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Guo Yanhua, Zhou Sihua, Zhou Xiaodong, Chen Bojun and Wang Shaohui

Abstract

Defect Detection is one of the most important parts of Automatic Identification and Data transmission. Quick Response code (QRcode) is one of the most popular types of two-dimensional barcodes. It isachallenge to detect defect of various QRcode images efficiently and accurately. In this paper, we propose the procedure byaserial of carefully designed preprocessing methods. The defect detection procedure consists of QRcode identification, QRcode reconstruction, perspective transformation, image binarization, morphological operation, image matching, and Blob analysis. By these steps, we can detect defect of different types of QRcode images. The experiment results show that our method has stronger robustness and higher efficiency. Moreover, experiment results on QRcode images show that the prediction accuracy of proposed method reaches 99.07%with an average execution time of 6.592 ms. This method can detect defect of these images in real time.

Open access

Jia-Bao Liu, Jing Zhao, Shaohui Wang, M. Javaid and Jinde Cao

Abstract

A topological index is a numeric quantity associated with a network or a graph that characterizes its whole structural properties. In [Javaid and Cao, Neural Computing and Applications, DOI 10.1007/s00521-017-2972-1], the various degree-based topological indices for the probabilistic neural networks are studied. We extend this study by considering the calculations of the other topological indices, and derive the analytical closed formulas for these new topological indices of the probabilistic neural network. Moreover, a comparative study using computer-based graphs has been carried out first time to clarify the nature of the computed topological descriptors for the probabilistic neural networks. Our results extend some known conclusions.