Distortion Calculation Method Based on Image Processing for Automobile Lateral Mirrors

Abstract

The automobile lateral-view mirrors are the most important visual support for driver safety; therefore, it is important they have robust quality control. Typically, the distortion of a lateral-view mirror is measured using the JIS-D-5705 standard; however, this methodology requires an expert person to perform the measurements and calculations manually, which can induce measurement errors. In this work, a semi-automatic distortion calculation method based on image processing is presented. Distortion calculations of five commercial mirrors from different manufacturers were performed, and a comparative study was carried out between the JIS-D-5705 standard and the proposed method. Experimental results performed according to the JIS-D-5705 standard showed that all mirrors have a distortion lower than 5%, indicating that all meet the standard. On the other hand, the proposed method was able to detect that one of the mirrors presented an important distortion, which was not detected by the methodology proposed in the standard; therefore, that mirror should not meet the standard. Then, it was possible to conclude that the proposed distortion calculation method, based on image processing, has higher robustness and precision than the standard. In addition, an appropriate and effective behavior against changes in scale, resolution, and, unlike the standard, against changes in image rotation was also shown.

Computer vision-based image data processing of digital media video images with ultra-clear picture quality

“…In the 1960s, a computer program could convert two-dimensional images into three-dimensional structures for analysis, and from then on, computer vision research in threedimensional scenes was opened. The article proposes computer vision algorithms for the correction of distorted images during object projection by designing a true 3D display computer vision system [7]. This image processing technique has a higher correction accuracy compared with the past BP neural network and can be widely used in image processing.…”