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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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    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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    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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    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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    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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    Use of line spectral frequencies for emotion recognition from speech
    (IEEE, 2010) Bozkurt, E.; Erzin, E.; Eroğlu Erdem, Ç.; Erdem, Tanju; Computer Science; ERDEM, Arif Tanju
    We propose the use of the line spectral frequency (LSF) features for emotion recognition from speech, which have not been been previously employed for emotion recognition to the best of our knowledge. Spectral features such as mel-scaled cepstral coefficients have already been successfully used for the parameterization of speech signals for emotion recognition. The LSF features also offer a spectral representation for speech, moreover they carry intrinsic information on the formant structure as well, which are related to the emotional state of the speaker. We use the Gaussian mixture model (GMM) classifier architecture, that captures the static color of the spectral features. Experimental studies performed over the Berlin Emotional Speech Database and the FAU Aibo Emotion Corpus demonstrate that decision fusion configurations with LSF features bring a consistent improvement over the MFCC based emotion classification rates.
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    Kamera ve ataletsel ölçüm birimi iç kalibrasyonu
    (IEEE, 2013) Erdem, Tanju; Ercan, Ali Özer; Aydın, T.; Electrical & Electronics Engineering; Computer Science; ERDEM, Arif Tanju; ERCAN, Ali Özer
    In this paper, we address the problem of internal calibration of a camera and an inertial measurement unit (IMU). The internal calibration of a camera and an IMU requires the determination of the relative orientation and displacement between the camera and the IMU. Although the problem of orientation estimation is well studied, and there exists simple algorithms for it, current displacement estimation techniques require very special data collection setups or particular sensor movements. We propose a novel method for estimating the displacement between the camera and an IMU. Our method has a very simple data collection step and involves the solution of linear equations only. Experimental results with real data show the effectiveness of our proposed algorithm.
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    RANSAC-based training data selection for emotion recognition from spontaneous speech
    (ACM, 2010) Eroğlu Erdem, Ç.; Bozkurt, E.; Erzin, E.; Erdem, Tanju; Computer Science; ERDEM, Arif Tanju
    Training datasets containing spontaneous emotional expressions 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 various number of states and Gaussian mixtures per state indicate that utilization of RANSAC in the training phase provides an improvement of up to 2.84% in the unweighted recall rates on the test set. This improvement in the accuracy of the classifier is shown to be statistically significant using McNemar’s test.
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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.