impressions

An R package for diagnosing cartridge case impression comparison algorithms.

Installation

You can install the development version of impressions from GitHub with:

# install.packages("devtools")
devtools::install_github("jzemmels/impressions")

Visual Diagnostics

As an example, consider two cartridge cases fired from the same Ruger SR9 semiautomatic 9-mm handgun. Learn more about the collection of these cartridge cases here. The cartridge cases are uniquely identified as “K013sA1” and “K013sA2.” We assume that the markings on these cartridge cases left by the handgun during the firing process are similar.

library(impressions)

library(cmcR)
library(tidyverse)

data("K013sA1","K013sA2")

Below is a visual of the two cartridge case scans. Note that these scans have already undergone some preprocessing to emphasize the breech face impression markings. The similarity between these cartridge cases is not immediately apparent. The color scheme of these plots is chosen to highlight extreme values (far from the median value) in the scan, meaning small, local similarities are not highlighted. We can use visual diagnostics available in the impressions package to understand the similarity between these scans.

x3pPlot(K013sA1,K013sA2,
        x3pNames = c("Scan A","Scan B"))

Comparison Plot

The comparison plot is a powerful tool to separate two cartridge case scans into “similar” and “different” regions. First, we attempt to align these two scans by rotating and translating K013sA2 to match K013sA1. We use the cmcR::comparison_allTogether() function to estimate this alignment. For the sake of this example, we’ll consider K013sA2 rotated by 3 degrees and translated to maximize the cross-correlation function with K013sA1.

comparisonData <- cmcR::comparison_allTogether(K013sA1,K013sA2,
                                               theta = 3,
                                               numCells = c(1,1),
                                               maxMissingProp = .99,
                                               sideLengthMultiplier = 1.1,
                                               returnX3Ps = TRUE)

The resulting comparisonData data frame contains a transformed version of scan K013sA2 after rotating/translating to align with K013sA1. We’ll pass this transformed scan to the x3p2 argument.

x3p_comparisonPlot(x3p1 = comparisonData$cellHeightValues[[1]],
                   x3p2 = comparisonData$alignedTargetCell[[1]],
                   legendLength = grid::unit(6,"in"),
                   legendQuantiles = c(0,.01,.05,.5,.95,.99,1))