Implicit Camera Calibration Using MultiLayer Perceptron Type Neural Network

Dong-Min Woo, Dong-Chul Park
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引用次数: 6

Abstract

This paper suggest a new camera calibration approach based on the neural network model. The proposed approach is shown to be very accurate because the neural network model implicitly contains all the physical parameters, some of which are very difficult to be estimated in the conventional explicit calibration methods. As the first step of this approach, this paper presents the camera calibration process which enables the coordinate transformation between 2D image points and points of a certain space in 3D real world. However, this approach is currently extended to be a general 3D camera calibration method in terms of 2 plane method. Experimental comparison of our method with well-known Tsai's 2 stage method is made to verify the accuracy of the proposed method.
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基于多层感知器型神经网络的隐式摄像机标定
提出了一种基于神经网络模型的摄像机标定方法。由于神经网络模型隐式地包含了常规显式校准方法难以估计的所有物理参数,因此该方法具有很高的精度。作为该方法的第一步,本文给出了实现二维图像点与三维真实世界中一定空间点之间坐标转换的摄像机标定过程。然而,该方法目前被扩展为一种通用的2平面三维摄像机标定方法。将本文方法与蔡氏二阶段法进行了实验比较,验证了本文方法的准确性。
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