Yazar "Iscimen, Bilal" seçeneğine göre listele

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    Classification of Fish Species With Two Dorsal Fins Using Centroid-Contour Distance
    (Ieee, 2015) Iscimen, Bilal; Kutlu, Yakup; Uyan, Ali; Turan, Cemal
    Color, texture and shape are generally used features in order to recognise an object from an image. In this study centroid-contour distance method is used in order to classify fish species with two dorsal fins. Therefore, fish images with two dorsal fins were used from fish images database taken under specific conditions. Various image processing methods were applied on images in order to extract centroid-contour distances. These distances were used as features and Nearest Neighbour algorithm was used for classification. 15 species from 427 fish images were classified with 95% general accuracy achievement.
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    Image analysis methods on fish recognition
    (Ieee, 2014) Iscimen, Bilal; Kutlu, Yakup; Reyhaniye, Asil Nadir; Turan, Cemal
    The aim of study is creating a new database which contains fish species and classifing this fish species. A new fish database was created by using the fish photos in seas of Turkey. The new feature set are obtained by marking the biometric points on fish to identify family and species of fishes. The features were obtained by using the three different biometric measurement techniques (Euclidean network technique, quadratic network technique, triangulation technique). A classificatios system was created by using Naive Bayesian classifier. The obtained accuracy is 93.10% for 7 families on family classification and 75.71% for 15 species on species classification.
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    MULTI-STAGE FISH CLASSIFICATION SYSTEM USING MORPHOMETRY
    (Parlar Scientific Publications (P S P), 2017) Kutlu, Yakup; Iscimen, Bilal; Turan, Cemal
    The aim of this study is to create a multi-stage fish classification system with high accuracy rate. Classifications are based on biometric points of the fishes that consists of three main phases, data acquisition, feature extraction and classification. In the first phase, fish image database was collected, then features were extracted using morphometry and classified with three stage classifier model. Nearest Neighbor algorithm was used as classifier, and 25 fish species were classified with accuracy of about 99%.