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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Frisky, Aufaclav Zatu Kusuma Putranto, Andi Zambanini, Sebastian Sablatnig, Robert Computer Vision Lab Institute of Visual Computing and Human-Centered Technology Faculty of Informatics TU Wien Vienna Austria Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia Department of Archaeology Faculty of Cultural Science Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9783030687861

Single image depth prediction is considerably difficult since depth cannot be estimated from pixel correspondences. Thus, prior knowledge, such as registered pixel and depth information from the user is required. Another problem rises when targeting a specific domain requirement as the number of freely available training datasets is limited. Due to color problem in relief images, we present a new outdoor Registered Relief Depth (RRD) Prambanan dataset, consisting of outdoor images of Prambanan temple relief with registered depth information supervised by archaeologists and computer scientists. In order to solve the problem, we also propose a new depth predictor, called Multi-Color Cascade Network (MCCNet), with weight transfer. Applied on the new RRD Prambanan dataset, our method performs better in different materials than the baseline with 2.53 mm RMSE. In the NYU Depth V2 dataset, our method’s performance is better than the baselines and in line with other state-of-the-art works. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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INTERPRETABLE EMBEDDINGS FOR SEGMENTATION-FREE SINGLE-CELL ANALYSIS IN MULTIPLEX IMAGING

arXiv

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arXiv 2024年

作者： Gutwein, Simon Lazic, Daria Walter, Thomas Taschner-Mandl, Sabine Licandro, Roxane St. Anna Children’s Cancer Research Institute Vienna Austria TU Wien Institute of Visual Computing and Human-Centered Technology CVL Vienna Austria Medical University of Vienna Biomedical Imaging and Image-guided Therapy CIR Vienna Austria Mines Paris PSL University Paris75006 France Institut Curie ParisCedex 75248 France INSERM U900 ParisCedex 75248 France

Multiplex Imaging (MI) enables the simultaneous visualization of multiple biological markers in separate imaging channels at subcellular resolution, providing valuable insights into cell-type heterogeneity and spatial organization. However, current computational pipelines rely on cell segmentation algorithms, which require laborious fine-tuning and can introduce downstream errors due to inaccurate single-cell representations. We propose a segmentation-free deep learning approach that leverages grouped convolutions to learn interpretable embedded features from each imaging channel, enabling robust cell-type identification without manual feature selection. Validated on an Imaging Mass Cytometry dataset of 1.8 million cells from neuroblastoma patients, our method enables the accurate identification of known cell types, showcasing its scalability and suitability for high-dimensional MI data. © 2024, CC BY-NC-ND.

关键词： Feature Selection

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Cost Volume Refinement for Depth Prediction

Cost Volume Refinement for Depth Prediction

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International Conference on Pattern Recognition

作者： João L. Cardoso Nuno Gonçalves Michael Wimmer Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria Institute of Systems and Robotics University of Coimbra Portuguese Mint and Official Printing Office Lisbon

Light-field cameras are becoming more popular in the consumer market. Their data redundancy allows, in theory, to accurately refocus images after acquisition and to predict the depth of each point visible from the camera. Combined, these two features allow for the generation of full-focus images, which is impossible in traditional cameras. Multiple methods for depth prediction from light fields (or stereo) have been proposed over the years. A large subset of these methods relies on cost-volume estimates - 3D objects where each layer represents a heuristic of whether each point in the image is at a certain distance from the camera. Generally, this volume is used to regress a depth map, which is then refined for better results. In this paper, we argue that refining the cost volumes is superior to refining the depth maps in order to further increase the accuracy of depth predictions. We propose a set of cost-volume refinement algorithms and show their effectiveness.

