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Öğe Three-dimensional modeling in medical image processing by using fractal geometry(Journal of Computers, 2017-09) Çalışkan, Abidin; Çevik, UlusMedical images are visualized by computer and processed to obtain larger, more organized, and three-dimensional (3D) images. Thus, significant images are provided. The processed data facilitate diagnosis and treatment in the medical fields. The 3D surface models of related areas are formed by using volumetric data obtained by employing medical imaging methods such as Magnetic Resonance (MR) and Computer Tomography (CT). The purpose of this study is to obtain 3D images from the two-dimensional CT slices. These slices are obtained from the existing medical imaging devices and transferred to the z space and a mesh structure is provided between them. In addition, we investigated 3D imaging techniques, visualization, basic data types, conversion into main graphical components, and imaging algorithms. At the phase of obtaining 3D images; the image processing methods such as surface and volume imaging techniques, smoothing, denoising, and segmentation were used. The complexity and efficiency properties of the imaging algorithms were investigated and image enhancement algorithms were utilized.Öğe Detection and recognition of Turkish license plate characters with image segmentation based correlation method(Batman Üniversitesi, 2018) Çalışkan, Abidin; Acar, Emrullah; Orun, ÖzgeÖğe Multi-feature extraction and multi-classification techniques with computer aided breast cancer detection(Batman Üniversitesi, 2018) Çalışkan, Abidin; Acar, Emrullah; Budak, CaferÖğe Overview of computer gaphics and algorithms(IEEE, 2015-05) Çalışkan, Abidin; Çevik, UlusComputer Graphics (CG) is the art of rendering, and visualizing images on the computer screens. In three-dimensional (3D) CG, a scene is first modeled geometrically, typically using triangles, and the computer is then used to calculate what the scene will look like from a specific view point at a particular instant. In CG, one of the major goals is to create photo-realistic images in real time. In recent years, Volume Visualization (VV) has attracted the attention of many researchers. VV techniques have been used to analyze and render 3D datasets, obtained from a variety of sources including medical scanners, and results of simulation of physical and synthetic phenomena, on the computer screen. Volume Graphics (VG) has proven itself as an independent graphics technology. A common purpose of VG is to achieve photo realistic rendering. To achieve this, reflections, shadows, refraction and perspective projections are all necessary elements since they occur naturally in the natural environment.Öğe Classification of soil surface wetness by histogram of oriented gradients and extreme learning machine models(Batman Üniversitesi, 2018) Çalışkan, Abidin; Acar, Emrullah; Budak, CaferÖğe Palmprint recognition based gabor wavelet transform using K-NN classification(European Journal of Technique (EJT), 2016-04-01) Çalışkan, AbidinPalmprint recognition system is regarded as the reliable and accurate biometric identification system available. Viewed in the palmprint recognition system of biometric approaches, compared to other models because it is a new handheld biometric feature recognition system has recently attracted the attention of researchers. In this study, palmprint recognition system based on Gabor wavelet transform has been developed. Firstly, image coordinate system is defined to facilitate image alignment for feature extraction. Then, region of interest is cropped from the palmprint images. With developed system feature extracted of region of interest and given of k-NN classifier.Öğe 3D imaging and visualization studies(Batman Üniversitesi, 2018) Çalışkan, Abidin; Karaman, Ömer Ali; Çelebi, Selahattin BarışÖğe Palmprint recognition system based on gabor wavelet transform with K-NN classifier model(INESEC, 2017) Çalışkan, Abidin; Ergen, Burhan; Acar, EmrullahPalmprint recognition system is regarded as reliable and accurate biometric identification system. The biometric approach palm recognition system has attracted the attention of researchers in recent times because of the presenting a new biometric feature compared to other models. In this work, gabor wavelet transform (GWT) based palmprint recognition system has been developed. Firstly, image coordinate system is determined in order to facilitate image alignment for feature extraction. Then, region of interest is cropped from the palmprint images. With the developed system, features are extracted from the region of interest and they are given to k-nearest neighbors (k-NN) classifier as input parameters. Finally, the highest success rate for GWT based systematic sampling was computed as 86.90% according to the non-request data selection and it was observed that the proposed recognition system provide successful results in classification of palmprint images. Moreover, a good identification of the feature vector is the main factor that affects performance. Thus, the performance can also be improved by finding more suitable feature vectors.Öğe Comparison of multiple biometric identification with a single biometric identification system(Batman Üniversitesi, 2018) Çalışkan, Abidin; Acar, Emrullah; Budak, CaferÖğe Fingerprint recognition system based on gray level co-occurrence matrix(INESEC, 2017) Çalışkan, AbidinThe biometric system provides an automatic identification of any person, depending on characteristic and feature/attribution of person. Fingerprint is, today, one of the biometric systems that have a wide range of use in many investigation areas. Fingerprint, especially used for authentication, is more reliable comparing to the other traditional methods which are used for access. In this study, a gray level co-occurrence matrix (GLCM) based fingerprint recognition system which provides successful results in tissue type imagining recognition has been implemented. The purpose of this study is to show the effectiveness of the GLCM in fingerprint recognition. By using GLCM which is a feature extracting method, fingerprint images are classified by multilayer perceptron (MLP) artificial neural network classification technique. Statistical methods were used to extraction the feature by obtaining the GLCM matrix for the gray level images. In the first step of system analysis, the system is trained by using GLCM attribute parameters and performance information is measured for different network topologies of the MLP classifier. After the classification stage, when the results are compared with the success rates of previously made fingerprint recognition systems, the success rate of 88.25% is considered as acceptable. As a result, it is considered that the results are reasonable when results are compared with other studies in the literature. Experimental results have also shown that the proposed method can improve the accuracy of existing methods.
