Electrical & Electronics Engineering

Permanent URI for this collectionhttps://hdl.handle.net/10679/44

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Now showing 1 - 9 of 9

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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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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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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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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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Formant position based weighted spectral features for emotion recognition
(Elsevier, 2011) Bozkurt, E.; Erzin, E.; Eroğlu Erdem, Ç.; Erdem, Tanju; Computer Science; ERDEM, Arif Tanju
In this paper, we propose novel spectrally weighted mel-frequency cepstral coefficient (WMFCC) features for emotion recognition from speech. The idea is based on the fact that formant locations carry emotion-related information, and therefore critical spectral bands around formant locations can be emphasized during the calculation of MFCC features. The spectral weighting is derived from the normalized inverse harmonic mean function of the line spectral frequency (LSF) features, which are known to be localized around formant frequencies. The above approach can be considered as an early data fusion of spectral content and formant location information. We also investigate methods for late decision fusion of unimodal classifiers. We evaluate the proposed WMFCC features together with the standard spectral and prosody features using HMM based classifiers on the spontaneous FAU Aibo emotional speech corpus. The results show that unimodal classifiers with the WMFCC features perform significantly better than the classifiers with standard spectral features. Late decision fusion of classifiers provide further significant performance improvements.
Open Access
Fusing inertial sensor data in an extended kalman filter for 3D camera tracking
(IEEE, 2015-02) Erdem, Tanju; Ercan, Ali Özer; Electrical & Electronics Engineering; Computer Science; ERDEM, Arif Tanju; ERCAN, Ali Özer
In a setup where camera measurements are used to estimate 3D egomotion in an extended Kalman filter (EKF) framework, it is well-known that inertial sensors (i.e., accelerometers and gyroscopes) are especially useful when the camera undergoes fast motion. Inertial sensor data can be fused at the EKF with the camera measurements in either the correction stage (as measurement inputs) or the prediction stage (as control inputs). In general, only one type of inertial sensor is employed in the EKF in the literature, or when both are employed they are both fused in the same stage. In this paper, we provide an extensive performance comparison of every possible combination of fusing accelerometer and gyroscope data as control or measurement inputs using the same data set collected at different motion speeds. In particular, we compare the performances of different approaches based on 3D pose errors, in addition to camera reprojection errors commonly found in the literature, which provides further insight into the strengths and weaknesses of different approaches. We show using both simulated and real data that it is always better to fuse both sensors in the measurement stage and that in particular, accelerometer helps more with the 3D position tracking accuracy, whereas gyroscope helps more with the 3D orientation tracking accuracy. We also propose a simulated data generation method, which is beneficial for the design and validation of tracking algorithms involving both camera and inertial measurement unit measurements in general.
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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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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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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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Browsing by Institution Author "ERDEM, Arif Tanju"