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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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SOAR: Stochastic optimization for affine global point set registration 19

SOAR: Stochastic optimization for affine global point set re...

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19th International Workshop on Vision, Modeling and visualization, VMV 2014

作者： Agus, M. Gobbetti, E. Jaspe Villanueva, A. Mura, C. Pajarola, R. Visual Computing CRS4 Pula Italy Visualization and MultiMedia Lab University of Zurich Switzerland

We introduce a stochastic algorithm for pairwise affine registration of partially overlapping 3D point clouds with unknown point correspondences. The algorithm recovers the globally optimal scale, rotation, and translation alignment parameters and is applicable in a variety of difficult settings, including very sparse, noisy, and outlierridden datasets that do not permit the computation of local descriptors. The technique is based on a stochastic approach for the global optimization of an alignment error function robust to noise and resistant to outliers. At each optimization step, it alternates between stochastically visiting a generalized BSP-tree representation of the current solution landscape to select a promising transformation, finding point-to-point correspondences using a GPU-accelerated technique, and incorporating new error values in the BSP tree. In contrast to previous work, instead of simply constructing the tree by guided random sampling, we exploit the problem structure through a low-cost local minimization process based on analytically solving absolute orientation problems using the current correspondences. We demonstrate the quality and performance of our method on a variety of large point sets with different scales, resolutions, and noise characteristics. © The Eurographics Association 2014.

关键词： Global optimization

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Paintings alive: A virtual reality-based approach for enhancing the user experience of art gallery visitors 6th

Paintings alive: A virtual reality-based approach for enhanc...

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6th International European-Mediterranean Conference on Digital Heritage, EuroMed 2016

作者： Panayiotou, Stavros Lanitis, Andreas Visual Media Computing Research Lab Department of Multimedia and Graphic Arts Cyprus University of Technology Lemesos Cyprus

ISBN: (纸本)9783319489735

Visits to art museums are not appealing to a wide sector of the public, limiting in that way the exposure of art works to the wider community and especially the younger generation. In this paper we investigate the use of Virtual Reality (VR) for displaying animated artworks as a means for creating an enhanced user experience that could make visits to art-galleries more appealing. A key point of the proposed technique is the analysis of the art-works that enables the determination of a most appropriate setting and animation that best promotes the original message of the painter. The promise of this approach has been validated through an experimental user evaluation where users clearly indicated the benefits of using interactive virtual environments for presenting animated art paintings. © Springer International Publishing AG 2016.

关键词： Virtual reality

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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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ROSS: Robust Learning of One-Shot 3D Shape Segmentation

ROSS: Robust Learning of One-Shot 3D Shape Segmentation

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Shuaihang Yuan Yi Fang NYU Multimedia and Visual Computing Lab USA

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

3D shape segmentation is a fundamental computer vision task that partitions the object into labeled semantic parts. Recent approaches to 3D shape segmentation learning heavily rely on high-quality labeled training datasets. This limits their use in applications to handle the large scale unannotated datasets. In this paper, we proposed a novel semi-supervised approach, named Robust Learning of OneShot 3D Shape Segmentation (ROSS), which only requires one single exemplar labeled shape for training. The proposed ROSS can generalize its ability from a one-shot training process to predict the segmentation for previously unseen 3D shape models. The proposed ROSS is composed of three major modules for 3D shape segmentation as follows. The global shape descriptor generator is the first module that utilizes the proposed reference weighted convolution to learn a 3D shape descriptor. The second module is a part-aware shape descriptor constructor that can generate weighted descriptors from a learned 3D shape descriptor according to semantic parts without supervision. The shape morphing with label transferring works as the last module. It morphs the exemplar shape and then transfers labels from the transformed exemplar shape to the target shape. The extensive experimental results on 3D mesh datasets demonstrate the ROSS is robust to noise and incomplete shapes and it can be applied to unannotated datasets. The experiment shows the proposed ROSS can achieve comparable performance with the supervised method.

关键词： Shape Three-dimensional displays Task analysis Convolution Learning systems Robustness Training

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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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MDA-Net: Memorable Domain Adaptation Network for Monocular Depth Estimation 31

MDA-Net: Memorable Domain Adaptation Network for Monocular D...

