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ERDEM, Arif Tanju

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Arif Tanju

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Now showing 1 - 10 of 21
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    Bispectrum estimation using a MISO autoregressive model
    (Springer International Publishing, 2016) Erdem, Tanju; Ercan, Ali Özer; Electrical & Electronics Engineering; Computer Science; ERDEM, Arif Tanju; ERCAN, Ali Özer
    Bispectra are third-order statistics that have been used extensively in analyzing nonlinear and non-Gaussian data. Bispectrum of a process can be computed as the Fourier transform of its bicumulant sequence. It is in general hard to obtain reliable bicumulant samples at high lags since they suffer from large estimation variance. This paper proposes a novel approach for estimating bispectrum from a small set of given low lag bicumulant samples. The proposed approach employs an underlying MISO system composed of stable and causal autoregressive components. We provide an algorithm to compute the parameters of such a system from the given bicumulant samples. Experimental results show that our approach is capable of representing non-polynomial spectra with a stable underlying system model, which results in better bispectrum estimation than the leading algorithm in the literature.
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    Automatic fall detection for elderly by using features extracted from skeletal data
    (IEEE, 2013) Davari, Amir; Aydin, T; Erdem, Tanju; Computer Science; ERDEM, Arif Tanju; Davari, Amir
    Automatic detection of unusual events such as falls is very important especially for elderly people living alone. Realtime detection of these events can reduce the health risks associated with a fall. In this paper, we propose a novel method for automatic detection of fall event by using depth cameras. Depth images generated by these cameras are used in computing the skeletal data of a person. Our contribution is to use features extracted from the skeletal data to form a strong set of features which can help us achieve an increased precision at low redundancy. Our findings indicate that our features, which are derived from skeletal data, are moderately powerful for detecting unusual events such as fall.
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    Hareketten yapı çıkarımı için görsel kullanıcı arayüzü
    (IEEE, 2014) Hüroğlu, Cengiz; Erdem, Tanju; Computer Science; ERDEM, Arif Tanju; Hüroğlu, Cengiz
    The usage of computer vision applications such as 3D reconstruction, motion tracking and augmented reality gradually increases. The first and the most important stage of these kind of applications is estimating the 3D scene model and motion information. We developed an easy-to-use user interface in order to use in these kind of applications. The user interface we developed, contains important functionalities such as robust feature detection, feature matching and extracting 3D structure from motion. We benefit from some open-source computer vision tools such as SiftGPU and Bundler and integrate them in our user interface application, so they can easily be used.
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    Curriculum learning for face recognition
    (IEEE, 2021) Büyüktaş, Barış; Erdem, Ç. E.; Erdem, Tanju; Computer Science; ERDEM, Arif Tanju
    We present a novel curriculum learning (CL) algorithm for face recognition using convolutional neural networks. Curriculum learning is inspired by the fact that humans learn better, when the presented information is organized in a way that covers the easy concepts first, followed by more complex ones. It has been shown in the literature that that CL is also beneficial for machine learning tasks by enabling convergence to a better local minimum. In the proposed CL algorithm for face recognition, we divide the training set of face images into subsets of increasing difficulty based on the head pose angle obtained from the absolute sum of yaw, pitch and roll angles. These subsets are introduced to the deep CNN in order of increasing difficulty. Experimental results on the large-scale CASIA-WebFace-Sub dataset show that the increase in face recognition accuracy is statistically significant when CL is used, as compared to organizing the training data in random batches.
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    A multi-sensor integrated head-mounted display setup for augmented reality applications
    (IEEE, 2015) Kermen, Ahmet; Aydın, T.; Ercan, Ali Özer; Erdem, Tanju; Electrical & Electronics Engineering; Computer Science; ERCAN, Ali Özer; ERDEM, Arif Tanju; Kermen, Ahmet
    We present an HMD based AR system comprising visual and inertial sensors. The visual sensor is a camera pair and the inertial sensors consist of an accelerometer and a gyroscope. We discuss the temporal and spatial calibration issues that relate to such a system. We introduce simple yet effective methods for estimating the time lag between the camera and the inertial sensors and for estimating the relative pose between the camera and the inertial sensors. These methods do not require a complicated setup for data collection and involve simple equations to solve. Sample results are presented to demonstrate the visual performance of the system.
