Yazar "Özok, Yasa Ekşioğlu" seçeneğine göre listele

Listeleniyor 1 - 6 / 6
  • [ X ]
    Öğe
    A new and effective method for human retina optic disc segmentation with fuzzy clustering method based on active contour model
    (Springer Heidelberg, 2020) Abdullah, Ahmad S.; Rahebi, Javad; Özok, Yasa Ekşioğlu; Aljanabi, Mohanad
    In this paper, a new approach is proposed for localization and segmentation of the optic disc in human retina images. This new approach can find the boundary of the optic disc by an initial fuzzy clustering means algorithm. The proposed approach uses active contour model evolution based on a fuzzy clustering algorithm. The robustness of the proposed approach was evaluated with retinal imaging medical databases, such as DRIVE, STARE, DIARETDB1, and DRIONS. These bases contained images affected by different abnormalities, for example, diabetes, retinitis pigmentosa, and age-related macular degeneration AMD. A success detection rate with 100% accuracy was achieved for the DRIVE, DIRATEDB1, and DRIONS-DB databases, and 97.53% for the STARE database. For the optic disc segmentation, the method achieved an average accuracy and overlap in the range of 97.01-99.46% and 78.35-84.56% in these four databases. The result was compared with various methods in the literature, and it was concluded that the proposed method is more accurate than the other existing methods.
  • [ X ]
    Öğe
    A novel method for retinal optic disc detection using bat meta-heuristic algorithm
    (Springer Heidelberg, 2018) Abdullah, Ahmad S.; Özok, Yasa Ekşioğlu; Rahebi, Javad
    Normally, the optic disc detection of retinal images is useful during the treatment of glaucoma and diabetic retinopathy. In this paper, the novel preprocessing of a retinal image with a bat algorithm (BA) optimization is proposed to detect the optic disc of the retinal image. As the optic disk is a bright area and the vessels that emerge from it are dark, these facts lead to the selected segments being regions with a great diversity of intensity, which does not usually happen in pathological regions. First, in the preprocessing stage, the image is fully converted into a gray image using a gray scale conversion, and then morphological operations are implemented in order to remove dark elements such as blood vessels, from the images. In the next stage, a bat algorithm (BA) is used to find the optimum threshold value for the optic disc location. In order to improve the accuracy and to obtain the best result for the segmented optic disc, the ellipse fitting approach was used in the last stage to enhance and smooth the segmented optic disc boundary region. The ellipse fitting is carried out using the least square distance approach. The efficiency of the proposed method was tested on six publicly available datasets, MESSIDOR, DRIVE, DIARETDB1, DIARETDB0, STARE, and DRIONS-DB. The optic disc segmentation average overlaps and accuracy was in the range of 78.5-88.2% and 96.6-99.91% in these six databases. The optic disk of the retinal images was segmented in less than 2.1s per image. The use of the proposed method improved the optic disc segmentation results for healthy and pathological retinal images in a low computation time.
  • [ X ]
    Öğe
    A robust hybrid control model implementation for autonomous vehicles
    (Institute of Electrical and Electronics Engineers Inc., 2024) Al-Jumaili, Mustafa Hamid; Özok, Yasa Ekşioğlu; Ibrahim, Abdullahi Abdu; Bayat, Oğuz
    This work presents a robust control strategy for controlling autonomous vehicles under various conditions. This approach makes use of two controllers to guarantee excellent performance and a few faults when the car is traveling. Model Predictive and Stanley based controller (MPS) is the name of the new control system. This combines the functionality of a Stanley controller with a model predictive controller. The suggested approach tries to address these issues and provides a high-performance control system. Utilizing the finest aspects of both controllers and attempting to improve the other, this hybrid approach to integrating two well-known controllers provides advantages. The MPS is put to the test on straight and curvy roads in a variety of scenarios for both path-following and vehicle control. This controller has demonstrated excellent performance and adaptability to handle various autonomous driving conditions. When the findings are compared to earlier controller kinds, the suggested system performs better.
