# Effect of Camera Choice on Image-Classification Inference
## 1 Introduction
An equivalent way of expressing this is to state that the AOV affects the perceived size of objects within images. These concepts are illustrated in Figure 2.  
Figure 1: Typical components and data flows of a digital camera. In this paper, we investigate the effect of the specific camera used to capture an image on classification inference. To do this, we capture images of certain objects that can be recognized by an established, independently pretrained image-classification model using different cameras while keeping the environmental conditions (e.g., lighting) as constant as possible. We then feed these images into the pretrained image-classification model to observe any differences in the classification output. It is important to note that the main target object in the images is easily recognizable by a human observer; we do not attempt to intentionally confuse the pretrained model in any way. In particular, the lighting is set to facilitate easy identification of the main target object in the images by a human observer. We expect the classification to depend on the camera AOV because this impacts how dominant the target object is in the captured image and how many other peripheral objects are captured in the image. Nevertheless, for cameras with similar or even identical AOV, it is of great interest to explore whether comparable images taken with these cameras can result in different classification outputs when using the same specific pretrained model. Note that we do not perform any model training in this paper; rather, the images we capture are used for inference purposes only in concert with a pretrained model. To our knowledge, there is no similar publicly available study on the effect of the specific camera used to capture an image on classification inference, despite the potentially significant implications for many fields, including industrial inspection. Although this study was exploratory, and there was always the possibility from the outset that the actual observed differences between cameras would transpire not to be particularly significant, we were, in fact, able to demonstrate that comparable images taken with different camera types (even those with a similar AOV) can result in different classification outputs when using the same specific pretrained CNN model. The remainder of this paper is organized as follows. In Section 2, we discuss related work, particularly the recently growing field of investigating the robustness of computer vision to natural image degradations, of which differences in camera types are an important yet neglected area. Section 3 addresses the materials and methods we adopted for acquiring comparable images from different camera types and classifying them according to a specific pretrained CNN model. Results from these experiments are depicted graphically in Section 4 and analyzed according to their significance using [PERSON]'s Kappa (\(\kappa\)) statistic. We demonstrate that comparable images taken with different camera types (even those with a similar AOV) can result in different classification outputs when using the same specific pretrained CNN model. Conclusions and directions for further research are discussed in Section 5. Figure 2: Simplified demonstration of the effect of camera Angle of View (AOV) on image capture (assumes pinhole lens).