Recognition of QR Code Image on Cylinder by Conic Segmentation

• Main Authors: Yu-xun Lin, 林鈺勛
• Other Authors: Kuen-tsai Lay
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/49451994156440311875

碩士 === 國立臺灣科技大學 === 電子工程系 === 102 === In recent years, QR codes have found a wide range of applications in business and industry. Moreover, their popularity and function are still growing (fast). Therefore, the processing of the images of QR codes is an important task. In this thesis, we try to solve a problem encountered in QR decoding when the QR code is pasted on the surface of a cylindrical container. The pasting can even be tilted. When a picture is taken on such a QR code, the picture is no longer in a rectangular shape, which is supposedly the “right” shape that any QR code should take, due to warping from the image capturing mechanism by the camera. This warping translates into serious distortion of the original QR-code image (also referred to as QR image, for short) and thus causes failure in its decoding. In this thesis, we try to deal with this kind of problem. Realizing that originally a QR code is a square array of black and white modules, each of which is a square-shaped cell, we try to segment the distorted QR image into separate cells, which are typically non-rectangular. Prepare a blank template alongside. Then, the color (more precisely speaking, intensity (black or white)) of each cell is duplicated into the corresponding cell in the template. After all cells in the template are thus painted (to be black or white), we have a square array of black and white modules. The result looks like a normal QR image, which is not distorted in shape, although some of its modules may be wrong in color as compared to the original QR code. The transformation of the distorted QR image into a square array of black and white modules, as described above, is referred to as QR image rectification. It is obvious that a QR code contains a set of horizontal lines and vertical lines. It is exactly those lines that segment the QR code into modules. When those lines are warped due to the cylinder-pasting, they of course are no longer straight lines. Instead, they become curves. The basic idea for our QR image rectification is to model those curves as conic sections and then use those conic sections to segment the QR image into cells. It is for this reason that we call our method by the name of “conic segmentation”. More specifically speaking, we pre-process the QR image to find its edge points (by Sobel edge detection). During the process, we also need to perform dilation and erosion, etc., to decompose the QR image into some connected components. Then, we will fit the edge points that are determined to have belonged to the same curve into a conic section, by the least-square-error fitting. Finally, those conic sections would naturally segment the whole QR image into many small cells, wherein each cell corresponds to a QR module. In this way, the rectification is completed. Experiments show that the proposed scheme is effective, in the sense that lots of failed QR decoding becomes successful after the rectification.