qrcoderecognitionbasedonimageprocessing-資料下載頁
2024-09-29 20:15本頁面
【導讀】YunhuaGu,WeixiangZhang,Introduction. ExperimentResults. Conclusion. quicklyidentified.aknownpoint.Thereferences[11]and[12]lookforposition. Thereference[13]usesOTSUmethodfor. code.situation.whiteofthemodule'sratiois1:1:3:1:1.QRcodeimage.
【正文】 irstly, make sure the version number of the QR code based on the decoding algorithm given in the national standard and symbol structures of QR code itself. ? Secondly, divide equally the QR image into n x n small grids according the version number, resample the center of each grid as the sampling point and get the normalized QR code symbol. 25 Image Normalization ? Thirdly, decode the standard QR code symbol according the National Standard Method of Quick Response Code after image resampling. 26 Experiment Results 27 Algorithm Average Time Recognition Rate OTSU 90% Geic algorithm use in threshold Bimodal histogram 80% Niblack(Window size is 5) Experiment Results ? In the same programming environment used for the time to determine the rotation angle. ? As opposed to in general Hough transform and in hollowing algorithm, the time used in algorithm in this paper time , shortened by 245 times and times. 28 Conclusion 1. In the rotation process, the QR code symbol was extracted and the surrounding noise information can be effectively eliminated. 2. In terms of the geometric correction, not using Hough transform, but using the coordinates of known points to infer the fourth vertex of QR code, and this method reduces the time plexity. 3. In terms of image normalization, using the principle of image scaling to reduce the error caused by average means. 29
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