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An integrated framework for developing and evaluating a lecture style assessment methodology

Multimedia Tools and Applications 2024年 1-28页

作者： Dimitriadou, Eleni Lanitis, Andreas Visual Media Computing Lab Department of Multimedia and Graphic Arts Cyprus University of Technology Limassol Cyprus CYENS Centre of Excellence Nicosia Cyprus

The aim of the work presented in this paper is to develop and evaluate an integrated lecture style evaluation methodology that provides, teachers instant feedback related to the quality of their lecturing style. The proposed method aims to promote improvement of lecture quality, that could upgrade the overall student learning experience. The proposed methodology utilizes specific measurable visual, and audio biometric characteristics extracted from a video showing the lecturer from the audience’s point of view. Measurable biometric features extracted during a lecture are combined to provide teachers with a score reflecting lecture style quality both at frame rate and by providing lecture quality metrics for the whole lecture. The results of a comprehensive quantitative evaluation indicate that the proposed methodology can be used for obtaining metrics that reflect lecture style quality. Furthermore, the performance evaluation of the proposed methodology was compared with the performance of humans in the task of lecture style evaluation. Results indicate that the proposed method not only achieves similar performance to human observers, but in some cases, it outperforms them. © The Author(s), under exclusive licence to Springer Science+Business media, LLC, part of Springer Nature 2024.

关键词： Biometrics

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Development and Evaluation of a Prototype VR Application for the Elderly, that can Help to Prevent Effects Related to Social Isolation 2

Development and Evaluation of a Prototype VR Application for...

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2nd International Conference on Interactive media, Smart Systems and Emerging Technologies, IMET 2022

作者： Anastasiadou, Zoe Lanitis, Andreas Cyprus University of Technology Visual Media Computing Lab Department of Multimedia and Graphic Arts Limassol Cyprus Cyens Centre of Excellence Cyprus

ISBN: (数字)9781665470162

ISBN: (纸本)9781665470162

The elderly need to communicate with their loved ones but they also need to get engaged in activities that require mental awareness as a means of preventing negative side-effects related to brain inactivity. This area of research is becoming increasingly important during periods of social isolation caused either by external factors, such as a pandemic, or by factors associated with reduced mobility in the elderly. In this paper, a prototype Virtual Reality application that will allow elderly users to deal with the problems of social isolation, while providing an entertaining brain-triggering activity, is presented. While this is work in progress, the results of an initial user evaluation provides insights related to the strengths and limitations of the prototype application, allowing the derivations of conclusions that can guide further development of the final application. © 2022 IEEE.

关键词： Brain

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End-to-End Learnable Multi-Scale Feature Compression for VCM

arXiv

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arXiv 2023年

作者： Kim, Yeongwoong Jeong, Hyewon Yu, Janghyun Kim, Younhee Lee, Jooyoung Jeong, Se Yoon Kim, Hui Yong Visual Media Lab School of Computing Kyung Hee University Korea Republic of Korea Republic of

The proliferation of deep learning-based machine vision applications has given rise to a new type of compression, so called video coding for machine (VCM). VCM differs from traditional video coding in that it is optimized for machine vision performance instead of human visual quality. In the feature compression track of MPEG-VCM, multi-scale features extracted from images are subject to compression. Recent feature compression works have demonstrated that the versatile video coding (VVC) standard-based approach can achieve a BD-rate reduction of up to 96% against MPEG-VCM feature anchor. However, it is still sub-optimal as VVC was not designed for extracted features but for natural images. Moreover, the high encoding complexity of VVC makes it difficult to design a lightweight encoder without sacrificing performance. To address these challenges, we propose a novel multi-scale feature compression method that enables both the end-to-end optimization on the extracted features and the design of lightweight encoders. The proposed model combines a learnable compressor with a multi-scale feature fusion network so that the redundancy in the multi-scale features is effectively removed. Instead of simply cascading the fusion network and the compression network, we integrate the fusion and encoding processes in an interleaved way. Our model first encodes a larger-scale feature to obtain a latent representation and then fuses the latent with a smaller-scale feature. This process is successively performed until the smallest-scale feature is fused and then the encoded latent at the final stage is entropy-coded for transmission. The results show that our model outperforms previous approaches by at least 52% BD-rate reduction and has ×5 to ×27 times less encoding time for object detection. It is noteworthy that our model can attain near-lossless task performance with only 0.002-0.003% of the uncompressed feature data size. Copyright © 2023, The Authors. All rights reserved.

