## 2 Background Information for Method Development
### Image Transformation
In this type, it is assumed that factors such as image orientation, lengths, angles, parallelism, and straight lines remain unchanged. In other words, this type of transformation only has two degrees of freedom. If relative translation and the rotation of the camera lens regarding the Y-axis occur together, it is called Euclidean (rigid). In this type, factors such as lengths of edges, angles, parallelism, and straight lines remain unchanged. In other words, this type of transformation has three degrees of freedom. Figure 7: Changes in the camera’s position and orientation can cause image transformation. The third type, similarity, occurs when the relative rotation and scale of the second image changes in relation to the first image. This means that the second picture, in addition to the rotation of the camera around the Y-axis, was captured from a different position relative to the scene (on the Y-axis). In this type, angles, parallelism, and straight lines remain unchanged. In other words, this type of transformation has four degrees of freedom. The fourth type, called affine, occurs when a camera that takes the second picture rotates around two coordination axes such that parallelism and straight lines remain unchanged. In other words, this type of transformation has six degrees of freedom. The fifth type, which is called projective (homography), occurs when a camera rotates around one or more coordination axes such that only the straight lines remain unchanged. In other words, this type of transformation has eight degrees of freedom. The image matching algorithms need to deal with these image transformations and bring them into agreement with the reference picture. To have a better understanding, [PERSON] [1] suggested a diagram to visualize these different types of transformations (Figure 8). Propose an Approach Based on Localization Systems to Remotely Repeat and Retrieve the Camera's Position and Orientation to Decrease Image Transformation (Sensor-Based Tracking Systems)  
As was previously indicated, at temporary and messy places such as construction sites, one way to potentially decrease the impact of image transformation is a system that navigates the crews to hold the camera in a single position and orientation without using a tripod or a fixed-point camera. For this purpose, an accurate positioning and orientation system is required. Sensor-based techniques, independent from vision techniques, could be suitable candidates. In other words, sensor-based approaches use non-vision sensors to track a scene. Mechanical sensors, magnetic sensors, GPS (Global Positioning System), and ultrasonic and inertia sensors are some examples of non-vision tracking sensors. The following paragraph introduces the limitations of these types of sensors. GPS has low user coverage in an indoor environment (4.5%) [45]. It requires direct lines of sight from a user's receiver to at least three orbital satellites [46; 47] and its signal accuracy is degraded by occlusion. Wi-Fi has high user coverage indoors (94.5%) [45], with 15 to 20 m accuracy in indoor environments [45]. Bluetooth has 75% accuracy for partial coverage and 98% accuracy for full coverage in a room, while target devices need to be stationary for long periods of time [48]. Ultrasonic sensors are sensitive to temperature, occlusion, and ambient noise, require significant infrastructure, and have a low update rate [47]. Infrared is short range and limited because of line-of-sight requirements, as seen in Active Badge [49]. Radio frequency (type of signals, IEEE 802.11, WLAN) has a median accuracy of 2 to 3 m [50]. Inertial sensors are prone to drift and require constant recalibration [51]. Radio frequency (type of signals, UWB) emits ultra-wideband signals that can pass through walls and have high accuracy [52; 53]. Figure 8: Two-dimensional (2D) geometric image transformations (adapted from [PERSON] [1]).