# Research on a Fast Image-Matching Algorithm Based on Nonlinear Filtering
## 1 Introduction  
With the widespread application of artificial intelligence technology, the influence of computer vision in the industrial field is increasing, and image matching based on vision theory has become a research hotspot in the field of computer vision. Image matching is of great value and significance in applications such as 3D scene reconstruction, target detection and recognition, video content tracking, pose estimation, motion estimation, and image recovery [1]. The key to image matching is feature band point extraction and description, in which the SIFT (Scale-invariant Feature Transform) algorithm was proposed by [PERSON] [2] in 2004. Although the SIFT algorithm performs well in many applications, it also has some drawbacks. First, the computational complexity of the SIFT algorithm is high, especially when matching in large-scale image databases. Secondly, the feature vectors generated by the SIFT algorithm have high dimensionality and require a large amount of storage space. In addition, the SIFT algorithm is less stable to image transformations and more sensitive to occlusions and noise. Finally, the selection of key parameters in the SIFT algorithm is difficult and requires experience and trial and error to determine the appropriate values. [PERSON] et al. [3] adopted the GLOH (Gradient Location and Orientation Histogram) descriptor and used polar coordinates for feature descriptor construction, which has more accurate orientation estimation, more stable descriptor generation, and shorter descriptor lengths relative to the SIFT algorithm, and under the condition of reduced computation, the computation remains large and sensitive to noise and occlusion. For the problem of the poor real-time performance of image-matching algorithms, [PERSON] et al. proposed the ORB algorithm [4], a feature extraction algorithm based on FAST corner point detection and a BRIEF binary descriptor, which has a great improvement in speed, but performs poorly in dealing with images with high texture repetitions or lowtexture regions, and the short length of the descriptor may lead to an increase in matching errors. [PERSON], [PERSON] et al. [5] proposed reducing the number of layers of a Gaussian pyramid and improving the SIFT operator to improve the time efficiency of feature point detection; [PERSON] and [PERSON] [6] proposed extracting the corner points in an image by using the Harris corner-detection algorithm and describing these feature points using concentric circle descriptors, which reduces the total time needed for feature extraction and feature matching, but thenumber of feature points extracted is reduced; [PERSON] et al. [7] proposed a corner point indicatorthat improves upon Harris and can obtain a large number of feature points, but in terms of the descriptor and matching method, it is the same as the traditional SIFT matching algorithm, which will consume a lot of resources and time. In order to improve the matching correctness of the image-matching algorithm, [PERSON] et al. [8] proposed the BRISK algorithm, which uses a binary descriptor, significantly reduces computational complexity, and provesmatching correctness, but its performance is not excellent in some extreme lighting changes, under high texture repetition, or in low-texture regions. [PERSON] and [PERSON] [9] proposed a SIFT matching algorithm combined with RANSAC; this method uses the SIFT algorithm to extract and match the feature points of an image sequence, and then uses the RANSAC algorithm to eliminate the mismatched points, which improves the matching correctness rate to a certain extent, but there is also the problem of high resource occupancy in the process of descriptor generation. [PERSON] et al. improved image quality by adjusting the contrast thresholding and scaling the image, which they achieved by adjusting the size and contrast thresholding factors to produce a sufficient number of keypoints and enhance the correctness of the feature point matching, even in smooth, low-contrast, or small regions [10].