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Quantum Image visualizer: visual Debugging of Quantum Image Processing Circuits

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arXiv 2025年

作者： Heim, Anja Lang, Thomas Gall, Alexander Gröller, Eduard Heinzl, Christoph Fraunhofer Institute of Integrated Circuits IIS Division Development Center X-ray Technology Germany University of Passau Faculty of Computer Science and Mathematics Germany TU Wien Institute of Visual Computing & Human-Centered Technology VRVis Research Center Austria

Quantum computing is an emerging field that utilizes the unique principles of quantum mechanics to offer significant advantages in algorithm execution over classical approaches. This potential is particularly promising in the domain of quantum image processing, which aims to manipulate all pixels simultaneously. However, the process of designing and verifying these algorithms remains a complex and error-prone task. To address this challenge, new methods are needed to support effective debugging of quantum circuits. The Quantum Image visualizer is an interactive visual analysis tool that allows for the examination of quantum images and their transformation throughout quantum circuits. The framework incorporates two overview visualizations that trace image evolution across a sequence of gates based on the most probable outcomes. Interactive exploration allows users to focus on relevant gates, and select pixels of interest. Upon selection, detailed visualizations enable in-depth inspection of individual pixels and their probability distributions, revealing how specific gates influence the likelihood of pixel color values and the magnitude of these changes. An evaluation of Quantum Image visualizer was conducted through in-depth interviews with eight domain experts. The findings demonstrate the effectiveness and practical value of our approach in supporting visual debugging of quantum image processing circuits. © 2025, CC BY-NC-SA.

关键词： Distribution functions

On Learning Vehicle Detection in Satellite Video

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arXiv 2020年

作者： Pflugfelder, Roman Weissenfeld, Axel Wagner, Julian AIT Austrian Institute of Technology Center for Digital Safety & Security TU Wien Institute of Visual Computing & Human-Centered Technology

Vehicle detection in aerial and satellite images is still challenging due to their tiny appearance in pixels compared to the overall size of remote sensing imagery. Classical methods of object detection very often fail in this scenario due to violation of implicit assumptions made such as rich texture, small to moderate ratios between image size and object size. Satellite video is a very new modality which introduces temporal consistency as inductive bias. Approaches for vehicle detection in satellite video use either background subtraction, frame differencing or subspace methods showing moderate performance (0.26 - 0.82 F1 score). This work proposes to apply recent work on deep learning for wide-area motion imagery (WAMI) on satellite video. We show in a first approach comparable results (0.84 F1) on Planet’s SkySat-1 LasVegas video with room for further improvement. Copyright © 2020, The Authors. All rights reserved.

关键词： Textures

Teachers’ Disappointment: Theoretical Perspective on the Inclusion of Ambivalent Emotions in human-Robot Interactions in Education

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Teachers’ Disappointment: Theoretical Perspective on the In...

ACM/IEEE International Conference on human-Robot Interaction (HRI)

作者： Anna Dobrosovestnova Glenda Hannibal The Austrian Institute for Artificial Intelligence (OFAI) Vienna Austria Human Computer Interaction Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria

Following affective turn in cognitive science, recent decades have witnessed an increasing interest toward the role of emotions in education. Ample evidence suggests that learners and teachers experience a variety of emotions, ranging from joy and pride to anger and frustration. However, when it comes to the design of affective behavior in robotic systems for education purposes, the emphasis has been predominantly on communication of positive emotions. While we recognize that positive emotions are fundamental to successful learning, in this paper we wish to make the case for the consideration of ambivalent emotions for the design of social robots for tutoring. To ground this proposal, we focus on the emotion of teachers’ disappointment. First, we discuss under which conditions communicated teachers’ disappointment, while it may be experienced as emotionally ambivalent by teachers and students, functions as an affiliating pedagogical strategy. We proceed to sketch out the methodological suggestions we consider relevant for future studies of communicated disappointment in human-robot interactions within learning contexts. We conclude with critical reflections about the ethics of responsible designs of such studies. CCS Concepts • Applied computing $\rightarrow$ Education; • human-centered computing $\rightarrow$ Interaction design theory, concepts and paradigms. ACM Reference Format: Anna Dobrosovestnova and Glenda Hannibal. 2020. Teachers’ Disappointment: Theoretical Perspective on the Inclusion of Ambivalent Emotions in human-Robot Interactions in Education. In Proceedings of the 2020 ACM/IEEE International Conference on human-Robot Interaction (HRI ’20), March 23–26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 10 pages. https://***/10.1145/3319502.3374816

关键词： human computer interaction Ethics Education Laboratories human-robot interaction Reflection Appraisal

Semi-automatic post-processing of multi-view 2D-plus-depth video 30

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Semi-automatic post-processing of multi-view 2D-plus-depth v...

