Issue No. 06 - June (1994 vol. 16)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.295907
<p>Traditionally, zero crossings of the second derivative provide edge features for the classification of blurred waveforms. The accuracy of these edge features deteriorates in the case of severely blurred images. In this paper, a new feature is presented that is more resistant to the blurring process, the image, and waveform peaks. In addition, an estimate of the standard deviation /spl sigma/ of the blurring kernel is used to perform minor deblurring of the waveform. Statistical pattern recognition is used to classify the peaks as bar code characters. The noise tolerance of this recognition algorithm is increased by using an adaptive, histogram-based technique to remove the noise. In a bar code environment that requires a misclassification rate of less than one in a million, the recognition algorithm showed a 43% performance improvement over current commercial bar code reading equipment.</p>
bar codes; edge detection; parameter estimation; statistical analysis; bar code waveform recognition; peak locations; zero crossings; blurred waveforms; edge features; blurring process; waveform peaks; waveform deblurring; statistical pattern recognition; noise tolerance; histogram
E. Joseph and T. Pavlidis, "Bar Code Waveform Recognition Using Peak Locations," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 630-640, 1994.