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ERDEM, Arif Tanju

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Arif Tanju

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    Conference paperPublicationMetadata only
    INTERSPEECH 2009 duygu tanıma yarışması değerlendirmesi
    (IEEE, 2010) Bozkurt, E.; Erzin, E.; Eroğlu Erdem, Ç.; Erdem, Tanju; Computer Science; ERDEM, Arif Tanju
    Bu makalede INTERSPEECH 2009 Duygu Tanıma Yarışması sonuçlarını değerlendiriyoruz. Yarışmanın sunduğu problem doğal ve duygu bakımından zengin FAU Aibo konuşma kayıtlarının beş ve iki duygu sınıfına en doğru şekilde ayrılmasıdır. Bu problemi çözmek için bürün ilintili, spektral ve SMM-temelli (sakl Markov model) öznitelikleri Gauss Bileşen Model (GBM) sınıflandırıcılar ile inceliyoruz. Spektral öznitelikler, Mel frekans kepstral katsayıların (MFKK), doru spektral frekans (DSF) katsayılarını ve bunların türevlerini içerirken, bürün öznitelikleri perde, perdenin birinci türevi ve enerjiden oluşuyor. Bürün ilintili özniteliklerin zamanla değimini tanımlayan SMM özniteliklerini, güdümsüz eğitilen SMM yapılar ile elde ediyoruz. Ayrıca, konuşmadan duygu tanıma sonuçların iyileştirmek için farklı özniteliklerin veri kaynaşımın ve farklı sınıflandırıcıların karar kaynaşımını da inceliyoruz. İki aşamalı karar kaynaşım yöntemimiz beş ve iki sınıflı problemler için sırasıyla,% 41.59 ve %67.90 başarım oranını ve tüm yarışma sonuçları arasında 2. ve 4. sırayı elde etti .
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    Book ChapterPublicationMetadata only
    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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    Conference paperPublicationMetadata only
    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.
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    Conference paperPublicationMetadata only
    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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    Conference paperPublicationMetadata only
    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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    ArticlePublicationMetadata only
    More learning with less labeling for face recognition
    (Elsevier, 2023-05) Büyüktaş, Barış; Eroğlu Erdem, Ç.; Erdem, Tanju; Computer Science; ERDEM, Arif Tanju; Büyüktaş, Barış
    In this paper, we propose an improved face recognition framework where the training is started with a small set of human annotated face images and then new images are incorporated into the training set with minimum human annotation effort. In order to minimize the human annotation effort for new images, the proposed framework combines three different strategies, namely self-paced learning (SPL), active learning (AL), and minimum sparse reconstruction (MSR). As in the recently proposed ASPL framework [1], SPL is used for automatic annotation of easy images, for which the classifiers are highly confident and AL is used to request the help of an expert for annotating difficult or low-confidence images. In this work, we propose to use MSR to subsample the low-confidence images based on diversity using minimum sparse reconstruction in order to further reduce the number of images that require human annotation. Thus, the proposed framework provides an improvement over the recently proposed ASPL framework [1] by employing MSR for eliminating “similar” images from the set selected by AL for human annotation. Experimental results on two large-scale datasets, namely CASIA-WebFace-Sub and CACD show that the proposed method called ASPL-MSR can achieve similar face recognition performance by using significantly less expert-annotated data as compared to the state-of-the-art. In particular, ASPL-MSR requires manual annotation of only 36.10% and 54.10% of the data in CACD and CASIA-WebFace-Sub datasets, respectively, to achieve the same face recognition performance as the case when the whole training data is used with ground truth labels. The experimental results indicate that the number of manually annotated samples have been reduced by nearly 4% and 2% on the two datasets as compared to ASPL [1].
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    Conference paperPublicationOpen Access
    RANSAC-based training data selection for speaker state recognition
    (The International Speech Communications Association, 2011) Bozkurt, E.; Erzin, E.; Erdem, Ç. E.; Erdem, Tanju; Computer Science; ERDEM, Arif Tanju
    We present a Random Sampling Consensus (RANSAC) based training approach for the problem of speaker state recognition from spontaneous speech. Our system is trained and tested with the INTERSPEECH 2011 Speaker State Challenge corpora that includes the Intoxication and the Sleepiness Subchallenges, where each sub-challenge defines a two-class classification task. We aim to perform a RANSAC-based training data selection coupled with the Support Vector Machine (SVM) based classification to prune possible outliers, which exist in the training data. Our experimental evaluations indicate that utilization of RANSAC-based training data selection provides 66.32 % and 65.38 % unweighted average (UA) recall rate on the development and test sets for the Sleepiness Sub-challenge, respectively and a slight improvement on the Intoxicationubchallenge performance.
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    Conference paperPublicationMetadata only
    Authoring and presentation tools for distance learning over interactive TV
    (ACM, 2010) Gürel, T. C.; Erdem, Tanju; Kermen, A.; Özkan, M. K.; Eroğlu Erdem, Ç.; Computer Science; ERDEM, Arif Tanju
    We present a complete system for distance learning over interactive TV with novel tools for authoring and presentation of lectures and exams, and evaluation of student and system performance. The main technological contributions of the paper include the development of plug-in software so that PowerPoint can be used to prepare presentations for the set-top-box, a software tool to convert PDF documents containing multiple-choice questions into interactive exams, and a virtual teacher whose facial animation is automatically generated from speech.
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    3B kamera takibi için eylemsizlik algılayıcılarının birleştirilmesi
    (IEEE, 2012) Özer, N.; Erdem, Tanju; Ercan, Ali Özer; Eroğlu Erdem, Ç.; Electrical & Electronics Engineering; Computer Science; ERDEM, Arif Tanju; ERCAN, Ali Özer
    It is well known in a Bayesian filtering framework, the use of inertial sensors such as accelerometers and gyroscopes improves 3D tracking performance compared to using camera measurements only. The performance improvement is more evident when the camera undergoes a high degree of motion. However, it is not well known whether the inertial sensors should be used as control inputs or as measurements. In this paper, we present the results of an extensive set of simulations comparing different combinations of using inertial sensors as control inputs or as measurements. We show that it is better use a gyroscope as a control input while an accelerometer can be used as a measurement or control input. We also derive and present the extended Kalman filter (EKF) equations for a specific case of fusing accelerometer and gyroscope data that has not been reported before.
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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.