An outsole database was produced, resulting in summary statistics and frequency estimates on 72,306 randomly acquired characteristics extracted from 1,300 outsole shoeprint images. Study results are derived from a combination of automated and analyst-derived image extraction and processing tools, with the human-dependent step of RAC detection and marking. Given some unavoidable subjective steps in the image-processing chain, inter- and intra-analyst variability in RAC marking was assessed using a quality control/assurance program that included the duplicate marking of 5,177 randomly acquired characteristics across 160 shoes (320 RAC maps). The results of the analysis indicate that RAC detection was the largest variable not easily controlled (even with training); however, when RACs are equally detected in repeat analyses, they are marked relatively consistently. Post-detection and extraction, each RAC was broadly characterized in terms of its degree of linearity, circularity, and triangularity. Using geometric shape classification rules, automated shape attribution was compared to human-perceptual assignments and found to be in agreement between 68 percent to 95 percent of the time across 1,352 comparisons, depending on the complexity of the dataset. Overall, the results show limited utility in classifying complex features into prescribed shape classes. Future work should consider alternative mechanisms, such as shape clustering, instead of strict categorization as a means of grouping randomly acquired characteristics in terms of shape similarity. This report also cautions that all results are a function of the nature of the footwear population studied, which was composed of athletic gear (86 percent), men's wear (72 percent), and sizes 9 through 11 (53 percent). 34 figures, 22 tables, and 33 references
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