Abstract
Halftone classification is a primary requisite for the perfect reconstruction of binary patterns during inverse halftone process. Majority of the halftone classification techniques are either limited to error diffused halftone or to limited categories, and cannot be generalized to all halftone versions. In this scenario, a new classification approach is proposed based on the premise that the stochastic analysis can uniquely characterize the halftone patterns. The proposed scheme exploits inherent association of halftone patterns and stochastic geometry, and utilizes its spatial and spectral parameters for feature vector construction. Extreme learning machine based multi-classifier model is adopted resulting in rapid and accurate classification. A digital halftone database comprises of 96 reference images along with the 21 varieties of halftone and multitone class is developed. From extensive analysis, it has been validated that the proposed scheme can achieve a 100% classification rate on many halftone versions and exhibit superior performance over the exiting classification techniques.
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Guo, J.M., Seshathiri, S. Generalized halftone classification approach using stochastic analysis. J Ambient Intell Human Comput 14, 14907–14919 (2023). https://doi.org/10.1007/s12652-018-0812-5
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DOI: https://doi.org/10.1007/s12652-018-0812-5
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