# Effect of Camera Choice on Image-Classification Inference
## 4 Results and Discussion
### Scenario 1  
We begin the results with Scenario 1 because it involves a very plain background, so there is less chance that an image-classification model might be confused between the foreground object (i.e., the drinking vessel) and background objects. As discussed previously, we do not declare whether the ground truth of each vessel is a cup or a coffee mug, as this would be subjective. Instead, we are interested in whether the pretrained Resnet-18 model classifies each image as a cup, coffee mug, or some other class as a function of the camera used to acquire the image. Figures 11 and 12 illustrate stacked bar charts showing the proportion of images classified as a cup or a coffee mug by the pretrained Resnet-18 model as a function of distance for different combinations of drinking vessel and camera. In Figure 11, only the top prediction class of an image is considered; in Figure 12, the top five prediction classes are considered such that if a cup and/or coffee mug appears in the top five, the highest ranking of these two classes is selected. It is clear from Figure 11 that the proportion of images classified as a cup or a coffee mug decreases with distance, especially for Vessel 2. Although this is to be expected from the perspective that it is harder to recognize an object with increasing distance, it is important to note that a human observer would be able to easily recognize vessels in all the images taken for this study. We also see that the proportion of images classified as a cup or a coffee mug generally decreases with distance more quickly for the EMEET SmartCam C960 due to its relatively large FOV and less quickly for the Logitech C270 due to its relatively small FOV. On the contrary, the proportion of images classified as a cup or a coffee mug for the Logitech C270 is quite low for the larger vessels (i.e., Vessel 1 and Vessel 3) at a distance of 20 cm; this is because of the relatively small FOV of this camera, which increases the apparent size of objects, sometimes making them blurred and only just able to fit in the frame at small distances. Figure 11: Proportion of images classified as a cup or a coffee mug in the top prediction as a function of distance for different drinking vessels and camera combinations (scenario: Scenario 1). The bar charts for Vessel 3 (and, to a lesser extent, Vessel 1) are interesting from the perspective that the pretrained Resnet-18 model tends to classify this vessel as a coffee mug at small distances but then transitions to classifying it as a cup at larger distances. We refer to this effect as transitioning in the remainder of this paper. One possible explanation is that at larger distances, the increasing pixelation of the image section that contains the vessel leads to a difference in classification for this vessel's shape, texture, and/or color. Another possible explanation is unintentional bias in the training data labeling within ImageNet, i.e., training images with drinking vessels in the distance may appear more like cups than mugs to a casual observer due to their small apparent size. For Scenario 1, the classification output of the pretrained Resnet-18 model does not appear to depend significantly on the specific camera used to capture an image, apart from with respect to the expected differences due to the different FOVs of the cameras. In particular, the pretrained Resnet-18 model seems to classify each vessel relatively consistently as a cup or a coffee mug (for a specific distance), irrespective of the camera. This is not entirely surprising because Scenario 1 is very simple with an extremely plain background. However, looking in greater detail at Figures 11 and 12, the proportion of images classified as a cup or a coffee mug decreases with distance more quickly for the Microsoft Lifecam than for the Angetube962 and Logitech C920e, especially for Vessel 2, even though the three cameras have a very similar FOV.