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31st British Machine Vision Conference, BMVC 2020

作者： Zhu, Jing Shi, Yunxiao Ren, Mengwei Fang, Yi NYU Multimedia and Visual Computing Lab United States New York University United States New York University Abu Dhabi United Arab Emirates

Monocular depth estimation is a challenging task that aims to predict a corresponding depth map from a given single RGB image. Recent deep learning models have been proposed to predict the depth from the image by learning the alignment of deep features between the RGB image and the depth domains. In this paper, we present a novel approach, named Memorable Domain Adaptation Network (MDA-Net), to more effectively transfer domain features for monocular depth estimation by taking into account the common structure regularities (e.g., repetitive structure patterns, planar surfaces, symmetries) in domain adaptation. To this end, we introduce a new Structure-Oriented Memory (SOM) module to learn and memorize the structure-specific information between RGB image domain and the depth domain. More specifically, in the SOM module, we develop a Memorable Bank of Filters (MBF) unit to learn a set of filters that memorize the structure-aware image-depth residual pattern, and also an Attention Guided Controller (AGC) unit to control the filter selection in the MBF given image features queries. Given the query image feature, the trained SOM module is able to adaptively select the best customized filters for cross-domain feature transferring with an optimal structural disparity between image and depth. In summary, we focus on addressing this structure-specific domain adaption challenge by proposing a novel end-to-end multi-scale memorable network for monocular depth estimation. The experiments show that our MDA-Net demonstrates the superior performance compared to the existing supervised monocular depth estimation approaches on the challenging KITTI and NYU Depth V2 benchmarks. © 2020. The copyright of this document resides with its authors.

关键词： Benchmarking

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Robust Image Matching By Dynamic Feature Selection 31

Robust Image Matching By Dynamic Feature Selection

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31st British Machine Vision Conference, BMVC 2020

作者： Huang, Hao Chen, Jianchun Li, Xiang Wang, Linging Fang, Yi NYU Multimedia and Visual Computing Lab United States New York University United States New York University Abu Dhabi United Arab Emirates

Estimating dense correspondences between images is a long-standing image understanding task. Recent works introduce convolutional neural networks (CNNs) to extract high-level feature maps and find correspondences through feature matching. However, high-level feature maps are in low spatial resolution and therefore insufficient to provide accurate and fine-grained features to distinguish intra-class variations for correspondence matching. To address this problem, we generate robust features by dynamically selecting features at different scales. To resolve two critical issues in feature selection, i.e., how many and which scales of features to be selected, we frame the feature selection process as a sequential Markov decision-making process (MDP) and introduce an optimal selection strategy using reinforcement learning (RL). We define an RL environment for image matching in which each individual action either requires new features or terminates the selection episode by referring a matching score. Deep neural networks are incorporated into our method and trained for decision making. Experimental results show that our method achieves comparable/superior performance with state-of-the-art methods on three benchmarks, demonstrating the effectiveness of our feature selection strategy. © 2020. The copyright of this document resides with its authors.

关键词： Reinforcement learning

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Where Is The Emotion? Dissecting A Multi-Gap Network For Image Emotion Classification

Where Is The Emotion? Dissecting A Multi-Gap Network For Ima...

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IEEE International Conference on Image Processing

作者： Lucinda Lim Huai-Qian Khor Phatcharawat Chaemchoy John See Lai-Kuan Wong Visual Processing Lab Faculty of Computing and Informatics Multimedia University Malaysia

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Image emotion recognition has become an increasingly popular research domain in the area of image processing and affective computing. Despite fast-improving classification performance in this task, the understanding and interpretability of its performance are still lacking as there are limited studies on which part of an image would invoke a particular emotion. In this work, we propose a Multi-GAP deep neural network for image emotion classification, which is extensible to accommodate multiple streams of information. We also incorporate feature dependency into our network blocks by adding a bidirectional GRU network to learn transitional features. We report extensive results on the variants of our proposed network and provide valuable perspectives into the class-activated regions via Grad-CAM, and network depth contributions by truncation strategy.

关键词： Feature extraction visualization Neural networks Streaming media Emotion recognition Psychology Robustness

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Local diffusion map signature for symmetry-aware non-rigid shape correspondence 16

Local diffusion map signature for symmetry-aware non-rigid s...

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24th ACM multimedia Conference, MM 2016

作者： Wang, Meng Fang, Yi NYU Multimedia and Visual Computing Lab Dept. of ECE NYU Abu Dhabi United Arab Emirates Dept. of CSE NYU Tandon School of Engineering United States

ISBN: (纸本)9781450336031

Identifying accurate correspondences information among different shapes is of great importance in shape analysis such as shape registration, segmentation and retrieval. This paper aims to develop a paradigm to address the challenging issues posed by shape structural variation and symmetry ambiguity. Specifically, the proposed research developed a novel shape signature based on local diffusion map on 3D surface, which is used to identify the shape correspondence through graph matching process. The developed shape signature, named local diffusion map signature (LDMS), is obtained by projecting heat diffusion distribution on 3D surface into 2D images along the surface normal direction with orientation determined by gradients of heat diffusion field. The local diffusion map signature is able to capture the concise geometric essence that is deformation-insensitive and symmetry-aware. Experimental results on 3D shape correspondence demonstrate the superior performance of our proposed method over other state-of-the-art techniques in identifying correspondences for non-rigid shapes with symmetry ambiguity. © 2016 ACM.

关键词： Diffusion

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