Yazar "Kayci, Lokman" seçeneğine göre listele

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    A Computer Vision System for the Automatic Identification of Butterfly Species via GaborFilter-Based Texture Features and Extreme Learning Machine: GF+ELM
    (Assoc Information Communication Technology Education & Science, 2013) Kaya, Yilmaz; Kayci, Lokman; Tekin, Ramazan
    Butterflies are classified first according to their outer morphological qualities. It is required to analyze their genital characters when classification according to their outer morphological qualities is not possible. The genital characters of butterflies can be obtained using various chemical substances and methods; however, these processes can only be carried out with some certain expenses. Furthermore, the preparation of genital slides is time-consuming since it requires specific processes. In this study, a new method based on the extreme learning machine (ELM) and Gabor filters (GFs), which is an image processing technique, was used for the identification of butterfly species as an alternative to conventional diagnostic methods. GFs have been recognized as a very useful tool in texture analysis, due to their optimal localization properties in both the spatial and frequency domains, and have been found to have widespread use in computer vision applications. To obtain the appropriate features from butterfly images in the spatial domain, 20 filters were designed for the various angles and frequencies (5 frequencies and 4 orientations). The diagnosing of butterflies was performed through ELM, with texture features based on GFs. The classification process was performed with a 75%-25% training-test set for different activation functions and the recognition performance value was obtained as 97.00%. In addition, the recognition success rates with ELM were compared to other machine learning methods and it was seen that ELM has a more significant success rate in butterfly identification than other methods. As a result, the proposed method is a suitable machine vision system for detecting butterfly species
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    A vision system for automatic identification of butterfly species using a grey-level co-occurrence matrix and multinomial logistic regression
    (Taylor & Francis Ltd, 2014) Kayci, Lokman; Kaya, Yilmaz
    We present an application of image-processing techniques for identifying butterfly species as an alternative to conventional diagnostic methods. Grey-level co-occurrence matrix (GLCM) matrices are utilised to evaluate the surface texture features of butterflies' wings, which is an important character for identification. Eleven textural features were extracted from butterfly images and characterised by the texture average in four directions (0 degrees, 45 degrees, 90 degrees and 135 degrees) and distances (d = 1, 2, 3 and 4 pixels). We used 190 butterfly images belonging to 19 different species of the family Pieridae. The identification accuracy of the GLCM+MLR was 96.3% with tenfold cross validation. The methodology presented here classified the butterflies effectively. These findings suggest that the proposed MLR algorithm and GLCM texture features technique are feasible for the identification and classification of butterfly species.
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    An automatic identification method for the comparison of plant and honey pollen based on GLCM texture features and artificial neural network
    (Taylor & Francis As, 2013) Kaya, Yilmaz; Erez, Mehmet Emre; Karabacak, Osman; Kayci, Lokman; Fidan, Mehmet
    Pollen grains vary in colour and shape and can be detected in honey used as a way of identifying nectar sources. Accurate differentiation between pollen grains record is hampered by the combination of poor taxonomic resolution in pollen identification and the high species diversity of many families. Pollen identification determines the origin and the quality of the honey product, but this indefiniteness is also a big challenge for the beekeepers. This study aimed to develop effective, accurate, rapid and non-destructive analysis methods for pollen classification in honey. Ten different pollen grains of plant species were used for the estimation. GLCM (grey level co-occurrence matrix) texture features and ANN (artificial neural network) were used for the identification of pollen grains in honey by the reference of plant species pollen. GLCM has been calculated in four different angles and offsets for the pollen of the plant and the honey samples. Each angle and offset pair includes five features. At the final step, features were classified using the ANN method; the success of estimation with ANN was 88.00%. These findings suggest that the texture parameters can be useful in identification of the pollen types in honey products.
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    Application of artificial neural network for automatic detection of butterfly species using color and texture features
    (Springer, 2014) Kaya, Yilmaz; Kayci, Lokman
    Butterflies can be classified by their outer morphological qualities, genital characteristics that can be obtained using various chemical substances and methods which are carried out manually by preparing genital slides through some certain processes or molecular techniques which is a very expensive method. In this study, a new method which is based on artificial neural networks (ANN) and an image processing technique was used for identification of butterfly species as an alternative to conventional diagnostic methods. Five texture and three color features obtained from 140 butterfly images were used for identification of species. Texture features were obtained by using the average of gray level co-occurrence matrix (GLCM) with different angles and distances. The accuracy of the purposed butterfly classification method has reached 92.85 %. These findings suggested that the texture and color features can be useful for identification of butterfly species.
