Traditional barcode recognition equipments are very expensive and very often do not fit common users due to their large
scale. A novel low-cost method is proposed in this paper to implement automatic recognition of the barcode information
from images captured by a CMOS camera without the need for additional light source. Firstly, the barcode orientation is
recognized based on the character of bar-like image. The barcode sequence is then generated by the image projection.
Finally, the barcode information is recognized according to the encoding rule. The overall success rate is up to 90%,
justified by 400 datasets provided by multiple individuals.
This paper presents a new image segmentation algorithm based on the pulse coupled neural network (PCNN) and histogram method for infrared images. The proposed algorithm abandons entirely the mechanism of the time exponential decaying function and uses the results of the gray-level histogram analysis as the interior thresholds of PCNN, meanwhile, it keeps the advantage of briding small spatial gaps and minor intensity variations. Experiment results demonstrate that the proposed algorithm can get more complete region and edge information in infrared images. It is also of much lower complexity and of high speed than the original one.
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