Ring Fusion of Fisheye Images Based on Corner Detection Algorithm for Around View Monitoring System of Intelligent Driving

Abstract

In order to improve the visual effect of the around view monitor (AVM), we propose a novel ring fusion method to reduce the brightness difference among fisheye images and achieve a smooth transition around stitching seam. Firstly, an integrated corner detection is proposed to automatically detect corner points for image registration. Then, we use equalization processing to reduce the brightness among images. And we match the color of images according to the ring fusion method. Finally, we use distance weight to blend images around stitching seam. Through this algorithm, we have made a Matlab toolbox for image blending. 100% of the required corner is accurately and fully automatically detected. The transition around the stitching seam is very smooth, with no obvious stitching trace.

A Novel AVM Calibration Method Using Unaligned Square Calibration Boards

“…Chang et al [8] proposed a method to determine accurate vertexes of calibration boards when the edges of the calibration boards were blurred and jagged. Zhao et al [9] reduced the brightness difference among fisheye images and achieved a smooth transition around stitching seam. Two methods [8,9] utilized direct linear transform (DLT) to estimate the image transformation matrix required to generate an AVM image and focused on increasing the accuracy of the AVM calibration.…”

“…Vehicle man ufacturers use AVM calibration facilities to measure the calibration board locations ac curately, as shown in Figure 2 [7]. Various AVM calibration studies are also based o well-aligned calibration boards [8][9][10][11][12][13][14][15][16][17][18][19]. The details of these studies are provided in Sec tion 2.1.…”