadium of the 3:2 complex, while the remaining vanadiums retained a pentacoordinate geometry.