Yazar "Baykan N.A." seçeneğine göre listele

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  • Küçük Resim Yok
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    A mineral classification system with multiple artificial neural network using k-fold cross validation
    (2011) Baykan N.A.; Yilmaz N.
    The aim of this study is to show the artificial neural network (ANN) on classification of mineral based on color values of pixels. Twenty two images were taken from the thin sections using a digital camera mounted on the microscope and transmitted to a computer. Images, under both plane-polarized and cross-polarized light, contain maximum intensity. To select training and test data, 5-fold-cross validation method was involved and multi layer perceptron neural network (MLPNN) with one hidden layer was employed for classification. The classification of mineral using ANN proved %93.86 accuracy for 400 data. In second study, for each of the 5 different mineral considered, 5 different network models were implemented. Size of data set was same with previous data. Each network model was differed from each other. Also 5-fold-cross validation method was involved to select data and MLPNN with one hidden layer was used. The classification accuracy of mineral using different ANN is %90.67; %96.16; %93.91; %92; %97.62 for quartz, muscovite, biotite, chlorite and opaque respectively. © Association for Scientific Research.
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    A satellite image classification approach by using one dimensional discriminant analysis
    (International Society for Photogrammetry and Remote Sensing, 2018) Saglam A.; Baykan N.A.
    The classification problem in the image processing field is an important challenge, so that in the process image pixels are separated into previously determined classes according to their features. This process provides a meaningful knowledge about an area thanks to the satellite images. Satellite images are digital images obtained from a satellite vehicle by the way scanning the interest areas with some specified sensors. These sensors provide the specific radiometric and spatial information about the surface of the object. This information allows the researchers to obtain reliable classification results to be used to solve some real life problems such as object extraction, mapping, recognition, navigation and disaster management. Linear Discriminant Analysis (LDA) is a supervised method that reduces the dimensions of data in respect to the maximum discrimination of the elements of the data. This method also transfers the data to a new coordinate space in which the discriminant features of the classes are highest using the objection data provided manually. In this work, we consider the classes as if the satellite images have two classes; one is foreground and the other is background. The true classes such as roofs, roads, buildings, spaces and trees are treated sequentially as the foreground. The area outside the foreground class is treated as the background. The one dimensional reduced feature values of pixels, such that each value is reduced according to the binary classification of each class, are considered as membership values to the classes. In this way, each pixel has membership values for each of the classes. Finally, the pixels are classified according to the membership values. We used the ISPRS WG III/4 2D Semantic Labeling Benchmark (Vaihingen) images includes the ground truths and give the accuracy result values for each class. © Authors 2018. CC BY 4.0 License.
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    User interactive object extraction with sequential image segmentation [Sirali Görüntü Bölütleme ile Kullanici Etkilesimli Nesne Çikarma]
    (Institute of Electrical and Electronics Engineers Inc., 2018) Saglam A.; Baykan N.A.
    In this study, a graph-based image segmentation algorithm which was developed in recent years and achieves a significant success in terms of the performance of both the accuracy and speed is used as an intermediate process for a user interactive object extraction method. In the object extraction method developed, the related image is subdivided into segments and, then, these segments are merged according to their label values by using the area determined by the user at first. The image segmentation algorithm used in the scope of this work fulfills a sequential segmentation process on the one dimensional edge array of Prim's minimum spanning tree representation. The algorithm does the segmentation by cutting the specified edges on the tree. According to the method developed, these cut edges are kept and some of them are added to the tree again in the merging stage; so that, the segments at the ends of the edge added are merged. Owing to this process, the process of finding the least weighted edge between the two segments to be merged, which needs to be performed before the merging stage according to the previous studies, is not needed. The method developed here is compared with some methods in the literature on a dataset consist of real life images, and it seems that the method shows a significant superiority to the other methods. © 2018 IEEE.