Robust Estimation of Fundamental Matrix of Torr-m-estimators Based on Correlation Entropy

Abstract

The invention discloses a Torr-M-Estimators basic matrix robust estimation method based on correlation entropy. The Torr-M-Estimators algorithm weights the residuals of each matching pair, thereby reducing the outlier pairs with larger residuals Due to the influence of the estimation process of the fundamental matrix, different weight functions will have different optimization results; the correlation entropy function based on information theory has a greater advantage in robustness, and has a more obvious improvement in accuracy and robustness; Use the Hartley method for data normalization, linear least squares method to obtain the initial value of the basic matrix, the correlation entropy is the Torr-M-Estimators weight function, the eigenvalue decomposition limits the rank of the basic matrix to 2, the minimum iteration error and the maximum number of iterations are determined The loop end condition; experiments show that this method has good estimation accuracy and robustness, and is an important improvement method in the basic matrix solution step in the fields of 3D reconstruction, motion estimation, and matching tracking.

Description

technical field [0001] The invention belongs to the field of multi-view geometry in computer vision, and is a research invention for solving the basic matrix of the mathematical expression of epipolar constraints in stereo vision, and specifically relates to a Torr-M-Estimators basic matrix robust estimation method based on correlation entropy. Background technique [0002] In the field of computer vision, multi-view geometry is an important basis for computers to express the three-dimensional world through two-dimensional images. figure 2 As shown, the corresponding point m' of the image point m on another image plane must be above its epipolar line e'. The epipolar geometric relationship can be expressed by a matrix of order 3 and rank 2, which is called the fundamental matrix, also known as the F matrix (Fundamental Matrix), and the mathematical expression of the epipolar geometric relationship is m' T Fm=0, where m', m is the matching pair of corresponding feature point...

AI Technical Summary

Problems solved by technology

Based on this problem, people have done a lot of research. The solution methods are divided into three categories: linear method, iterative method and robust solution method. The linear solution method is fast, but when there are wrong matching points, the accuracy is very low. ; The iterative solution method has high precision, but it takes a long time to calculate compared with the linear solution method, and the result is still not very good when there are many error points; robust solution methods such as the minimum median method (LMedS), random sampling consistency method ( RANSAC) and its modified MLESAC, maximum a posteriori consensus algorithm (MAPSAC), M estimation method (Torr-M-Estimators), etc., these methods have a better effect on removing wrong matching points and bad points

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