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Yayın Raylı sistemlerde yüksek gerilim aksamının otomatik denetimi(IEEE, 2014-04-23) Ağdoğan, Didem; Babacan, Veysel Karani; Eskil, Mustafa TanerRaylı sistemlerde yolculugun sorunsuz tamamla-nabilmesi için sistem bütünlüğü kritik öneme sahiptir. Sistem bütünlüğü, lokomotif ve vagonlar haricinde katener (yüksek gerilim) hattı, pantograf ve raylara bağlıdır. Katener hattı ve pantograf, lokomotife elektrik iletimini sağlarken rayların seviyesi pantografın elektrik hattına düzenli temasına etki eder. Raylarda oluşabilecek çöküntüler katener hattı ile pantograf arasında ark (kıvılcım) oluşumuna neden olur. Katener hattının pantograf sınırları dışına çıkması, pantografta oluşabilecek çentikler ve ark oluşumu lokomotif üzerinden anlık izlenebilir. Bu çalışmada amacımız, bu üç ögeden kaynaklanabilecek hataları kameralı bir sistemle, gerçek zamanlı ve otomatik izleyerek tren yolculuğunun güvenli ve kesintisiz yapılmasına katkıda bulunmaktır.Yayın Numerical integration methods for simulation of mass-spring-damper systems(Springer-Verlag, 2012) Özgüz, Mete; Eskil, Mustafa TanerThe dynamics of a face are often implemented as a system of connected particles with various forces acting upon them. Animation of such a system requires the approximation of velocity and position of each particle through numerical integration. There are many numerical integrators that are commonly used in the literature. We conducted experiments to determine the suitability of numerical integration methods in approximating the particular dynamics of mass-spring-damper systems. Among Euler, semi-implicit Euler, Runge-Kutta and Leapfrog, we found that simulation with Leapfrog numerical integration characterizes a mass-spring-damper system best in terms of the energy loss of the overall system.Yayın Palmprint verification using SIFT majority voting(Springer-Verlag, 2012) Abeysundera, Hasith Pasindu; Eskil, Mustafa TanerIn this paper we illustrate the implementation of a robust, real-time biometric system for identity verification based on palmprint images. The palmprint images are preprocessed to align the major axes of hand shapes and to extract the palm region. We extract features using Scale Invariant Feature Transform (SIFT). Classification of individual SIFT features is done through KNN. The class of the hand image is decided by a majority based voting among its classified SIFT features. We demonstrate on the CASIA and PolyU datasets that the proposed system achieves authentication accuracy comparable to other state of the art algorithms.Yayın Extraction and selection of muscle based features for facial expression recognition(IEEE Computer Soc, 2014-12-04) Benli, Kristin Surpuhi; Eskil, Mustafa TanerIn this study we propose a new set of muscle activity based features for facial expression recognition. We extract muscular activities by observing the displacements of facial feature points in an expression video. The facial feature points are initialized on muscular regions of influence in the first frame of the video. These points are tracked through optical flow in sequential frames. Displacements of feature points on the image plane are used to estimate the 3D orientation of a head model and relative displacements of its vertices. We model the human skin as a linear system of equations. The estimated deformation of the wireframe model produces an over-determined system of equations that can be solved under the constraint of the facial anatomy to obtain muscle activation levels. We apply sequential forward feature selection to choose the most descriptive set of muscles for recognition of basic facial expressions.Yayın Convolutional neural network (CNN) algorithm based facial emotion recognition (FER) system for FER-2013 dataset(IEEE, 2022-11-18) Ezerceli, Özay; Eskil, Mustafa TanerFacial expression recognition (FER) is the key to understanding human emotions and feelings. It is an active area of research since human thoughts can be collected, processed, and used in customer satisfaction, politics, and medical domains. Automated FER systems had been developed and have been used to recognize humans’ emotions but it has been a quite challenging problem in machine learning due to the high intra-class variation. The first models were using known methods such as Support Vector Machines (SVM), Bayes classifier, Fuzzy Techniques, Feature Selection, Artificial Neural Networks (ANN) in their models but still, some limitations affect the accuracy critically such as subjectivity, occlusion, pose, low resolution, scale, illumination variation, etc. The ability of CNN boosts FER accuracy. Deep learning algorithms have emerged as the greatest way to produce the best results in FER in recent years. Various datasets were used to train, test, and validate the models. FER2013, CK+, JAFFE and FERG are some of the most popular datasets. To improve the accuracy of FER models, one dataset or a mix of datasets has been employed. Every dataset includes limitations and issues that have an impact on the model that is trained for it. As a solution to this problem, our state-of-the-art model based on deep learning architectures, particularly convolutional neural network architectures (CNN) with supportive techniques has been implemented. The proposed model achieved 93.7% accuracy with the combination of FER2013 