I. Introduction
In today's digital age, the importance of image processing technology is self-evident. It plays a key role in fields such as driverless driving, computer vision, and facial recognition. As an important part of image processing, image stitching algorithm is an important means to achieve a wide field of view image. Today we will explain how to use SIFT, homography, KNN, Ransac and other technologies to implement image stitching algorithms in the Python and OpenCV environments.
The primary purpose of what follows is to provide an entry-level guide to understanding these fundamental concepts and their application in practice. We hope that through this article, readers can gain a deeper understanding of image processing and try some hands-on examples themselves.
The remainder of this article will be divided into three main parts, first to introduce and explain all relevant concepts and methods, secondly to demonstrate through example code, and finally to review and summarize what has been learned.
2. Basic knowledge and methodology analysis
1. SIFT (Scale Invariant Feature Transform)
SIFT, the full name of Scale-Invariant Feature Transform, is a method of detecting and describing local image features, proposed by David Lowe in 1999. The advantage of this method is that it is invariant to image scale, rotation, brightness changes, etc., and is also stable to image noise and small viewing angle changes, which is very useful for image stitching.
2. Homography
Homography is a transformation that describes the correspondence between pixel points between two images. It can be solved from the correspondence between feature points through a method called direct linear transformation (DLT). We can project each pixel in one image into another image through this transformation relationship, which is very critical for image splicing.