  • [ X ]
    Öğe
    New control model for autonomous vehicles using integration of Model Predictive and Stanley based controllers
    (2024) Al-Jumaili, Mustafa Hamid; Özok, Yasa Ekşioğlu
    In this paper, a robust control method is introduced for autonomous vehicle control in different scenarios. Dual controllers have been used in this method to ensure high performance and low errors during the vehicle's trip. The new control system is called Model Predictive and Stanley based controller (MPS), which is an integration of a model predictive controller and a Stanley controller. Each of these two controllers has its drawbacks and weaknesses. The proposed method tries to overcome these points and come up with a high-performance control system. This hybrid way of combining two of the famous controllers has the benefit of using the best part of each one and trying to enhance the other part. The MPS is tested for both path-following and vehicle control in different scenarios and on both straight and curved roads. This controller has shown high performance and flexibility to deal with different scenarios of autonomous driving. The results are compared to previous types of controllers, and the proposed system outperformed these types.
  • Küçük Resim
    Öğe
    Skin lesion segmentation method for dermoscopy images using artificial bee colony algorithm
    (Mdpi, 2018) Aljanabi, Mohanad; Özok, Yasa Ekşioğlu; Rahebi, Javad; Abdullah, Ahmad S.
    The occurrence rates of melanoma are rising rapidly, which are resulting in higher death rates. However, if the melanoma is diagnosed in Phase I, the survival rates increase. The segmentation of the melanoma is one of the largest tasks to undertake and achieve when considering both beneath and over the segmentation. In this work, a new approach based on the artificial bee colony (ABC) algorithm is proposed for the detection of melanoma from digital images. This method is simple, fast, flexible, and requires fewer parameters compared with other algorithms. The proposed approach is applied on the PH2, ISBI 2016 challenge, the ISBI 2017 challenge, and Dermis datasets. These bases contained images are affected by different abnormalities. The formation of the databases consists of images collected from different sources; they are bases with different types of resolution, lighting, etc., so in the first step, the noise was removed from the images by using morphological filtering. In the next step, the ABC algorithm is used to find the optimum threshold value for the melanoma detection. The proposed approach achieved good results in the conditions of high specificity. The experimental results suggest that the proposed method accomplished higher performance compared to the ground truth images supported by a Dermatologist. For the melanoma detection, the method achieved an average accuracy and Jaccard's coefficient in the range of 95.24-97.61%, and 83.56-85.25% in these four databases. To show the robustness of this work, the results were compared to existing methods in the literature for melanoma detection. High values for estimation performance confirmed that the proposed melanoma detection is better than other algorithms, which demonstrates the highly differential power of the newly introduced features.
  • Küçük Resim
    Öğe
    Vehicle position estimation and vehicle classification using deep convolutional neural networks
    (Altınbaş Üniversitesi, 2021) Kabeayla, Bashaer Isam Hasan; Özok, Yasa Ekşioğlu
    The aim of this paper is to classify the vehicles and estimate the position with license plate localization using deep convolutional Neural Network (DCNN). Vehicle pose estimation with license plate localization serves as one of the most widely-used real-world applications in fields like toll control, traffic scene analysis, and suspected vehicle tracking. We proposed a one-stage anchor-free vehicle classifier for simultaneously localizing the region of license plates and vehicles’ poses. The classifier, rather than bounding rectangles, gives bounding quadrilaterals, which gives a more precise indication for vehicle pose estimation with license plates localization. For single scale input, we reached mean Precision Accuracy mAP/mAP50 of 35.4/82.3 on the LISA benchmark dataset, already outperformed the existing commercial systems OpenALPR and Sighthound. For multi-scale input, we reached the best mAP/mAP50 of 40.8/90.1. For the vehicle pose (front-rear), classification accuracy reached 98.8%, average IoU reached 71.3%, giving a promising result as an end-to-end vehicle position estimation and license plate localization with contextual information. The work has performed in python programming language with several libraries of deep learning were being used for this purpose. Our DCNN model training started from an initial weight which we had already trained for about 110000 iterations in the model without classification head, so the total training iterations will be around 780000 including the transfer learning part in DCNN. Transfer learning made the DCNN model start at a smart point and made it easier to optimize all of the functional heads simultaneously.