Gıda incelemesi için imge işleme yöntemleri

With the advances in computer technology, signal processing techniques are widely applied to many food safety applications. In this thesis, new methods are developed to solve two food safety problems using image processing techniques. First problem is the detection of fungal infection on popcorn kernel images. This is a damage called blue-eye caused by a fungus. A cepstrum based feature extraction method is applied to the kernel images for classification purposes. The results of this technique are compared with the results of a covariance based feature extraction method, and previous solutions to the problem. The tests are made on two different databases; reflectance and transmittance mode image databases, in which the method of the image acquisition differs. Support Vector Machine (SVM) is used for image feature classification. It is experimentally observed that an overall success rate of 96% is possible with the covariance matrix based feature extraction method over transmittance database and 94% is achieved for the reflectance database.The second food inspection problem is the detection of acrylamide on cookies that is generated by cooking at high temperatures. Acrylamide is a neurotoxin and there have been various studies on detection of acrylamide during the baking process. Some of these detection routines include the correlation between the acrylamide level and the color values of the image of the cookies, resulting easier detection of acrylamide without the need of complex, expensive and time consuming chemical tests. Studies on the subject are tested on still images of the cookies, which are obtained after the cookies are removed from the oven. An active contour method is developed, that makes it possible to detect the cookies inside the oven or possibly on a moving tray, from the video captured from a regular camera. For this purpose, active contour method is modified so that elliptical shapes are detected in a more efficient manner.