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    Automatic identification of butterfly species based on local binary patterns and artificial neural network
    (Elsevier, 2015) Kaya, Yilmaz; Kayci, Lokman; Uyar, Murat
    Butterflies are classified firstly according to their outer morphological qualities. It is required to analyze genital characters of them when classification according to outer morphological qualities is not possible. Genital characteristics of a butterfly can be determined by using various chemical substances and methods. Currently, these processes are carried out manually by preparing genital slides of the collected butterfly through some certain processes. For some groups of butterflies molecular techniques should be applied for identification which is expensive to use. In this study, a computer vision method is proposed for automatically identifying butterfly species as an alternative to conventional identification methods. The method is based on local binary pattern (LBP) and artificial neural network (ANN). A total of 50 butterfly images of five species were used for evaluating the effectiveness of the proposed method. Experimental results demonstrated that the proposed method has achieved well recognition in terms of accuracy rates for butterfly species identification. (C) 2014 Elsevier B.V. All rights reserved.
  • Küçük Resim
    Öğe
    Characterization of Multifloral Honeys of Pervari Region with Different Properties
    (2015) Erez, Mehmet Emre; Karabacak, Osman; Kayci, Lokman; Fidan, Mehmet; Kaya, Yılmaz
    The quality of honey from Pervari region was almost known by all over the country in Turkey. This study was undertaken to determine (i) physico-chemical parameters, (ii) antimicrobial analysis and (iii) pollen estimation method with expert computer system obtained from three different sites of Pervari region (Siirt/Turkey). For physico-chemical parameters; moisture, free acidity, diastase activity, hydroxyl methyl furfural (HMF), invert sugar, ash, commercial glucose and proline analysis were examined. For anti-microbial analysis disc dilution method were studied on six different bacteria species. For pollen analysis; different and new expert computer system was used for comparison of pollen of plants and honey samples. The aim of the study was to evaluate the properties of multi floral honey determined from three different locations in the same region and the way to understand to which plants were visited by the bees with comparing of pollen grains of flowers and honey by using the expert computer system. Honey samples of Pervari region were of acceptable quality based on recommended criteria of Turkish Food Codex and International Honey Commission.
  • Küçük Resim
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    Chazaria incarnata (Freyer, 1838) Türünün Biyolojisi ve Yayılışı Hakkında Notlar (Lepidoptera, Noctuidae)
    (2010) Kayci, Lokman; Fidan, Mehmet
    Notes on the Biology of Chazaria incarnata (Freyer, 1838) (Lepidoptera, Noctuidae). Cesa News 59: 55-59, 6 figs.[in Turkish] In this paper, new record of the larval food-plant, Silene compacta (Caryophyllaceae), and the provincial distribution of Chazaria incarnata (Freyer, 1838) are given.
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    Classification of butterfly images with multi-scale local binary patterns
    (IEEE, 2013) Kaya, Yilmaz; Kayci, Lokman; Sezgin, Necmettin
    Butterflies are classified first according to their outer morphological qualities. It is required to analyze their genital characters when classification according to their outer morphological qualities is not possible. The genital characters of butterflies can be obtained using various chemical substances and methods; however, these processes can only be carried out with some certain expenses. Furthermore, the preparation of genital slides is time-consuming since it requires specific processes. In this study, a computer vision system based on local binary patterns was proposed to alternative conventional diagnostic methods for the diagnosis of butterfly species. 140 images of 14 butterfly species belonging to the family of Styridae are used. The butterfly diagnostic process was carried out by using LBPP, R attributes as inputs for the ANN, SVM and LR classification methods. 100% classification was achieved with macro and micro patterns obtained with LBPP, R for different values of parameter R. As a result, it was seen butterfly wings have different types of micro and macro properties, and LBP has a major advantage in identification of butterfly species.
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    Evaluation of texture features for automatic detecting butterfly species using extreme learning machine
    (Taylor & Francis Ltd, 2014) Kaya, Yilmaz; Kayci, Lokman; Tekin, Ramazan; Ertugrul, O. Faruk
    In this study, we present an application of extreme learning machine (ELM) and image processing techniques for identifying butterfly species as an alternative to conventional diagnostic methods. This paper evaluates the capability of butterfly species classification by using texture features of butterfly images. Two texture descriptors such as grey-level co-occurrence matrix (GLCM) and local binary patterns (LBP) were used for comparison purpose. ELM is employed for classification in butterfly-feature space. A total of 190 butterfly images belonging to 19 different species of Pieridae family were used. The identification accuracy of the proposed method was 98.25% and 96.45% with GLCM and LBP butterfly-feature spaces, respectively. The methodology presented herein effectively detected and classified these butterflies. These findings suggested that the proposed GLCM, LBP texture features extraction techniques and ELM algorithm are feasible and excellent in identification and classification of butterfly species.