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Öğe Combining Different Image Parts of Instruments with Image Mosaicing(IEEE, 2017) Ozturk, Saban; Ozkaya, Umut; Akdemir, Bayram; Seyfi, Levent A.; Kulaksiz, AfsinIn this study, the different image parts belonging to one instrument are combined to obtain a high resolution and full-bodied image as a whole. Images of different sizes that represent different regions of the same instrument are properly combined to obtain a full representation of the instrument. First, properties of images are obtained by using the histogram of gradient (HOG) features. Then, features similar to those obtained features are searched in other images. For this, the obtained properties are applied using convolution in all image. The regions having the highest convolution value are selected as junction regions. There are two problems in the image combining process. These are: the dimensions of the instrument parts in the images may be different, and the location where these parts are located may not overlap. An approach for automatic image sliding and automatic scale synchronization has been proposed for these problems. Finally, the merged pixels of the resulting image are softened. So, the contrast differences between the images due to the light is minimized. The proposed method was tested using different instruments in experiments. Successful results have been achieved for all compelling test images.Öğe Comparison of Edge Detection Algorithms for Texture Analysis on Glass Production(ELSEVIER SCIENCE BV, 2015) Ozturk, Saban; Akdemir, BayramThe use of technological innovations in production will increase the number of product and quality. With proposed method in this paper, it is aimed to improve the production process of glass which used almost every field. In this study, some of the popular edge detection algorithms (Roberts, Prewitt, Sobel, LoG and Canny) are used for the texture analysis process. It is aimed to determine glass surface defect with the applied of mentioned edge detection operators to same image. The results obtained from application are compared with the reference image and texture analysis performance of edge detection algorithms are evaluated. In this study the used material is glass and it is aimed to determine the glass surface defect such as scratch, crack and bubble with the use of edge detection operators. Glass is a difficult material to examine with cameras because glass has reflection and the transparency features. So, some improvement are applied in the image before edge detection algorithms are applied. Performed controlled experiments showed that LoG edge detection algorithm is better than other edge detection algorithms in determining texture analysis. (C) 2015 The Authors. Published by Elsevier Ltd.Öğe Comparison of HOG, MSER, SIFT, FAST, LBP and CANNY features for cell detection in histopathological images(BIOAXIS DNA RESEARCH CENTRE PRIVATE LIMITED, 2018) Ozturk, Saban; Akdemir, BayramCell segmentation and counting has a very important role in diagnosing diseases and in the treatment process. But the complexity of the histopathological images and the differences in cell groups make this process very difficult, even for an expert. In order to facilitate this process, analysis of histopathological images is performed by using computer vision methods. This paper presents the use of different feature extraction methods for cell detection in histopathological images and the comparison of the results of these algorithms. For this reason, HOG, MSER, SIFT, FAST, LBP and CANNY feature extraction algorithms are used. The aim of the study is to determine cells using different feature extraction methods and to determine which of these feature extraction algorithms will be more successful. Firstly, image pre-processing has been applied to clear the noises in the histopathological images. Then, feature extraction algorithms are applied to image, respectively. Finally, the successes of different feature extraction algorithms have been compared.Öğe CONTROL OF A REAL-TIME HUMANOID ROBOT BASED ON GESTURE DETECTION(ST JOHN PATRICK PUBL, 2017) Ozturk, Saban; Akdemir, Bayram[Abstract not Available]Öğe Convolution Kernel Size Effect on Convolutional Neural Network in Histopathological Image Processing Applications(IEEE, 2018) Ozturk, Saban; Ozkaya, Umut; Akdemir, Bayram; Seyfi, LeventIn this study, the change in the classification success of the convolutional neural network (CNN) is investigated when the dimensions of the convolution window are altered. For this purpose, four different linear convolution neural network architectures are constructed. The first architecture includes 4 convolution layers with 3x3 convolution window dimensions. The second architecture includes 4 convolution layers with 5x5 convolution window dimensions. The third architecture includes 4 convolution layers with 7x7 convolution window dimensions. The fourth architecture includes 4 convolution layers with 9x9 convolution window dimensions. A dataset consisting of histopathological image patches is used to test the CNN architects that are created. 