# Effect of Camera Choice on Image-Classification Inference
## 4 Results and Discussion
### Quantitative Effect of Camera Choice  
To assess the effect of camera choice on inference quantitatively, we examine the agreement of the model inference in terms of the top prediction class for comparable images taken by different cameras. As in the previous section, we limit attention to the Angetube 962A, Logitech C920e, and Microsoft LifeCam cameras because these three cameras have a very similar FOV, and they have a relatively large number of supported resolutions in common (see Table 1). For each scenario and camera, there are 150 comparable images corresponding to the following:  
* Five target vessels;
* Five distances (20 cm, 30 cm, 40 cm, 50 cm, and 60 cm);
* Six common image resolutions (1280 \(\times\) 720, 800 \(\times\) 448, 640 \(\times\) 360, 640 \(\times\) 480, 320 \(\times\) 240, and 352 \(\times\) 288). For each camera pairing and scenario, the most basic agreement statistic is the raw observed agreement proportion \(p_{o}\) between the comparable images of the two cameras, defined as follows:  
\[p_{o}=\frac{n_{agree}}{N} \tag{2}\]  
where  
* \(n_{agree}\) is the number of compatible images for the two cameras for which the top model prediction is the same;
* \(N\) is the total number of comparable images for the two cameras (\(N\) = 150 per scenario in this case). \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Camera**} & \multicolumn{4}{c}{**Scenario 1**} & \multicolumn{4}{c}{**Scenario 2**} & \multicolumn{4}{c}{**Scenario 3**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Vessel 2**} & \multicolumn{2}{c}{**Vessel 5**} & \multicolumn{2}{c}{**Vessel 2**} & \multicolumn{2}{c}{**Vessel 5**} & \multicolumn{2}{c}{**Vessel 2**} & \multicolumn{2}{c}{**Vessel 5**} \\ \hline \multirow{4}{*}{Angetube 962A} & Cup & 0.78 & Cup & 0.98 & Cup & 0.46 & Cup & 0.00 & Cup & 0.31 & Cup & 0.58 \\ \cline{2-10}  & Mug & 0.00 & Mug & 0.00 & Mug & 0.00 & Mug & 0.75 & Mug & 0.00 & Mug & 0.00 \\ \cline{2-10}  & Others & 0.22 & Others & 0.02 & Others & 0.54 & Others & 0.25 & Others & 0.69 & Others & 0.42 \\ \hline \multirow{4}{*}{Logitech C920e} & Cup & 0.71 & Cup & 1.00 & Cup & 0.32 & Cup & 0.00 & Cup & 0.22 & Cup & 0.23 \\ \cline{2-10}  & Mug & 0.00 & Mug & 0.00 & Mug & 0.00 & Mug & 0.82 & Mug & 0.06 & Mug & 0.38 \\ \cline{2-10}  & Others & 0.29 & Others & 0.00 & Others & 0.68 & Others & 0.18 & Others & 0.72 & Others & 0.38 \\ \hline \multirow{4}{*}{Microsoft LifeCam} & Cup & 0.58 & Cup & 0.91 & Cup & 0.29 & Cup & 0.00 & Cup & 0.00 & Cup & 0.02 \\ \cline{2-10}  & Mug & 0.00 & Mug & 0.00 & Mug & 0.02 & Mug & 0.60 & Mug & 0.22 & Mug & 0.40 \\ \cline{1-1} \cline{2-10}  & Others & 0.42 & Others & 0.09 & Others & 0.69 & Others & 0.40 & Others & 0.78 & Others & 0.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Top prediction proportions for different scenario, drinking vessel, and camera combinations (summarized over all distances). \(p_{o}\) has a range of between 0 and 1, with higher values indicating a greater level of agreement. \(p_{o}=0\) indicates zero agreement, and \(p_{o}=1\) indicates full agreement. However, a more complex agreement statistic is [PERSON]'s Kappa \(\kappa\)[44,45], which has a value in the range \(-1<\kappa\leq+1\) and compensates for the fact that an observed agreement in the top model prediction may have occurred by random chance.