Camera calibration optimization algorithm that uses a step function.

Area of Science:

• Computer Vision
• Machine Vision
• Image Processing

Background:

• Camera calibration is crucial for machine vision tasks like 3D reconstruction and measurement.
• Traditional calibration models apply uniform distortion parameters, leading to high pixel reprojection errors at image edges.
• These edge errors compromise overall camera calibration accuracy.

Purpose of the Study:

• To present a novel camera calibration optimization algorithm.
• To address the limitations of traditional models in handling edge distortions.
• To improve the accuracy of machine vision systems through enhanced calibration.

Main Methods:

• Developed a flexible method to divide images into distinct center and edge regions based on pixel distance from the image center.
• Implemented a step function to automatically determine the optimal boundary between regions.
• Rebuilt the calibration model to apply different distortion parameters to the center and edge regions separately.

Main Results:

• The new model doubles the number of distortion parameters compared to traditional methods.
• Achieved significantly improved pixel reprojection accuracy, particularly at image edges.
• Experimental simulations demonstrated superior flexibility and accuracy over conventional calibration techniques.

Conclusions:

• The proposed algorithm effectively optimizes traditional camera calibration models.
• Separate calibration for center and edge regions enhances overall accuracy and reduces reprojection errors.
• This method offers a more flexible and accurate approach for machine vision applications.