The Back Propagation (BP) neural network has the problems of low accuracy and poor convergence in the process of binocular camera calibration. A method based on BP neural network optimized by improved genetic simulated annealing algorithm (IGSAA-BP) is proposed to solve these problems to complete the binocular camera calibration. The method of combining Gaussian scale space and Harris corner detection operator is used for corner detection. A matched algorithm of homonymous corner is proposed by combining point-to-point spatial mapping and grid motion statistics. The pixel values of the homonymous corner and three-dimensional coordinate values are taken as the input and output of BP neural network respectively. The crossover and mutation probability of genetic simulated annealing algorithm and the annealing criterion are improved, the IGSAA-BP neural network is used to calibrate the binocular camera. The average calibration accuracy of BP neural network and IGSAA-BP neural network is 0.71mm and 0.03mm, respectively. The average calibration accuracy of binocular camera is improved by 96%. The iteration speed is increased by 20 times and global optimization ability is improved. It can be seen that the IGSAA-BP neural network can improve the calibration accuracy of binocular camera and accelerate convergence speed.

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Research on the Calibration of Binocular Camera Based on BP Neural Network Optimized by Improved Genetic Simulated Annealing Algorithm

Omnidirectional three-dimensional (3D) measurement based on the binocular omnidirectional vision system attracts increasing attention in computer vision. A 360° horizontal field of view (FOV) and 180° vertical FOV are obtained by the binocular omnidirectional vision system with only a pair of images. However, a nonlinear relationship exists between points on image planes and in 3D space for the binocular omnidirectional vision system, and traditional calibration methods are no longer applicable. This study proposes a hybrid calibration approach, which effectively fuses the unified camera model and the backpropagation neural network with a virtual 3D target, in which the neural network improved with genetic algorithm compensates all kinds of distortions and errors. First, the unified camera model is used to calibrate the intrinsic parameters, extrinsic parameters, and a transformation matrix. Second, the pixel coordinates on image planes are converted to 3D coordinates by 3D reconstruction through binocular vision. The errors between actual and calculated 3D coordinates are obtained. In addition, the neural network improved with genetic algorithm is established and trained with pixel coordinates on image planes and errors. Experimental results indicate that the average measurement errors in the X and Y directions of the central area are respectively reduced to 0.1057 and 0.1548 mm, and those of the edge area are reduced to 0.1992 and 0.2748 mm. Compared with mathematical and neural network methods, the hybrid calibration method provides more stable, robust, and accurate results. It is proven suitable for complex calibration scenes with large FOV, high distortion, and strong nonlinearity.

A Hybrid Calibration Method for the Binocular Omnidirectional Vision System

Original scientific paper As the basic part of the mechanical transmission, gears should have high transmission accuracy, so the gear’s precision measurement is a key technology. Based on the existing research, the computer vision technology is introduced in gear test. According to the definitions of technical indexes, such as pitch deviation, tooth thickness deviation, etc., through analysis and research, a new measurement method is proposed, so as to complete the determination of the modulus, tooth number and total accumulated pitch deviation of a gear. In the proposed methods, those indexes such as the pitch and tooth thickness, are measured in arc length, rather than in chordal length, so that they are consistent with their definitions. And the measured points can be positioned on the reference circle of a gear while the pitch and total accumulative pitch deviations are examined. Finally, a gear is measured with the proposed method. The main test is obtained as follows. The single pitch deviation is 10,3 μm, total cumulative pitch deviation is 44,8 μm, and tooth thick deviation is 5,2 μm. The experiment demonstrates that the proposed method is feasible and effective, and can meet the precision need of engineering practice. This study provides a new solution method for the gear’s on-line measurement.

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Applications of computer vision in measuring total cumulative pitch deviation of a gear

In order to calibrate camera with radial distortion model, a novel approach based on the hybrid neural network with rotational weight matrix and self-adaptive genetic-annealing algorithm is proposed. Firstly two sorts of neural networks are structured, whose weights are corresponding to the camera's extrinsic parameters and intrinsic parameters without and with radial distortion, so the structured neural networks coincide with the camera's pin hole model and radial distortion model respectively. And the performance index is obtained from the square of 2-norm of the difference between the vector consisted of network's outputs and the tested retinal coordinates of corresponding feature points projected in image planes. At the same time, a genetic-annealing algorithm is introduced into the solving-programming, where the probabilities of crossover and mutation are tuned according to the distance density of individuals, and unequal probability matching strategy is adopted. Thus while the system come to the equilibrium, the camera's parameters with radial distortion are obtained in the light of network's weights. The experimental results illustrate that the proposed approach is robust, and has the advantages over existing algorithms in calibration precision, and orthogonality of rotational matrix, in particular the precision of intrinsic and extrinsic parameters of camera, which provides a practical scheme for calibrating camera with radial distortion model.

Chao Hu

Papers

Nonlinear camera model calibrated by neural network and adaptive genetic-annealing algorithm