30th Stereoscopic Displays and Applications Conference, SD and A 2019

作者： Sespede, Braulio Seitner, Florian Gelautz, Margrit Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria Emotion3D GmbH Vienna Austria

We propose a post-processing framework based on multi-view interactive video segmentation for correcting 2D-plus-depth video footage. The suggested approach uses user-made scribbles to guide the multi-view segmentation process, which is based on an efficient cost-volume filtering algorithm. We extend the 2D algorithm to 3D and propose several improvements that increase precision and recall while also decreasing the need for user input. Our semi-automatic approach is supported by an interactive visualization tool that integrates both 2D and 3D views of the footage, allowing the user to explore novel views coherently and grasp a better understanding of the underlying data. We integrate our post-processing framework into a workflow for generating dynamic meshes from footage recorded by multiple stereo cameras, demonstrating the applicability of the technique. © 2019, Society for Imaging Science and technology.

关键词： Stereo image processing

2D points curve reconstruction survey and benchmark

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arXiv 2021年

作者： Ohrhallinger, Stefan Peethambaran, Jiju Parakkat, Amal D. Dey, Tamal K. Muthuganapathy, Ramanathan Institute of Visual Computing and Human-Centered Technology TU Wien Austria Department of Math and Computing Science Saint Mary's University Halifax Canada Department of Computer Science and Engineering Indian Institute of Technology Guwahati India Department of Computer Science Purdue University Indiana United States Department of Engineering Design Indian Institute of Technology Madras India

Curve reconstruction from unstructured points in a plane is a fundamental problem with many applications that has generated research interest for decades. Involved aspects like handling open, sharp, multiple and non-manifold outlines, run-time and provability as well as potential extension to 3D for surface reconstruction have led to many different algorithms. We survey the literature on 2D curve reconstruction and then present an open-sourced benchmark for the experimental study. Our unprecedented evaluation on a selected set of planar curve reconstruction algorithms aims to give an overview of both quantitative analysis and qualitative aspects for helping users to select the right algorithm for specific problems in the field. Our benchmark framework is available online to permit reproducing the results, and easy integration of new algorithms. © 2021, CC BY-NC-SA.

关键词： Surveys

Person Localisation under Fragmented Occlusion*

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Person Localisation under Fragmented Occlusion*

IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Roman Pflugfelder Jonas Auer Center of Digital Safety & Security Giefinggasse 4 AIT Austrian Institute of Technology Vienna Austria TU Wien Inst. of Visual Comp. & Human-Centered Technology Favoritenstr. 9/193-1 Vienna Austria

ISBN: (纸本)9781665433976

Occlusion is a fundamental challenge in object recognition. Fragmented occlusion is much more challenging than ordinary partial occlusion and occurs in natural environments such as forests. Less is known in computer vision about fragmented occlusion and object recognition. Interestingly, human vision has far more explored this problem as the human visual system evolved to fragmented occlusion at the times when mankind occupied rainforest. A motivating example of fragmented occlusion is object detection through foliage which is an essential requirement in green border surveillance. Instead of detection, this paper studies the simpler problem of localisation with persons. A neural network based method shows a precision larger than 90% on new image sequences capturing the problem. This is possible by two observations: (i) fragmented occlusion is unsolvable in single images without temporal information, and (ii) colour quantisation and colour swapping is essential to force the training of the network to learn from the available temporal information in the spatiotemporal data.

