Image distortion correction method based on bp neural network for fisheye lens
A BP neural network and fisheye lens technology, applied in the field of fisheye lens image distortion correction, can solve the problems of time-consuming and labor costs, and achieve the effect of improved efficiency and strong self-learning
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Embodiment 1
[0051] Such as Figure 18 As shown, the fisheye lens image distortion correction method provided by the present invention includes the following steps;
[0052] S1. If figure 1 As shown, assume that there are m rows and n columns of feature points evenly distributed on the paper A, use the fisheye lens to shoot the paper A, and obtain the captured image, as shown in figure 2 shown;
[0053] S2. Preprocessing the image, the obtained image is as follows image 3 , Figure 13 as shown in (a);
[0054] S3. Extract feature points from the preprocessed image, specifically as Figure 4 , Figure 13 as shown in (b);
[0055] S4. Among the extracted feature points, the horizontal distance x between the two adjacent feature points with the largest horizontal distance is the distance between the feature points in each column, and the vertical distance y between the two adjacent feature points with the largest vertical distance is The distance between feature points in each row; ...
Embodiment 2
[0071] This embodiment has carried out concrete simulation experiment to the method provided by the present invention, and its experiment process and result are as follows Figure 14~17 As shown, it can be seen that some of the input feature points cannot be well recognized due to the edge relationship, and some feature points are lost in the isolated point extraction. After the correction of the neural network, a better correction effect can still be obtained.
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