Handbook of Machine Vision
Wavelet based multi-spectral image analysis of maize leaf chlorophyll content
Computers and Electronics in Agriculture
MaZda A Software for Texture Analysis
ISITC '07 Proceedings of the 2007 International Symposium on Information Technology Convergence
MaZda-A software package for image texture analysis
Computer Methods and Programs in Biomedicine
Local polynomial approximation for unsupervised segmentation of endoscopic images
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part II
An intelligent automated recognition system of abnormal structures in WCE images
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
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This paper presents an algorithm for analyzing barley kernel images to evaluate cereal grain quality and perform grain classification. The input data comprised digital images of kernels obtained from an optical scanner. The algorithm identified individual kernels' smooth and wrinkled regions, described their orientation relative to the axis of symmetry, crease visibility and germ location. We were also able to determine the size of the wrinkled and smooth areas on a grain's surface, which allowed automatic grain classification and kernel quality assessment. The proposed algorithm was tested using barley grain images, and validated by comparison with the evaluation results of a professional assessor. The validation of the algorithm confirmed that it is efficient and robust allowing accurate description of over 93% of kernel samples in comparison with the expert.