Car Logo Recognition Matlab Realization Based on SIFT Algorithm
In recent years, automobile logo recognition technology has gradually become a research hotspot in the field of computer vision. Among them, the car logo recognition technology based on the SIFT algorithm has attracted much attention because of its strong robustness and invariance to transformations such as rotation and scaling. This article will introduce how to use matlab to realize the car logo recognition based on SIFT algorithm.
1. Introduction to SIFT algorithm
The SIFT algorithm (Scale-invariant feature transform) is a local invariant feature extraction algorithm. In the SIFT algorithm, the original image is first convolved multiple times through the Gaussian kernel to obtain image pyramids at different scales. Then, at each scale, the difference between the adjacent two layers of images is calculated by Difference-of-Gaussian (DoG), and the key points at this scale can be obtained. Next, calculate the gradient and gradient direction of the surrounding pixels for each key point, and then determine the corresponding sampling window size according to the scale of the key point to generate the feature vector of the key point.
2. Realization of Car Logo Recognition
- image preprocessing
First of all, for the image of the car logo to be recognized, preprocessing is required. Through the imread function in matlab, the image can be read in and converted into a grayscale image. Then, since the SIFT algorithm is sensitive to noise, the image needs to be smoothed, and the imgaussfilt function in matlab can be used to achieve Gaussian blur.
- Keypoint detection and feature extraction
After preprocessing, the key points and feature vectors can be extracted by the SIFT algorithm. It can be achieved using the vl_sift function in matlab. The function returns the position, scale, orientation, and feature vector of each keypoint.
- image matching
For the recognition task, it is necessary to compare the image to be recognized with the Logo in the known Logo image library one by one to find the best match. Eigenvector-based matching can be achieved using the vl_ubcmatch function in matlab. Should