关键词： Three-dimensional displays Smoothing methods Volume measurement Refining Redundancy Pipelines Cameras

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visual Parameter Selection for Spatial Blind Source Separation

arXiv

引用

arXiv 2021年

作者： Piccolotto, N. Bögl, M. Muehlmann, C. Nordhausen, K. Filzmoser, P. Miksch, S. TU Wien Institute of Visual Computing and Human-Centered Technology Austria TU Wien Institute of Statistics and Mathematical Methods in Economics Austria University of Jyväskylä Finland

Analysis of spatial multivariate data, i.e., measurements at irregularly-spaced locations, is a challenging topic in visualization and statistics alike. Such data are integral to many domains, e.g., indicators of valuable minerals are measured for mine prospecting. Popular analysis methods, like PCA, often by design do not account for the spatial nature of the data. Thus they, together with their spatial variants, must be employed very carefully. Clearly, it is preferable to use methods that were specifically designed for such data, like spatial blind source separation (SBSS). However, SBSS requires two tuning parameters, which are themselves complex spatial objects. Setting these parameters involves navigating two large and interdependent parameter spaces, while also taking into account prior knowledge of the physical reality represented by the data. To support analysts in this process, we developed a visual analytics prototype. We evaluated it with experts in visualization, SBSS, and geochemistry. Our evaluations show that our interactive prototype allows to define complex and realistic parameter settings efficiently, which was so far impractical. Settings identified by a non-expert led to remarkable and surprising insights for a domain expert. Therefore, this paper presents important first steps to enable the use of a promising analysis method for spatial multivariate data. Copyright © 2021, The Authors. All rights reserved.

关键词： visualization

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"The support needs to be part of the system": designing inclusive eHealth applications for older adults with low eHealth literacy

"The support needs to be part of the system": designing incl...

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2022 Engaging Citizen Science Conference, CitSci 2022

作者： Prinzellner, Yvonne Simon, Ali Drachmann, Danielle Werner, Katharina Münter, Lars Bulsink, Vera Smits, Carolien Fitzpatrick, Geraldine Schwaninger, Isabel Johanniter Österreich Ausbildung und Forschung Gemeinnützige GmbH Ignaz-Köck-Straße 22 Vienna1210 Austria Danish Committee for Health Education Classensgade 71 5. Sal Copenhagen2100 Denmark TU Wien Institute of Visual Computing and Human-Centered Technology Human Computer Interaction Group Argentinierstraße 8 Vienna1040 Austria Roessingh Research and Development Roessinghsbleekweg 33b Enschede7522 Netherlands Arthur van Schendelstraat 600 Utrecht3511 MJ Netherlands

In the project "Got-IT", an online toolkit was developed to assist with the design of inclusive eHealth solutions targeting the promotion of healthy lifestyles among older adults (65+) with low eHealth literacy. The project consortium has used participatory citizen science as an approach to actively engage with the relevant stakeholders, including older adults with low eHealth literacy, eHealth developers, and secondary end users like eHealth professionals. We applied three different research designs, all centered on the active engagement of the different stakeholders, using the design of an eHealth app to ground the investigations. The outcomes of the Got-IT project align not only with the first objective of the AAL Programme ("better quality of life for older adults and their networks, by empowering the citizens to take care of their own health") but are also central components of citizen science as a whole, by actively engaging the different target groups at various levels of our research process. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

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Video shot analysis for digital curation and preservation of historical films

Video shot analysis for digital curation and preservation of...

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2019 Eurographics Workshop on Graphics and Cultural Heritage, GCH 2019

作者： Helm, D. Kampel, M. TU Wien Institute for Visual Computing and Human-Centered Technology Austria