2000 training images and 250 validation images on dataset are applied to all architectures with the same order, in order to fair assessment. In conclusion, the effect of convolution dimensions on classification of histopathological images by deep learning methods is determined. The test results of four different linear convolutional neural network architectures are evaluated using sensitivity, specificity and accuracy parameters.Öğe Effective histopathological image area reduction method for real-time applications(IS&T & SPIE, 2018) Ozturk, Saban; Akdemir, BayramHistopathologic images are time consuming for both specialist and machine learning methods with their complex structure and huge dimensions. In these cases, delays in the diagnosis of disease occur, as well as the treatment of fewer patients. When the histopathological images are examined at low resolution for shortening the examination time, it is almost impossible to identify the cancerous regions. When examining high-resolution images, it takes a long time to inspect because the image is divided into patches. Despite the fact that fairly fast machine learning methods are offered for the shortening of the analysis period, the number of patches to be examined has a negative effect on the decision time. For this reason, the area under examination needs to be reduced. For this, first of all, the destruction of cell-free areas and then the destruction of areas containing noncancerous cells must be eliminated. An effective and fast method of area reduction is presented for faster analysis and real-time use of histopathological images by machine learning algorithms. A two-step approach is used in the proposed method. In the first step, 3 x 3 texture properties of images are obtained and discrete wavelet transform is applied. Then, the image is cleaned with simple morphological processes. In the second step, the cleaned image is subjected to a discrete wavelet transform. Thus, the changes in cell-containing regions are captured, and regions that may be dangerous are identified. The proposed method reduced the areas to be examined by 98.5% to 99.5% with 95.33% accuracy. (C) 2018 SPIE and IS&TÖğe Fuzzy logic-based segmentation of manufacturing defects on reflective surfaces(SPRINGER LONDON LTD, 2018) Ozturk, Saban; Akdemir, BayramAutomatic defect detection on reflective surfaces is a compelling process. In particular, detection of tiny defects is almost impossible for human eye or simple machine vision methods. Therefore, the need for fast and sensitive machine vision methods has gained importance. In this study, an effective defect detection method is presented for reflective surfaces such as glass, tile, and steel. Defects on the surface of the product are determined automatically without the need for human intervention. The proposed system involves illumination unit, digital camera, and defect detection algorithm. Firstly, color image is taken by digital camera. Then, properties of taken image are selected. At this stage, ambient condition of lighting devices is very important. Reflections are minimized thanks to the true lighting. Selected properties are: red, green, and blue values, and luminance value. These properties are applied to fuzzy inputs. Information from the inputs is evaluated according to determined rules. Finally, each pixel is classified as black or white. Thirty-two glass pieces are tested using the proposed system. The proposed method was compared with commonly used methods. The success rate of the proposed algorithm is 83.5% and is higher than that of other algorithms .Öğe Novel BiasFeed Cellular Neural Network Model for Glass Defect Inspection(IEEE, 2016) Ozturk, Saban; Akdemir, BayramIn this study, an effective segmentation method is presented for defect detection on the glass surface. Defect detection on the glass surface is compelling and strenuous job for human eyes. Transparency and reflection properties of the glass surface reduce success of image processing algorithms using detection of the factors that unwanted and affecting quality of products such as crack, scratch, bubble. Traditional methods have limited success and long processing time in this process. Therefore, fast and effective method has been proposed. In the proposed method (BiasFeed CNN), bias input which is single number value traditional CNN algorithm is converted bias template. Bias template is used to balance the brightness level of the image. Input image and bias template convolution is applied bias input. Through the contribution from bias input, background reflections and negative effects arising from transparency are decreased. The developed method is fast as traditional CNN, because it does not cause significant changes in the structure of traditional CNN. 35 pieces of glass was tested using the algorithm. Damages in the glass surface and location of these damages were determined. Accuracy rates of inspected images are; sensivity % 91, specificity % 99, accuracy % 98. BiasFeed CNN algorithm was tested on various images and it is more successful than traditional CNN algorithm.