Wavelets in the analysis of seed image similarity: an approach using the Hurst directional exponent

Abstract

Modernization is present in all fields of knowledge. The wavelet transform and the Hurst exponent are tools that have fundamental importance in many of these advances. In the present study, the wavelet decomposition technique was combined with the Hurst exponent calculation to analyze X-ray images of seeds and thus classify them as full, slightly damaged or damaged. To calculate the Hurst exponent the mean and median were used as measurements of position. A support vector machine was used to validate the proposed method. For the full, damaged and slightly damaged seed groups, the average accuracy of the method, using the mean as measure position, was 74.5%, and using the median was 57.05%. For the full and damaged seed groups, the average accuracy using the mean was 99.76%, and using the median was 80.93%. For the slightly damaged and damaged seed groups, the average accuracy, using the mean as measure of position, was 99.26%, and the median was 76.22%. When analyzing seeds with slight damage, we observed a decrease in accuracy because the classification of the X-rays was subjective. Therefore, for the image database used in this study, the proposed methodology is efficient for automatic classification.