ISBN: (纸本)9783038680826

In automatic video analysis and film preservation, Shot Boundary Detection (SBD) and Shot Type Classification (STC) are fundamental pre-processing steps. While previous research focuses on detecting and classifying shots in different video genres such as sports movies, documentaries or news clips only few studies investigate on SBD and STC in historical footage. In order to promote research on automatic video analysis the project visual History of the Holocaust (VHH) has been started in January 2019. The main aim of this paper is to present first results on the fundamental topics SBD and STC in the context of the project VHH. Therefore, a deep learning-based SBD approach is implemented to detect Abrupt Transitions (ATs). Furthermore, a CNN-based algorithm is analyzed and optimized in order to classify shots into the four categories: Extreme-Long-Shot (ELS), Long-Shot (LS), Medium-Shot (MS) and Close-Up (CU). Finally, both algorithms are evaluated on a self-generated historical dataset related to the National Socialism and the Holocaust. The outcome of this paper demonstrates a first quantitative evaluation of the SBD approach and displays a F1;Score of 0.866 without the need of any re-training or optimization. Moreover, the proposed STC algorithm reaches an accuracy of 0.71 on classifying shots. This paper contributes a significant base for future research on automatic shot analysis related to the project VHH. © 2019 Eurographics Association.

关键词： Deep learning

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Lens calibration for focus shift correction in close-range multispectral imaging

Lens calibration for focus shift correction in close-range m...

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2019 Eurographics Workshop on Graphics and Cultural Heritage, GCH 2019

作者： Brenner, S. Sablatnig, R. TU Wien Institute of Visual Computing and Human-Centered Technology Austria

ISBN: (纸本)9783038680826

Multispectral imaging has become a popular tool to reveal properties and structures in cultural heritage objects that are hidden to the human observer. One of the inherent problems of multispectral imaging applications is chromatic aberration. Due to an extended spectral range, the effect appears more pronounced than in conventional photography in the visible spectrum. This paper is concerned with longitudinal chromatic aberrations, i.e. shifts of the focal plane along the principal axis of the camera, as they are hard to correct in post-processing and should be avoided during acquisition. To this end, a calibration scheme to measure the wavelength- and distance-dependent focal shift behavior of a given camera/lens system is proposed, which allows for a mechanical compensation at acquisition time. The approach is demonstrated on a multispectral imaging system for historical manuscript analysis. We show that the application of this compensation approach enables the acquisition of in-focus images in non-visible wavelengths using a lens optimized for the visible spectrum only. © 2019 Eurographics Association.

关键词： Aberrations

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HAAQI-Net: A Non-Intrusive Neural Music Audio Quality Assessment Model for Hearing Aids

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1877-1892页

作者： Dyah A. M. G. Wisnu Stefano Rini Ryandhimas E. Zezario Hsin-Min Wang Yu Tsao Taiwan International Graduate Program—Social Network and Human-Centered Computing Institute of Information Science Academia Sinica Taipei Taiwan College of Informatics National Chengchi University Taipei Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Electrical Engineering Chung Yuan Christian University Taiwan

This paper introduces HAAQI-Net, a non-intrusive music audio quality assessment model for hearing aid users. Unlike traditional methods such as Hearing Aid Audio Quality Index (HAAQI), which requires intrusive reference signal comparisons, HAAQI-Net offers a more accessible and computationally efficient alternative. Leveraging a bidirectional long short-term memory architecture with attention mechanisms and features extracted from a pre-trained BEATs model, it can predict HAAQI scores directly from music audio clips and hearing loss patterns. The experimental results demonstrate that, compared to the traditional HAAQI as the reference, HAAQI-Net achieves a linear correlation coefficient (LCC) of 0.9368, a Spearman's rank correlation coefficient (SRCC) of 0.9486, and a mean squared error (MSE) of 0.0064, while significantly reducing the inference time from 62.52 seconds to 2.54 seconds. Furthermore, a knowledge distillation strategy was applied, reducing the parameters by 75.85% and inference time by 96.46%, while maintaining strong performance (LCC: 0.9071, SRCC: 0.9307, MSE: 0.0091). To expand its capabilities, HAAQI-Net was adapted to predict subjective human scores, mean opinion score (MOS), by fine-tuning. This adaptation significantly improved the prediction accuracy. Furthermore, the robustness of HAAQI-Net was evaluated under varying sound pressure level (SPL) conditions, revealing optimal performance at a reference SPL of 65 dB, with the accuracy gradually decreasing as SPL deviated from this point. The advancements in subjective score prediction, SPL robustness, and computational efficiency position HAAQI-Net as a reliable solution for music audio quality assessment, significantly contributing to the development of efficient and accurate models in audio signal processing and hearing aid technology.

