An Analysis of the Effect of Gaussian Error in Object Recognition

Object recognition is complicated by clutter, occlusion, and sensor error. Since pose hypotheses are based on image feature locations, these effects can lead to false negatives and positives. In a typical recognition algorithm, pose hypotheses are tested against the image, and a score is assigned to each hypothesis. We use a statistical model to determine the score distribution associated with correct and incorrect pose hypotheses, and use binary hypothesis testing techniques to distinguish between them. Using this approach we can compare algorithms and noise models, and automatically choose values for internal system thresholds to minimize the probability of making a mistake.

An Assessment of the Degree of Implementation of the Lean Aerospace Initiative Principles and Practices within the US Aerospace and Defense Industry

This report is a formal documentation of the results of an assessment of the degree to which Lean Principles and Practices have been implemented in the US Aerospace and Defense Industry. An Industry Association team prepared it for the DCMA-DCAAIndustry Association “Crosstalk” Coalition in response to a “Crosstalk” meeting action request to the industry associations. The motivation of this request was provided by the many potential benefits to system product quality, affordability and industry responsiveness, which a high degree of industry Lean implementation can produce.