# Progressive Dynamic Registration Method for Tile Maps Based on Optimum Multi-Features
## 2 Literature Review  
In terms of feature points, [PERSON] [7] proposed the [PERSON] corner point detection algorithm in 1977. He used a sliding window with a \(W\times W\) range and determined whether the current pixel point is an edge or a corner point based on the pixel change E when the window is moved in different directions. The algorithm calculates the change of E in only four main directions. Therefore, the algorithm is fast, but is sensitive to noise and edges and has directional limitations. Due to the shortcomings of [PERSON]'s algorithm, in 1988, [PERSON] [8] proposed the Harris operator which computes the pixel change when the window is moved in any direction. He used Taylor expansion to approximate the arbitrary orientation and determined whether a pixel point within the window is a corner point based on the two eigenvalues of the binomial function. [PERSON] proposed a feature extraction algorithm which is based on scale-invariant feature transform (SIFT) in 1999 [9] and improved it in 2004 [10]. The SIFT algorithm simulates images of different scales by constructing image pyramids, identifying the extreme points in the pyramid as candidate feature points, then removing edge effects and discarding some low-contrast points. To ensure the orientation invariance of feature points, each feature point has an orientation. Combining the orientation information of the neighboring sub-blocks of the current point forms a 128-dimensional descriptor. SIFT feature points have good stability and invariance to rotation, scale, and luminance changes, but the extraction speed is slow. In 2006, [PERSON] [11] proposed the speed-up robust features (SURF) algorithm, which improves the convolution speed with a box filter and becomes three times more efficient compared to the SIFT algorithm. Its principle will be described in detail below. In 2011, [PERSON] et al. [12] proposed the Oriented FAST and Rotated BRIEF (ORB) feature detection operator, which uses the FAST feature detection and BRIEF descriptors. The speed of the ORB algorithm is much faster than the SIFT algorithm and the SURF algorithm. Since then, many studies that use these feature points or improve on them to solve problems have been conducted, such as image stitching, image fusion, and remote sensing image registration. In 2019, [PERSON] et al. [13] used the SURF algorithm to extract image feature points and combined it with the improved RANSAC algorithm for matching screening to achieve fast stitching of a large number of images. Aiming at low accuracy of feature points matching, [PERSON] et al. [14] proposed an image-matching algorithm that is based on SURF and approximate nearest neighbor search. Fast-Hessian was used to detect the feature points and then SURF feature descriptors were generated. He used RANSAC to eliminate the mismatching points. The proposed matching algorithm improved the matching accuracy rate and real-time performance. In 2022, [PERSON] et al. [15] proposed the AT-SIFT algorithm with adaptive thresholding for image registration based on the SIFT algorithm to solve the problems of large computation and low matching accuracy, and used the adaptive thresholding FLANN algorithm and the improved RANSAC algorithm for matching filtering, which led to a large improvement in registration accuracy and efficiency. In terms of feature lines and feature surfaces, in 1968, [PERSON] proposed the Sobel operator in a workshop, and the algorithm differentiates or second-order differentiates each pixel of the image according to the different reasons for edge formation, so that the points with significant changes can be detected. Its extraction speed is fast, but the extracted edges are often discontinuous. The Laplace operator is a second-order differential linear operator with stronger edge localization capability. Since the operator is very sensitive to image noise, it is often used by first smoothing the image with a Gaussian smoothing filter, a method called Laplacian of Gaussian (LoG).