出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：This paper chiefly focuses on calibration of depth camera system, particularly on stereo
camera. Owing to complexity of parameter estimation of camera, i.e., it is an inverse problem
the calibration is still challenging problem in computer vision. As similar to the previous
method of the calibration, checkerboard is used in this work. However, corner detection is
carried out by employing the concept of neural network. Since the corner detection of the
previous work depends on the exterior environment such as ambient light, quality of the
checkerboard itself, etc., learning of the geometric characteristics of the corners are conducted.
The pro-posed method detects a region of checkboard from the captured images (a pair of
images), and the corners are detected. Detection accuracy is increased by calculating the
weights of the deep neural network. The procedure of the detection is de-tailed in this paper.
The quantitative evaluation of the method is shown by calculating the re-projection error.
Comparison is performed with the most popular method, Zhang’s calibration one. The
experimental results not only validate the accuracy of the calibration, but also shows the
efficiency of the calibration.
关键词：Calibration; Neural network; Deep learning; Re;projection error; Depth camera
