Scaling Theorems for Zero Crossings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Uniqueness of the Gaussian Kernel for Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision, Graphics, and Image Processing
Feature detection from local energy
Pattern Recognition Letters
Recognition and restoration of blurred bilevel waveforms
Recognition and restoration of blurred bilevel waveforms
A syntactic algorithm for peak detection in waveforms with applications to cardiography
Communications of the ACM
Digital Picture Processing
Detection, Localization, and Estimation of Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Twenty Years of Document Image Analysis in PAMI
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Blur estimation for barcode recognition in out-of-focus images
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Deblurring of One Dimensional Bar Codes via Total Variation Energy Minimization
SIAM Journal on Imaging Sciences
Robust 1-d barcode recognition on camera phones and mobile product information display
Mobile Multimedia Processing
Two-dimensional bar code out-of-focus deblurring via the Increment Constrained Least Squares filter
Pattern Recognition Letters
A directed graphical model for linear barcode scanning from blurred images
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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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.