GRAIN SAMPLE QUALITY ASSESSMENT FUSING THE RESULTS FROM COLOR IMAGE AND SPECTRA ANALYSES

R. Tsenkova, M. I. Mladenov, M. P. Dejanov

Abstract: The paper presents the approaches, methods and tools for assessment of main quality features of grain samples which are based on color image and spectra analyses. Visible features like grain color, shape, and dimensions are extracted from the object images. Information about object color and surface texture is obtained from the object spectral characteristics. The categorization of the grain sample elements in three quality groups is accomplished using two data fusion approaches. The first approach is based on the fusion of the results about object color and shape characteristics obtained using image analysis
only. The second approach fuses the shape data obtained by image analysis and the color and surface texture data obtained by spectra analysis. The results obtained by the two data fusion approaches are compared.

Keywords: classifcation; color image analysis; data fusion; grain quality assessment; spectra analysis

Date published: 2017-09-15