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    RANSAC-based training data selection on spectral features for emotion recognition from spontaneous speech
    (Springer International Publishing, 2011) Bozkurt, E.; Erzin, E.; Erdem, Tanju; Eroğlu Erdem, Ç.; Computer Science; ERDEM, Arif Tanju
    Training datasets containing spontaneous emotional speech are often imperfect due the ambiguities and difficulties of labeling such data by human observers. In this paper, we present a Random Sampling Consensus (RANSAC) based training approach for the problem of emotion recognition from spontaneous speech recordings. Our motivation is to insert a data cleaning process to the training phase of the Hidden Markov Models (HMMs) for the purpose of removing some suspicious instances of labels that may exist in the training dataset. Our experiments using HMMs with Mel Frequency Cepstral Coefficients (MFCC) and Line Spectral Frequency (LSF) features indicate that utilization of RANSAC in the training phase provides an improvement in the unweighted recall rates on the test set. Experimental studies performed over the FAU Aibo Emotion Corpus demonstrate that decision fusion configurations with LSF and MFCC based classifiers provide further significant performance improvements.
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    Hareketli kameralarda örtücü tespiti
    (IEEE, 2012) Aydın, T.; Erdem, Tanju; Computer Science; ERDEM, Arif Tanju
    In this work, we propose a novel method for detection of moving or static objects which are not part of the background in an image sequence captured by a moving camera. The method is composed of two stages, namely, construction of a background model and detection of occluding objects. The constructed background model makes it possible to detect both static and moving occluding objects. Experiments on the real scenes show that the proposed method works effectively and produces visually correct results.
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    On sensor fusion for head tracking in augmented reality applications
    (IEEE, 2011) Ercan, Ali Özer; Erdem, Tanju; Electrical & Electronics Engineering; Computer Science; ERCAN, Ali Özer; ERDEM, Arif Tanju
    The paper presents a simple setup consisting of a camera and an accelerometer located on a head mounted display, and investigates the performance of head tracking for augmented reality applications using this setup. The information from the visual and inertial sensors is fused in an extended Kalman filter (EKF) tracker. The performance of treating accelerometer measurements as control inputs is compared to treating both camera and accelerometer measurements as measurements, i.e., fusing them in the measurement update stage of the EKF simultaneously. It is concluded via simulations that treating accelerometer measurements as control inputs performs practically as good as treating both measurements as measurements, while providing a lower complexity tracker.
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    Combining haar feature and skin color based classifiers for face detection
    (IEEE, 2011) Eroğlu Erdem, Ç.; Ulukaya, S.; Karaali, A.; Erdem, Tanju; Computer Science; ERDEM, Arif Tanju
    This paper presents a hybrid method for face detection in color images. The well known Haar feature-based face detector developed by Viola and Jones (VJ), that has been designed for gray-scale images is combined with a skin-color filter, which provides complementary information in color images. The image is first passed through a Haar-Feature based face detector, which is adjusted such that it is operating at a point on its ROC curve that has a low number of missed faces but a high number of false detections. Then, using the proposed skin color post-filtering method many of these false detections can be eliminated easily. We also use a color compensation algorithm to reduce the effects of lighting. Our experimental results on the Bao color face database show that the proposed method is superior to the original VJ algorithm and also to other skin color based pre-filtering methods in the literature in terms of precision.
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    Effect of camera-IMU displacement calibration error on tracking performance
    (IEEE, 2015) Maxudov, Nekruzjon; Ercan, Ali Özer; Erdem, Tanju; Electrical & Electronics Engineering; Computer Science; ERCAN, Ali Özer; ERDEM, Arif Tanju; Maxudov, Nekruzjon
    Due to their complementary properties, inertial measurement units (IMU) and cameras are used in ego-motion tracking applications. For this, the relative rotation and displacement between the camera and IMU reference frames has to be known. There are established methods for the accurate estimation of the relative orientation, however, accurate estimation of the displacement is still a challenging problem. When this is not possible, one might resort to the alternative approach of fusing camera and gyroscope data only, as this does not require the displacement information. To be able to asses such alternatives, this paper presents a systematic methodology based on realistic simulations to analyze the effect of the camera - IMU displacement calibration error on tracking performance, and discusses in detailed simulations the dependency of tracker performance metrics on the camera - IMU displacement's magnitude and calibration error.