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    Application of image processing with multilevel thresholding for mould detection on blue cheese cut surface
    (2021) Ivanov, I.; Karparov, V.; Kutryanska, M.
    The techniques used by computer vision are based on signals which carry information about objects and their processing using wide range of methods. There are variety of types of signals according to their frequency or wave length. The visual range of spectrum is defined for wave with length between 380 nm and 750 nm. The waves with such length are registered by humans’ receptors and they form sensitivity for colors. The human accepts the biggest portion of information about environment through its vision. Because of this there are developed a lot of methods for storing and processing the visual information. In the last years digital information processing and especially computer based such, is widely used in every area of human activity. By this way the computer vision and especially image processing became to be very important for human life. As a part of quality of human’s life, the quality of food is also controlled by methods of computer vision. The subject of this research is one of popular dairy products – blue cheese. The characteristics taste and smell of this cheese are formed thanks to growth of mould Penicillium roqueforti. The growth of this moulds is detected by images processing with multilevel thresholding based on Otsu’s method. Samples of eight trademarks of blue cheese are used for experiments. The results show that multilevel thresholding with two levels is appropriate for mould detection but multilevel thresholding with four levels support detection of areas with high concentration of moulds Penicillium roqueforti and areas with weak growth of moulds. Based on these results, in the future a software which would be helpful in reaching a high-quality control in the dairy industry could be developed.