**2. Theoretical Background for Dual Camera-Based Image Correction and the Proposed Method of Distance Measurement**

#### *2.1. Road Lane Detection Method*

#### 2.1.1. Theoretical Background for Lane Detection

The ROI in a camera-captured image is the region containing the information relevant to the task at hand. As the range of the scenery captured by a camera affixed within a vehicle remains constant, the ROI in particular images must be obtained by removing the corresponding irrelevant regions.

Cameras usually capture images in the red, green, and blue (RGB) format, which comprises three channels. Grayscale conversion of such images produces monochromatic images, which comprise a single channel. As images converted via this method retain only brightness information, the amount of data to be processed is reduced by two-thirds, increasing the computational speed.

The canny edge detector is an edge detection algorithm that utilizes successive steps such as noise reduction, determination of the intensity gradient of the image, non-maximum suppression, and hysteresis thresholding [27]. Owing to its multi-step mechanism, it performs better than the methods that use differential operators (e.g., the Sobel mask).

The Hough transform is a method for transforming components in the Cartesian coordinate system to those in the parameter space [28]. Straight lines and points on the Cartesian coordinate system are represented by points and straight lines, respectively, in the parameter space. Thus, points of intersection between straight lines in the parameter space can be used to search for straight lines passing through a given set of points in the Cartesian coordinate system.

The hue, saturation, value (HSV) format is a color model that represents an image in terms of hue, saturation, and value. It is particularly effective for the facile expression of desired colors because its operational template agrees with the human mode of color recognition.

Perspective transform facilitates the modeling of homography using a 3 × 3 transformation matrix. The perspective of any image can be removed via a geometric processing method by relocating the pixels of the image.

The sliding window method uses a sub-level array of a certain size called a window and reduces the computational load for calculating the elements in each window in the entire array by reusing (rather than discarding) redundant elements.

The curve fitting method involves fitting a function to a given curve representing the input data. A polynomial function is most commonly used for this purpose. Furthermore, the input data can be approximated using a quadratic function by employing the leastsquares approximation method.