关键词： Hearing aids Quality assessment Training Feature extraction Speech processing Auditory system Computational modeling Adaptation models Transformers Multiple signal classification

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TBSSvis:visual analytics for Temporal Blind Source Separation

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visual Informatics 2022年第4期6卷 51-66页

作者： Nikolaus Piccolotto Markus Bögl Theresia Gschwandtner Christoph Muehlmann Klaus Nordhausen Peter Filzmoser Silvia Miksch TU Wien Institute of Visual Computing&Human-Centered TechnologyFavoritenstrasse 9–11A-1040 ViennaAustria Erste Group Bank AG Am Belvedere 1A-1100 ViennaAustria TU Wien Institute of Statistics and Mathematical Methods in EconomicsWiedner Hauptstrasse 8–10A-1040 ViennaAustria University of Jyväskylä Department of Mathematics and StatisticsFI40014 JyväskyläFinland

Temporal Blind Source Separation(TBSS)is used to obtain the true underlying processes from noisy temporal multivariate data,such as *** has similarities to Principal Component Analysis(PCA)as it separates the input data into univariate components and is applicable to suitable datasets from various domains,such as medicine,finance,or civil *** TBSS’s broad applicability,the involved tasks are not well supported in current tools,which offer only text-based interactions and single static *** are limited in analyzing and comparing obtained results,which consist of diverse data such as matrices and sets of time ***,parameter settings have a big impact on separation performance,but as a consequence of improper tooling,analysts currently do not consider the whole parameter *** propose to solve these problems by applying visual analytics(VA)*** primary contribution is a design study for TBSS,which so far has not been explored by the visualization *** developed a task abstraction and visualization design in a user-centered design ***-specific assembling of well-established visualization techniques and algorithms to gain insights in the TBSS processes is our secondary *** present TBSSvis,an interactive web-based VA prototype,which we evaluated extensively in two interviews with five TBSS *** and observations from these interviews show that TBSSvis supports the actual workflow and combination of interactive visualizations that facilitate the tasks involved in analyzing TBSS results.

关键词： Blind source separation Ensemble visualization visual analytics Parameter space exploration

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Lookahead Exploration with Neural Radiance Representation for Continuous Vision-Language Navigation

arXiv

引用

arXiv 2024年

作者： Wang, Zihan Li, Xiangyang Yang, Jiahao Liu, Yeqi Hu, Junjie Jiang, Ming Jiang, Shuqiang Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Department of Computer Science University of Wisconsin MadisonWI United States Department of Biostatistics and Medical Informatics University of Wisconsin MadisonWI United States Department of Human-centered Computing Indiana University IndianapolisIN United States

Vision-and-language navigation (VLN) enables the agent to navigate to a remote location following the natural language instruction in 3D environments. At each navigation step, the agent selects from possible candidate locations and then makes the move. For better navigation planning, the lookahead exploration strategy aims to effectively evaluate the agent’s next action by accurately anticipating the future environment of candidate locations. To this end, some existing works predict RGB images for future environments, while this strategy suffers from image distortion and high computational cost. To address these issues, we propose the pre-trained hierarchical neural radiance representation model (HNR) to produce multi-level semantic features for future environments, which are more robust and efficient than pixel-wise RGB reconstruction. Furthermore, with the predicted future environmental representations, our lookahead VLN model is able to construct the navigable future path tree and select the optimal path via efficient parallel evaluation. Extensive experiments on the VLN-CE datasets confirm the effectiveness of our method. The code is available at https://***/MrZihan/HNR-VLN Copyright © 2024, The Authors. All rights reserved.

关键词： Navigation

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