Optimization analysis of binocular stereo vision calibration technique based on BP algorithm
Camera calibration refers to one of the main ways to extract many features of an image from multiple two-dimensional images,which constitutes three-dimensional image information,and the degree of fidelity of three-dimensional image reconstruction is mainly determined by the accuracy of camera calibration.In the traditional camera calibration algorithm,many assumed parameters are often introduced,resulting in a relatively large amount of computation of the algorithm,and the accuracy of 3D reconstruction is also relatively low.In order to fundamentally solve the above defects and deficiencies,BP neural algorithm is introduced as a way to improve the accuracy and speed of camera calibration and effectively increase the degree of fidelity of three-dimensional reconstruction,and the binocular stereo vision calibration technology based on BP neural network algorithm is proposed.The experimental data show that after the introduction of BP neural network algorithm through binocular calibration technology,the accuracy of 3D reconstruction is significantly improved,and the convergence speed of the algorithm is fast,which verifies that the algorithm has strong correctness and feasibility.
camera calibrationneural networkbinocular stereo visionoptimization research
万方数据
维普