关键词： Image compression

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AN INTEGRATED FRAMEWORK FOR DEVELOPING AND EVALUATING AN AUTOMATED LECTURE STYLE ASSESSMENT SYSTEM

arXiv

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arXiv 2023年

The aim of the work presented in this paper is to develop and evaluate an integrated system that provides automated lecture style evaluation, allowing teachers to get instant feedback related to the goodness of their lecturing style. The proposed system aims to promote improvement of lecture quality, that could upgrade the overall student learning experience. The proposed application utilizes specific measurable biometric characteristics, such as facial expressions, body activity, speech rate and intonation, hand movement, and facial pose, extracted from a video showing the lecturer from the audience point of view. Measurable biometric features extracted during a lecture are combined to provide teachers with a score reflecting lecture style quality both at frame rate and by providing lecture quality metrics for the whole lecture. The acceptance of the proposed lecture style evaluation system was evaluated by chief education officers, teachers and students regarding the functionality, usefulness of the application, and possible improvements. The results indicate that participants found the application novel and useful in providing automated feedback regarding lecture quality. Furthermore, the performance evaluation of the proposed system was compared with the performance of humans in the task of lecture style evaluation. Results indicate that the proposed system not only achieves similar performance to human observers, but in some cases, it outperforms them. Copyright © 2023, The Authors. All rights reserved.

关键词： Biometrics

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Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study

Attention Modules Improve Image-Level Anomaly Detection for ...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： André Luiz Vieira e Silva Francisco Simões Danny Kowerko Tobias Schlosser Felipe Battisti Veronica Teichrieb Voxar Labs Centro de Informática Universidade Federal de Pernambuco Brazil Visual Computing Lab DC Universidade Federal Rural de Pernambuco Brazil Junior Professorship of Media Computing Chemnitz University of Technology Germany

Within (semi-)automated visual industrial inspection, learning-based approaches for assessing visual defects, including deep neural networks, enable the processing of otherwise small defect patterns in pixel size on high-resolution imagery. The emergence of these often rarely occurring defect patterns explains the general need for labeled data corpora. To alleviate this issue and advance the current state of the art in unsupervised visual inspection, this work proposes a DifferNet-based solution enhanced with attention modules: AttentDifferNet. It improves image-level detection and classification capabilities on three visual anomaly detection datasets for industrial inspection: InsPLAD-fault, MVTec AD, and Semiconductor Wafer. In comparison to the state of the art, AttentDifferNet achieves improved results, which are, in turn, highlighted throughout our quali-quantitative study. Our quantitative evaluation shows an average improvement - compared to DifferNet -of 1.77 ± 0.25 percentage points in overall AUROC considering all three datasets, reaching SOTA results in InsPLAD-fault, an industrial inspection in-the-wild dataset. As our variants to AttentDifferNet show great prospects in the context of currently investigated approaches, a baseline is formulated, emphasizing the importance of attention for industrial anomaly detection both in the wild and in controlled environments.

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Artwork Identification in a Museum Environment: A Quantitative Evaluation of Factors Affecting Identification Accuracy 8th

Artwork Identification in a Museum Environment: A Quantitati...

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8th European-Mediterranean Conference, EuroMed 2020

作者： Lanitis, A. Theodosiou, Z. Partaourides, H. Visual Media Computing Lab Department of Multimedia and Graphic Arts Cyprus University of Technology Limassol Limassol Cyprus CYENS Centre of Excellence Nicosia Cyprus

ISBN: (纸本)9783030730420

The ability to identify the artworks that a museum visitor is looking at, using first-person images seamlessly captured by wearable cameras can be used as a means for invoking applications that provide information about the exhibits, and provide information about visitors’ activities. As part of our efforts to optimize the artwork recognition accuracy of an artwork identification system under development, an investigation aiming to determine the effect of different conditions on the artwork recognition accuracy in a gallery/exhibition environment is presented. Through the controlled introduction of different distractors in a virtual museum environment, it is feasible to assess the effect on the recognition performance of different conditions. The results of the experiment are important for improving the robustness of artwork recognition systems, and at the same time the conclusions of this work can provide specific guidelines to curators, museum professionals and visitors, that will enable the efficient identification of artworks, using images captured with wearable cameras in a museum environment. © 2021, Springer Nature Switzerland AG.

关键词： Computer vision

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A Smartphone Application Designed to Detect Obstacles for Pedestrians’ Safety 6th

A Smartphone Application Designed to Detect Obstacles for Pe...

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6th EAI International Conference on Science and Technologies for Smart Cities, SmartCity 2020

作者： Thoma, Marios Theodosiou, Zenonas Partaourides, Harris Tylliros, Charalambos Antoniades, Demetris Lanitis, Andreas Research Centre on Interactive Media Smart Systems and Emerging Technologies - RISE Nicosia Cyprus Visual Media Computing Research Lab Department of Multimedia and Graphic Arts Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9783030760625

Encouraging people to walk rather than using other means of transportation is an important factor towards personal health and environmental sustainability. However, given the large number of pedestrian accidents recorded every year, the need for safe urban environments is increasing. Taking advantage of the potential of citizen-science for crowdsourcing data and creating awareness, we developed a smartphone application for enhancing the safety of pedestrians while walking in cities. Using the application, citizens will monitor the urban sidewalks and update a crowdsourcing platform with the detected barriers and damages that hinder safe walking, along with their location on a city map. To help users assign the correct type of obstacle, and authorities to assess the urgency, a Convolutional Neural Network (CNN) model for barrier and damage recognition is embedded in the application. The results of a user evaluation, based on a group of volunteers who used the application in real conditions, demonstrate the potential of using the application in conjunction with a smart city framework. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Crowdsourcing

