Fractional scale transform and fractional Mellin transform for scale, rotation, and translation invariant pattern recognition to discriminate between 30 phytoplankton species
DOI:
https://doi.org/10.31349/RevMexFis.72.051301Keywords:
Computer vision, fractional Mellin transform, fractional Scale transform, pattern recognition, phytoplanktonAbstract
The fractional scale transform is introduced and studied to develop a new pattern recognition system invariant to scale, translation, and rotation. This system was also implemented with the fractional Mellin transform, and the results were compared with those obtained with the fractional scale transform. The analysis was performed to classify 30 phytoplankton species. For both transformations, optimal orders were found for each species. This study implemented nonlinear correlation and adaptive nonlinear correlation for the classification stage. The system achieved a mean accuracy of 0.998 with nonlinear correlation and 0.999 with adaptive nonlinear correlation.
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