Abstract: Training sets of images used for training machine vision systems for object recognition may be augmented via various training set augmentation methods. The machine vision systems trained via the augmented training sets may be further used in an augmented reality procedural guidance system for guiding an operator to complete one or more steps with respect to one or more objects. The training set augmentations may include one or more of an object motion augmentation, a camera motion augmentation, an object herding or clumping augmentation, an object size reduction augmentation, a diversified background augmentation, and a diversified background augmentation with synthetic background images. In some instances, machine vision systems may also be alternatively or concurrently trained to use optically distinguishable markers to recognize objects.

Abstract: The majority of PCR-based fingerprinting technologies generate dominant genetic markers; homozygote present and heterozygote genotypes cannot be distinguished using conventional detection methods. In contrast, codominant genetic markers provide an unambiguous distinction among each genotype. A genotyping method is described that includes procedures implemented in software. This method quantifies allele copy number and enables recovery of codominant genotypes from markers expressing ostensibly dominant phenotypes. These procedures are designed and implemented to (1) greatly reduce variability attributable to sample assay and detector noise, (2) accurately estimate allele size and copy number, (3) provide normalization criteria for intra- and inter-marker comparisons, and (4) scale the resulting data to determine the genotype of individual markers.