Camera Calibrating Method for Smart Phone Based on Web Image

Abstract

Abstract. In this paper, we proposed a method of calibrating the camera of smart phone based on web image. In order to achieve the goal, a website and the corresponding app are built: the website is used for displaying the calibrating pattern; the app is used to grab the image in real time and guide the user to rotate the smart phone so as to acquiring more precise parameters. Although the rough extrinsic parameters can be computed for the guiding procedure, the high computational complexity inhibits it from applying in the app. Instead, due to the regularity of the calibrating pattern-a chessboard, the vanishing points can be obtained by estimating the Intersection of parallel lines. The rotating angle can be roughly estimated by vanishing points. The proposed method can be applied in a number of different applications which need to calibrate the camera.

Calibration of Smartphone’s Rear Dual Camera System

“…In this sub-section, image coordinates of the chessboard pattern node points in each photo are extracted. First, four corners of chessboard pattern are manually assigned in each photo for estimating the vanishing points of parallel chess lines (see Li et al, 2010;Ge et al, 2016;Caprile & Torre, 1990). Then, the cross ratio theory (Cao et al, 2010) was applied for detecting the initial position of the node points in each photo.…”

Semi‐automatic reconstruction of object lines using a smartphone’s dual camera

“…First, the calibration model was established by taking 13 dual photos for a 30 mm × 30 mm size chessboard pattern and then dividing the captured pairs of photos into two groups: wide-angle and telephoto camera photos. The image coordinates of the chessboard pattern's node points in each photo were extracted by manually assigning the four corners of the chessboard to estimate the vanishing points of the parallel chessboard lines (Caprile and Torre, 1990;Li et al, 2010;Ge et al, 2016). Then, the initial positions of the node points in each photo were detected by applying the cross-ratio theory (Cao et al, 2010).…”