Öğe Phase classification of mitotic events using selective dictionary learning for stem cell populations(PERGAMON-ELSEVIER SCIENCE LTD, 2018) Ozturk, Saban; Akdemir, BayramNowadays, thanks to the use of advanced technological tools, stem cell studies which play an important role in regenerative medicine and cancer studies have increased considerably. In this study, selective dictionary learning method is presented for detecting mitotic event phases in stem cells using phase contrast time-lapse microscopy images. In the proposed method, three phases are defined for representation of mitotic events. Creating a dictionary that represents these phases with a single feature space restricts the success. For this reason, three dictionaries with different features are created. Although the multiplication of image alpha values with all generated dictionaries is quite suitable for determining the lowest error value, this process is time consuming. For this reason, a selective dictionary approach based on the automatic selection of the best values with a cooperation between the dictionaries has been proposed. In this way, the high success rate is maintained and the processing time is significantly reduced. The proposed method gives better results than other state-of-art studies in terms of computational efficiency and accuracy in experiments with C2C12 and BAEC datasets. (C) 2018 Elsevier Ltd. All rights reserved.Öğe Real-time product quality control system using optimized Gabor filter bank(SPRINGER LONDON LTD, 2018) Ozturk, Saban; Akdemir, BayramMachine vision systems provide significant advantages when compared to conventional methods. Inspired from this idea, a visual inspected system that is independent of dust and dirt is presented for glass production systems. The method consists of a camera, a conveyor system, and an image segmentation method. Architecture of proposed system is as follows: a specific area of the conveyor is isolated from the outside. When glass enters this area, defect inspection process begins. In the inspection process, damaged and undamaged regions on glass surface are segmented. Gabor filter is very effective to detect orientation and thickness of these defects. But, Gabor filter bank should be created using appropriate Gabor coefficients for real-time applications. Otherwise, the processing time will be too long or fail results will be obtained. For this purpose, a new Gabor filter bank is created using gray wolf optimizer. In the hardware section, light beams are injected into the glass and the movements of these beams are observed to increase the perceptibility of the damage. Beam distribution is homogenous in the undamaged regions, but homogeneity is disturbed in defected areas. To avoid irregular glare on the glass surface, external lights are blocked and an artificial light source is used. Artificial light beams are injected into perpendicularly in the glass. So, homogeneous illumination in the glass can be occurred. Finally, optimized Gabor filter bank is applied to glass images. Proposed system detects all defects on the glass surface in the experiments. Size of the smallest defect is 0.4 mm. Defect detection performance of proposed system is nearly 100%. If it is evaluated in terms of shape and size, accuracy rate is 98.1%.Öğe Weighting and Classification of Image Features using Optimization Algorithms(IEEE, 2018) Ozturk, Saban; Ozkaya, Umut; Akdemir, Bayram; Seyfi, LeventIn this study, importance ratios of features extracted from images using feature extraction algorithms are examined. A significance coefficient is determined for each feature parameter. The number of features is reduced according to the weight of the importance calculated for each feature. The classification success is examined for each case. Firstly, six feature extraction algorithms are used for this purpose. The classification success of all these feature extraction algorithms has been examined separately. Then, all properties are combined to form a single property matrix. The obtained property matrix is reduced by using principal component analysis and relieff methods. New feature matrices provide increased classification performance. However, it is inefficient to classify a high number of properties in real-time applications. To overcome this problem, the effect of classifying each parameter in the property matrix is examined and the insignificant properties are discarded. The proposed method is tested using histopathological images. Histopathological images are divided into 4 separate classes. The proposed method reduces the raw feature matrix by 50% with 97.2% classification success.
