Prof. Gholamreza Anbarjafari
University of Tartu, Estonia.
+2
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Academic Career Length
19Years (2008-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
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Journal Article
Dorota KaminskaTSTomasz Sapiński
Tomasz Sapiński
Slawomir Jan WiakTTToomas Tikk
Toomas TikkRHRain Eric Haamer
Rain Eric HaamerGholamreza Anbarjafari +3
In the education process, students face problems with understanding due to the complexity, necessity of abstract thinking and concepts. More and more educational centres around the world have started to introduce powerful new technology-based tools that help meet the needs of the diverse student population. Over the last several years, virtual reality (VR) has moved from being the purview of gaming to professional development. It plays an important role in teaching process, providing an interesting and engaging way of acquiring information. What follows is an overview of the big trend, opportunities and concerns associated with VR in education. We present new opportunities in VR and put together the most interesting, recent virtual reality applications used in education in relation to several education areas such as general, engineering and health-related education. Additionally, this survey contributes by presenting methods for creating scenarios and different approaches for testing and validation. Lastly, we conclude and discuss future directions of VR and its potential to improve the learning experience.
16 October 2018 in IEEE Transactions on Affective Computing
Journal Article
FNFatemeh Noroozi
Fatemeh NorooziCCCiprian Adrian Corneanu
Ciprian Adrian CorneanuDorota KaminskaTSTomasz Sapinski
Tomasz Sapinski
Sergio EscaleraGholamreza AnbarjafariAutomatic emotion recognition has become a trending research topic in the past decade. While works based on facial expressions or speech abound, recognising affect from body gestures remains a less explored topic. We present a new comprehensive survey hoping to boost research in the field. We first introduce emotional body gestures as a component of what is commonly known as "body language" and comment general aspects as gender differences and culture dependence. We then define a complete framework for automatic emotional body gesture recognition. We introduce person detection and comment static and dynamic body pose estimation methods both in RGB and 3D. We then comment the recent literature related to representation learning and emotion recognition from images of emotionally expressive gestures. We also discuss multi-modal approaches that combine speech or face with body gestures for improved emotion recognition. While pre-processing methodologies (e.g. human detection and pose estimation) are nowadays mature technologies fully developed for robust large scale analysis, we show that for emotion recognition the quantity of labelled data is scarce, there is no agreement on clearly defined output spaces and the representations are shallow and largely based on naive geometrical representations.
18 October 2010 in IEEE Transactions on Image Processing
Journal Article
HDHasan Demirel
Hasan Demirel
Gholamreza AnbarjafariIn this correspondence, the authors propose an image resolution enhancement technique based on interpolation of the high frequency subband images obtained by discrete wavelet transform (DWT) and the input image. The edges are enhanced by introducing an intermediate stage by using stationary wavelet transform (SWT). DWT is applied in order to decompose an input image into different subbands. Then the high frequency subbands as well as the input image are interpolated. The estimated high frequency subbands are being modified by using high frequency subband obtained through SWT. Then all these subbands are combined to generate a new high resolution image by using inverse DWT (IDWT). The quantitative and visual results are showing the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.
17 November 2009 in IEEE Geoscience and Remote Sensing Letters
Journal Article
HDHasan Demirel
Hasan Demirel
COCagri Ozcinar
Cagri OzcinarGholamreza AnbarjafariIn this letter, a new satellite image contrast enhancement technique based on the discrete wavelet transform (DWT) and singular value decomposition has been proposed. The technique decomposes the input image into the four frequency subbands by using DWT and estimates the singular value matrix of the low-low subband image, and, then, it reconstructs the enhanced image by applying inverse DWT. The technique is compared with conventional image equalization techniques such as standard general histogram equalization and local histogram equalization, as well as state-of-the-art techniques such as brightness preserving dynamic histogram equalization and singular value equalization. The experimental results show the superiority of the proposed method over conventional and state-of-the-art techniques.
9 June 2017 in IEEE Transactions on Affective Computing
Journal Article
FNFatemeh Noroozi
Fatemeh Noroozi
MARINA MARJANOVICANAngelina Njegus
Angelina Njegus
Sergio EscaleraGholamreza AnbarjafariThis paper presents a multimodal emotion recognition system, which is based on the analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral Coefficients, Filter Bank Energies and prosodic features are extracted. For the visual part, two strategies are considered. First, facial landmarks' geometric relations, i.e., distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames, which are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to key-frames summarizing videos. Finally, confidence outputs of all the classifiers from all the modalities are used to define a new feature space to be learned for final emotion label prediction, in a late fusion/stacking fashion. The experiments conducted on the SAVEE, eNTERFACE'05, and RML databases show significant performance improvements by our proposed system in comparison to current alternatives, defining the current state-of-the-art in all three databases.
28 January 2011 in IEEE Transactions on Geoscience and Remote Sensing
Journal Article
HDHasan Demirel
Hasan Demirel
Gholamreza AnbarjafariSatellite images are being used in many fields of research. One of the major issues of these types of images is their resolution. In this paper, we propose a new satellite image resolution enhancement technique based on the interpolation of the high-frequency subbands obtained by discrete wavelet transform (DWT) and the input image. The proposed resolution enhancement technique uses DWT to decompose the input image into different subbands. Then, the high-frequency subband images and the input low-resolution image have been interpolated, followed by combining all these images to generate a new resolution-enhanced image by using inverse DWT. In order to achieve a sharper image, an intermediate stage for estimating the high-frequency subbands has been proposed. The proposed technique has been tested on satellite benchmark images. The quantitative (peak signal-to-noise ratio and root mean square error) and visual results show the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.
About Me
Gholamreza Anbarjafari is head of the iCV Lab, the largest computer vision and applied machine learning lab in the Baltic States, and a Professor of Computer Vision at
the Institute of Technology, Faculty of Science and Technology, University of
Tartu. He received his BSc and MSc in Electrical and Electronic Engineering at
the Eastern Mediterranean University in 2006 and 2008, respectively, and earned
his Ph.D. in Electrical and Electronic Engineering from the same university in
2010. He is Chief Data Scientist and Director of AI in PwC Finland. He is a co-founder of iVCV (the first responsible AI and ethical AI startup in Estonia). He is an expert in computer vision, human--robot interaction, 3D modeling, and responsible AI. He has conducted various R&D projects for industries. He has received grants from national and international venues. He is an associate editor of several journals such as SIVP, Information, Entropy, and JIVP and has been lead guest editor of several special issues on human behaviour analysis. He was the Deputy
Scientific Coordinator of The European Network on Integrating Vision and
Language (iV&L Net) ICT COST Action IC1307 (2016–2018) and the Chair of
SP/CAS/SCC Joint Chapter of IEEE Estonia Section (2018–2021). He is also a Senior
Member of IEEE. He has been in the technical committee of conferences such as ICOSST, ICGIP, SIU, SampTA, FG, and ICPR. He is organising a challenge and a workshop on FG17, CVPR17, ICCV17, ECML19, FG20, and FG24.