关键词： Training visualization Image color analysis Surveillance Vegetation mapping Detectors visual systems

Machine Learning and Computer Vision Techniques in Continuous Beehive Monitoring Applications: A Survey

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arXiv 2022年

作者： Bilik, Simon Zemcik, Tomas Kratochvila, Lukas Ricanek, Dominik Richter, Miloslav Zambanini, Sebastian Horak, Karel Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 Brno61600 Czech Republic Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Yliopistonkatu 34 Lappeenranta53850 Finland Computer Vision Lab Institute of Visual Computing & Human-Centered Technology Faculty of Informatics TU Wien Favoritenstr. 9/193-1 ViennaA-1040 Austria

Wide use and availability of machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new applications appears also in biology and agriculture, where they may be used to detect infections, parasites and weeds, but also for automated monitoring and early warning systems. This goes in concordance with the introduction of the easily accessible hardware and development kits such as the Arduino, or RaspberryPi families. In this paper, we survey 50 papers focusing on the methods of automated beehive monitoring using computer vision techniques. Particularly on the pollen and Varroa mite detection together with the bee traffic monitoring. Such systems could also be used for monitoring of honeybee colonies and for the inspection of their health state, which could potentially identify dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Further on, we also include analysis of the research trends in this application field and we outline the possible directions of new development. Our paper is also aimed at veterinary and apidology professionals and experts, who may not be familiar with machine learning to introduce them to its capabilities, hence each family of techniques is prefaced by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring. © 2022, CC BY.

关键词： Computer vision

Precomputed fast rejection ray-triangle intersection

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Graphics and visual computing 2022年 6卷

作者： Thomas Alois Pichler Andrej Ferko Michal Ferko Peter Kán Hannes Kaufmann Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria Department of Algebra and Geometry Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Bratislava Slovakia

We propose a ray-triangle intersection algorithm with fast-rejection strategies. We intersect the ray with the triangle plane, then transform the intersection problem into 2D by applying a transformation matrix to the ray-plane intersection point. For 2D transformation, we study two different approaches. The first approach uses a transformation matrix which transforms the triangle into a unit triangle. Then, simple 2D tests are performed. The second approach transforms the triangle into a 2D triangle while preserving similarity. This allows us to prune (i.e., to clip away) areas surrounding the triangle, determining whether the transformed intersection point lies within the triangle. We discuss several optimizations for this pruning approach. We implemented both approaches into the CPU-based ray-tracing framework PBRT, version 3, and we performed a time-based comparison against PBRT’s default intersection algorithm and Baldwin and Weber’s algorithm. The results show that our algorithms are faster than the default algorithm. They are comparable to or slightly slower than Baldwin and Weber’s algorithm, however, the pruning approach produces watertight results and may be further optimized. Moreover, additional CPU/GPU experiments outside of PBRT document promising speedup over the standard Möller–Trumbore algorithm in areas like ray-casting or collision detection.

关键词： Ray-Triangle Intersection Ray shooting Collision Point location Intersections

Scalable Class-Centric visual Interactive Labeling

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Computers & Graphics 2025年 129卷

作者： Matthias Matt Jana Sedlakova Jürgen Bernard Matthias Zeppelzauer Manuela Waldner Institute of Visual Computing & Human-Centered Technology TU Wien Vienna 1040 Austria Department of Informatics University of Zurich Zurich 8006 Switzerland Digital Society Initiative University of Zurich Zurich 8006 Switzerland Institute of Creative Media Technologies St. Pölten University of Applied Sciences St. Pölten 3100 Austria

Large unlabeled datasets demand efficient and scalable data labeling solutions, in particular when the number of instances and classes is large. This leads to significant visual scalability challenges and imposes a high cognitive load on the users. Traditional instance-centric labeling methods, where (single) instances are labeled in each iteration struggle to scale effectively in these scenarios. To address these challenges, we introduce cVIL, a Class-Centric visual Interactive Labeling methodology designed for interactive visual data labeling. By shifting the paradigm from assigning-classes-to-instances to assigning-instances-to-classes , cVIL reduces labeling effort and enhances efficiency for annotators working with large, complex and class-rich datasets. We propose a novel visual analytics labeling interface built on top of the conceptual cVIL workflow, enabling improved scalability over traditional visual labeling. In a user study, we demonstrate that cVIL can improve labeling efficiency and user satisfaction over instance-centric interfaces. The effectiveness of cVIL is further demonstrated through a usage scenario, showcasing its potential to alleviate cognitive load and support experts in managing extensive labeling tasks efficiently.

关键词： visual analytics visual-interactive data labeling Class-centric labeling Property measures Interactive machine learning