# Effect of Camera Choice on Image-Classification Inference
## 3 Materials and Methods
### Image Capture  
As illustrated in Figure 4, images were captured at distances of 20 cm, 30 cm, 40 cm, 50 cm, and 60 cm between the camera lens and the frontmost point of the vessel. This set of distances was chosen because images are often blurred for a distance less than 20 cm,  
Figure 3: Drinking vessels used in the study (camera: Logitech C920e, distance: 20 cm, scenario: Scenario 1, image resolution: 320 \(\times\) 240).  
and the image-classification model employed will usually fail to recognize the target vessel correctly at a distance greater than 60 cm, at least for scenarios where there are other background objects of interest in the image.  
Images of Vessel 1 taken at different distances are illustrated in Figure 5. At 60 cm, Vessel 1, which is a relatively large drinking vessel, only occupies a relatively small section of the image space, although it is still clearly identifiable as a drinking vessel by a human observer.  
For each image taken, the camera was positioned such that the target vessel was in the center of the field of view with the plane of the handle perpendicular to the notional line between the camera and vessel. This ensured that the target vessel was clearly identifiable by a human observer as a drinking vessel and as the principal object in the image. We did not make any changes to the configuration settings of the cameras (apart from exercising different capture resolutions) and relied on default out-of-box settings. When capturing an image, the camera autofocus mechanism, if supported by the specific camera, was allowed to settle before the image was taken, thus ensuring crisp, non-blurred images.  
For each camera, images were captured for each combination of the following:  
* The supported resolutions of the camera (see Table 1);
* The five target vessels;  
Figure 4: Distance values used for image capture.  
Figure 5: Images of Vessel 1 at different distances (camera: Logitech C920e, scenario: Scenario 1, image resolution: 320 \(\times\) 240).  
* The five distances of 20 cm, 30 cm, 40 cm, 50 cm, and 60 cm;
* Three scenarios, where each scenario comprises a different background to the target vessel.  
Therefore, with reference to the resolutions supported by each camera in Table 1, the complete dataset to be used for image-classification inference comprised \((13+7+16+13+9+4)\times 5\times 5\times 3=4650\) images.  
The three different scenarios/backgrounds are illustrated in Figure 6. The use of different scenarios facilitated a larger overall dataset for analysis and also allowed an investigation into whether the image backgrounds affect the image classification to a significant degree. Scenario 1 has the quietest background, and Scenario 3 has the busiest background, but for all scenarios, it is clear to a human observer that the foreground drinking vessel is the primary object of interest in the image.  
The three scenarios are all indoors since it was easier to maintain consistent environmental conditions, particularly with respect to lighting, while capturing the complete image dataset. However, in order to guard against any residual changes to environmental conditions and the consequent impact on assessing the effect of the camera type on image-classification inference, the following methodology was adopted:  
* For each scenario, vessel, and distance combination, a camera was used to capture images of the vessel for all supported resolutions and then swapped out for another camera.
* For each scenario, vessel, and distance combination, the order in which cameras were used was randomized.  
This frequent cycling of cameras to guard against residual changes to environmental conditions is illustrated in Figure 7.  
The complete image-capture process is illustrated in the flow chart of Figure 8.  
Figure 6: Images of Vessel 1 in different scenarios (camera: Logitech C920e, distance: 20 cm, image resolution: 320 \(\times\) 240).  
Figure 7: Image-capture methodology involving frequent cycling of cameras and randomized ordering of camera use.