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    A Computer Vision System for Classification of Some Euphorbia (Euphorbiaceae) Seeds Based on Local Binary Patterns
    (IEEE, 2013) Kaya, Yilmaz; Karabacak, Osman; Caliskan, Abidin
    In this study, a computer vision system was proposed for the seed images classification. The classification process was performed using uniform local binary patterns obtained from digital seed images. In this study, 240 (120 training and 120 test) images of the seed were used. First, the average uniform histograms of each type of seed (seed type classes) was obtained for the training set. Then the uniform LBP histogram of each seed in the test set were produced and compared with histograms of classes by using nearest neighbor. The Euclidean distance, sum square error, histogram intersection and Chi-square statistics were used to calculate the distance between seed samples. 95.83%. of seed images has been diagnosed properly with the proposed. As a result, the surface shape of the seeds include important information patterns to determine the taxonomic relationships, it is is expected that the computer vision systems provide significant advantages to identify the type of seed.
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    Öğe
    Classification of Pollen Images with Structural Characteristics
    (IEEE, 2013) Erez, M. Emre; Kaya, Yilmaz; Caliskan, Abidin
    In this study, a computer vision system has been developed to separate the pollen grains of plants according to their taxonomic categories without the help of an expert person. Pollen grains have a complex three-dimensional structure however they can be distinguished from one to another with their specific features. In the research, for the classification of pollen images the local edge patterns (LEP) were used. The proposed system is consists of three stages. At first Stage, Sobel edge detection algorithm was applied to pollen images to obtained new images that have prominent structural features. At the second stage LEP features were obtained and at the last stage the classification process was performed by machine learning methods by LEP features. The 98.48% classification success were obtained by LEP features.