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Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study

arXiv

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arXiv 2023年

作者： Vieira e Silva, André Luiz Simões, Francisco Kowerko, Danny Schlosser, Tobias Battisti, Felipe Teichrieb, Veronica Voxar Labs Centro de Informática Universidade Federal de Pernambuco Brazil Media Computing Chemnitz University of Technology Germany Visual Computing Lab DC Universidade Federal Rural de Pernambuco Brazil

Within (semi-)automated visual industrial inspection, learning-based approaches for assessing visual defects, including deep neural networks, enable the processing of otherwise small defect patterns in pixel size on high-resolution imagery. The emergence of these often rarely occurring defect patterns explains the general need for labeled data corpora. To alleviate this issue and advance the current state of the art in unsupervised visual inspection, this work proposes a DifferNet-based solution enhanced with attention modules: AttentDifferNet. It improves image-level detection and classification capabilities on three visual anomaly detection datasets for industrial inspection: InsPLAD-fault, MVTec AD, and Semiconductor Wafer. In comparison to the state of the art, AttentDifferNet achieves improved results, which are, in turn, highlighted throughout our quali-quantitative study. Our quantitative evaluation shows an average improvement - compared to DifferNet - of 1.77 ± 0.25 percentage points in overall AUROC considering all three datasets, reaching SOTA results in InsPLAD-fault, an industrial inspection in-the-wild dataset. As our variants to AttentDifferNet show great prospects in the context of currently investigated approaches, a baseline is formulated, emphasizing the importance of attention for industrial anomaly detection both in the wild and in controlled environments. © 2023, CC BY-NC-SA.

关键词： Anomaly detection

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Struct2Hair: A hair shape descriptor for hairstyle modeling

Struct2Hair: A hair shape descriptor for hairstyle modeling

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作者： Zhang, Wenshu Nie, Yinyu Guo, Shihui Chang, Jian Zhang, Jianjun Tong, Ruofeng Faculty of Arts and Media Norwich University of the Arts Norfolk United Kingdom The Visual Computing & Artificial Intelligence Lab Technical University of Munich München Germany School of Informatics Xiamen University Fujian China National Centre for Computer Animation Bournemouth University Dorset United Kingdom College of Computer Science and Technology Zhejiang University Zhejiang China

In recent years, it becomes possible to extract hair information for hair reconstruction from multiple cameras or monocular camera. Using a single image as the input avoids the high cost setups and complex calibration compared to multiviewed reconstruction. Taking advantage of an extendible hairstyle database, this paper introduced Struct2Hair, a novel single-viewed hair modelling approach by extracting hair shape descriptor (HSD). The HSD is defined as the fundamental structure-aware feature, which is a combination of critical shapes in a hairstyle. A complete dataset of critical hair shapes is constructed from a known database of three-dimensional (3D) hair models. We first analyze the input two-dimensional (2D) image to extract the orientation information and 2D hair sketch automatically. The extracted information is then used to retrieve the corresponding critical shapes with optimization to build the robust HSD. Finally, the HSD constructs a weighted 3D hair orientation field to guide full-head hair model generation. Our method can preserve local geometric features of hair and retain the whole shape of the hairstyle globally owing to the HSD, which will benefit further hair editing and stylization. © 2022 John Wiley & Sons Ltd.

关键词： Cameras

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Virtual-Reality Based Crisis Management Training for Teachers: An Overview of the VRTEACHER Project 23

Virtual-Reality Based Crisis Management Training for Teacher...

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23rd International Symposium on Computers in Education, SIIE 2021

作者： Stavroulia, Kalliopi Evangelia Aedo, Ignacio Diaz, Paloma Lanitis, Andreas Cyprus University of Technology Department of Multimedia and Graphic Arts Limassol Cyprus Universidad Carlos III de Madrid Computer Science Department Madrid Spain Cyprus University of Technology and CYENS Centre of Excellence Visual Media Computing Lab Department of Multimedia and Graphic Arts Limassol Cyprus

ISBN: (纸本)9781665440240

The coronavirus pandemic has created an educational crisis without precedent, posed new challenges, and highlighted the need for digital skills empowerment among teachers along with the use of technology-based approaches to support the delivery of education and ensure the future of learning. As a return to normality is far from over, the implementation of novel training approaches for teacher training is more imperative than ever. In this context, this paper presents the VRTEACHER project that aims to promote the use of digital technologies and innovative and open pedagogies as part of teachers' training methodology. The vision of the project is to empower teacher's skills (such as empathy, perspective-taking, self-efficacy, adaptability, etc.) and readiness related to class management in crisis situations, through Virtual Reality (VR) immersive and experiential training approaches that reflect real-life scenarios and situations. The aim and challenge of the project are not just to adapt the training provided to the digital era and connect it with new technologies but to build a holistic new approach to teacher training through the use of VR. © 2021 IEEE.

关键词： Virtual reality

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