Computer Science
Permanent URI for this collectionhttps://hdl.handle.net/10679/43
Browse
Browsing by Institution Author "ERDEM, Arif Tanju"
Now showing 1 - 12 of 12
- Results Per Page
- Sort Options
Conference ObjectPublication Metadata 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 TanjuWe 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.Conference ObjectPublication Metadata only 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, AmirAutomatic 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.Conference ObjectPublication Metadata 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 TanjuThis 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.Conference ObjectPublication Metadata only Hareketli kameralarda örtücü tespiti(IEEE, 2012) Aydın, T.; Erdem, Tanju; Computer Science; ERDEM, Arif TanjuIn 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.Conference ObjectPublication Metadata only Hareketten yapı çıkarımı için görsel kullanıcı arayüzü(IEEE, 2014) Hüroğlu, Cengiz; Erdem, Tanju; Computer Science; ERDEM, Arif Tanju; Hüroğlu, CengizThe 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.Conference ObjectPublication Metadata only Improving automatic emotion recognition from speech signals(International Speech Communications Association, 2009) Bozkurt, E.; Erzin, E.; Eroğlu Erdem, Ç.; Erdem, Tanju; Computer Science; ERDEM, Arif TanjuWe present a speech signal driven emotion recognition system. Our system is trained and tested with the INTERSPEECH 2009 Emotion Challenge corpus, which includes spontaneous and emotionally rich recordings. The challenge includes classifier and feature sub-challenges with five-class and two-class classification problems. We investigate prosody related, spectral and HMM-based features for the evaluation of emotion recognition with Gaussian mixture model (GMM) based classifiers. Spectral features consist of mel-scale cepstral coefficients (MFCC), line spectral frequency (LSF) features and their derivatives, whereas prosody-related features consist of mean normalized values of pitch, first derivative of pitch and intensity. Unsupervised training of HMM structures are employed to define prosody related temporal features for the emotion recognition problem. We also investigate data fusion of different features and decision fusion of different classifiers, which are not well studied for emotion recognition framework. Experimental results of automatic emotion recognition with the INTERSPEECH 2009 Emotion Challenge corpus are presented.Conference ObjectPublication Metadata only INTERSPEECH 2009 duygu tanıma yarışması değerlendirmesi(IEEE, 2010) Bozkurt, E.; Erzin, E.; Eroğlu Erdem, Ç.; Erdem, Tanju; Computer Science; ERDEM, Arif TanjuBu 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 .Conference ObjectPublication Metadata 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, AhmetWe 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.Conference ObjectPublication Metadata only 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 TanjuTraining 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.Conference ObjectPublication Open 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 TanjuWe 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.Book PartPublication Metadata 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 TanjuTraining 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.Conference ObjectPublication Open Access 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 TanjuWe 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.