and CK+ datasets for FER2013.Yayın İfade tanıma için yüz anatomisine dayalı öznitelikler(IEEE, 2014-04-23) Benli, Kristin Surpuhi; Eskil, Mustafa TanerBu çalışmada yüz ifadesi tanıma için kas kuvvetlerine dayalı yeni öznitelikler öneriyoruz. Yüz üzerinde seçtiğimiz noktaların video üzerindeki hareketlerini izleyerek kas kuvvetlerini çözüyoruz. Yüz noktaları, ilk video çerçevesi üzerinde, kas kuvvet alanları üzerinde ilklendirilir. Bu noktalar optik akış algoritması ile izlenir. Noktaların devinimleri yüzün 3 boyutlu yönelimi ve yüz ifadesine dayalı bağıl devinimleri kestirmek için kullanılır. İnsan yüzünü yaylarla, artık-belirtilmiş doğrusal bir denklem sistemi olarak modelliyoruz. Bu sistemi yüz anatomisi kısıtı altında, kas kuvvetleri için çözüyoruz. Ardışık ileri seçim yaparak, temel yüz ifadeleri için en betimleyici kas kümesini belirliyoruz.Yayın Subset selection for tuning of hyper-parameters in artificial neural networks(IEEE, 2017) Aki, K.K.Emre; Erkoç, Tuğba; Eskil, Mustafa TanerHyper-parameters of a machine learning architecture define its design. Tuning of hyper-parameters is costly and for large data sets outright impractical, whether it is performed manually or algorithmically. In this study we propose a Neocognitron based method for reducing the training set to a fraction, while keeping the dynamics and complexity of the domain. Our approach does not require processing of the entire training set, making it feasible for larger data sets. In our experiments we could successfully reduce the MNIST training data set to less than 2.5% (1,489 images) by processing less than 10% of the 60K images. We showed that the reduced data set can be used for tuning of number of hidden neurons in a multi-layer perceptron.Yayın Semi-automatic adaptation of high-polygon wireframe face models through inverse perspective projection(Springer-Verlag, 2012) Benli, Kristin Surpuhi; Ağdoğan, Didem; Özgüz, Mete; Eskil, Mustafa TanerPrecise registration of a generic 3D face model with a subject's face is a critical stage for model based analysis of facial expressions. In this study we propose a semi-automatic model fitting algorithm to fit a high-polygon wireframe model to a single image of a face. We manually mark important landmark points both on the wireframe model and the face image. We carry out an initial alignment by translating and scaling the wireframe model. We then translate the landmark vertices in the 3D wireframe model so that they coincide with inverse perspective projections of image landmark points. The vertices that are not manually labeled as landmark are translated with a weighted sum of vectorial displacement of k neighboring landmark vertices, inversely weighted by their 3D distances to the vertex under consideration. Our experiments indicate that we can fit a high-polygon model to the subject's face with modest computational complexity.Yayın Unreasonable effectiveness of last hidden layer activations for adversarial robustness(Institute of Electrical and Electronics Engineers Inc., 2022) Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa TanerIn standard Deep Neural Network (DNN) based classifiers, the general convention is to omit the activation function in the last (output) layer and directly apply the softmax function on the logits to get the probability scores of each class. In this type of architectures, the loss value of the classifier against any output class is directly proportional to the difference between the final probability score and the label value of the associated class. Standard White-box adversarial evasion attacks, whether targeted or untargeted, mainly try to exploit the gradient of the model loss function to craft adversarial samples and fool the model. In this study, we show both mathematically and experimentally that using some widely known activation functions in the output layer of the model with high temperature values has the effect of zeroing out the gradients for both targeted and untargeted attack cases, preventing attackers from exploiting the model's loss function to craft adversarial samples. We've experimentally verified the efficacy of our approach on MNIST (Digit), CIFAR10 datasets. Detailed experiments confirmed that our approach substantially improves robustness against gradient-based targeted and untargeted attack threats. And, we showed that the increased non-linearity at the output layer has some ad-ditional benefits against some other attack methods like Deepfool attack.Yayın Analysis of single image super resolution models(IEEE, 2022-11-18) Köprülü, Mertali; Eskil, Mustafa TanerImage Super-Resolution (SR) is a set of image processing techniques which improve the resolution of images and videos. Deep learning approaches have made remarkable improvement in image super-resolution in recent years. This article aims and seeks to provide a comprehensive analysis on recent advances of models which has been used in image superresolution. This study has been investigated over other essential topics of current model problems, such as publicly accessible benchmark data-sets and performance evaluation measures. Finally, The study concluded these analysis by highlighting several weaknesses of existing base models as their feeding strategy and approved that the training technique which is Blind Feeding, which led several model to achieve state-of-the art.
