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27th International Conference on Pattern Recognition, ICPR 2024

作者： Hirner, Dominik Fraundorfer, Friedrich Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Cologne Germany

ISBN: (纸本)9783031781919

Stereo estimation has made many advancements in recent years with the introduction of deep-learning. However the traditional supervised approach to deep-learning requires the creation of accurate and plentiful ground-truth data, which is expensive to create and not available in many situations. This is especially true for remote sensing applications, where there is an excess of available data without proper ground truth. To tackle this problem, we propose a self-supervised CNN with self-improving adaptive abilities. In the first iteration, the created disparity map is inaccurate and noisy. Leveraging the left-right consistency check, we get a sparse but more accurate disparity map which is used as an initial pseudo ground-truth. This pseudo ground-truth is then adapted and updated after every epoch in the training step of the network. We use the sum of inconsistent points in order to track the network convergence. The code for our method will be made available after acceptance at https://***/thedodo/SAda-Net. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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A Tangible Interface for Creating Virtual Cutaways in Mixed Reality 21st

A Tangible Interface for Creating Virtual Cutaways in Mixe...

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21st EuroXR International Conference, EuroXR 2024

作者： Li, Xuyu Jalan, Priyansh Dingliana, John Graphics and Vision Research Group School of Computer Science and Statistics Trinity College Dublin Dublin Ireland

ISBN: (纸本)9783031785924

In this paper, we discuss work-in-progress research on tangible interfaces for interactive cutaway visualizations in Mixed Reality (MR). We present an approach that allows users to flexibly and intuitively define virtual cutaway geometry by directly interacting with real-world objects. Using hand movements the user physically traces the shape of the cutaway on an object, without the need for specialized input devices. Tangible interaction allows more accurate interaction and makes it easier for users to plan the placement and shape of required cutaways that fit the object. We developed a prototype demonstrator on the Microsoft HoloLens 2 and present the design and implementation details for such a system and discuss insights, gained from preliminary testing, that motivate compelling directions for future work. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mixed reality

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Anytime Optimal Trajectory Repairing for Autonomous Vehicles

IEEE Open Journal of Intelligent Transportation Systems

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IEEE Open Journal of Intelligent Transportation Systems 2025年 6卷 537-553页

作者： Tong, Kailin Steinberger, Martin Horn, Martin Solmaz, Selim Watzenig, Daniel Virtual Vehicle Research GmbH Graz8010 Austria Graz University of Technology Institute of Automation and Control Graz8010 Austria Graz University of Technology Institute of Computer Graphics and Vision Graz8010 Austria

Adapting to dynamically changing situations remains a pivotal challenge for automated driving systems, which demand robust and efficient solutions. Occasional perception errors inherent in artificial intelligence further complicate the task. Whereas traditional motion planning algorithms address this challenge by replanning the entire trajectory, a significantly more efficient strategy is to repair only the flawed segments. Our paper introduces a groundbreaking approach by formulating an optimal trajectory repairing problem and proposing an innovative and efficient framework for critical timing detection and trajectory repairing. This trajectory repairing specifically employs Bernstein basis polynomials in both 2D distance-time and 3D spatiotemporal spaces. A distinctive feature of our method is the use of an anytime grid search to determine a sub-optimal time-to-repair, which contrasts with previous methods that relied on manually tuned or fixed repair times, limiting both flexibility and robustness. A statistical analysis of 100 scenarios demonstrates that our trajectory-repairing framework outperforms the path-speed decoupled repairing framework in terms of scenario success rate. Furthermore, we introduce a novel algorithm for driving corridor generation that more accurately approximates the collision-free space than state-of-the-art work. The proposed approach has broad potential for application in embedded systems across various autonomous platforms. © 2020 IEEE.

关键词： Accidents

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Bigger Isn’t Always Better: Towards a General Prior for Medical Image Reconstruction

Bigger Isn’t Always Better: Towards a General Prior for M...

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46th Annual Conference of the German Association for Pattern Recognition, DAGM-GCPR 2024

作者： Glaszner, Lukas Zach, Martin Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/II Graz8010 Austria

ISBN: (纸本)9783031851803

Diffusion models have been successfully applied to many inverse problems, including MRI and CT reconstruction. Researchers typically re-purpose models originally designed for unconditional sampling without modifications. Using two different posterior sampling algorithms, we show empirically that such large networks are not necessary. Our smallest model, effectively a ResNet, performs almost as good as an attention U-Net on in-distribution reconstruction, while being significantly more robust towards distribution shifts. Furthermore, we introduce models trained on natural images and demonstrate that they can be used in both MRI and CT reconstruction, out-performing model trained on medical images in out-of-distribution cases. As a result of our findings, we strongly caution against simply re-using very large networks and encourage researchers to adapt the model complexity to the respective task. Moreover, we argue that a key step towards a general diffusion-based prior is training on natural images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Inverse problems

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LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar Segmentation 1st

LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar ...

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1st MICCAI Challenge Comprehensive Analysis and Computing of Real-World Medical Images, CARE 2024 Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Thaler, Franz Štern, Darko Plank, Gernot Urschler, Martin Gottfried Schatz Research Center: Medical Physics and Biophysics Medical University of Graz Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria AVL List GmbH Graz Austria Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria

ISBN: (纸本)9783031870088

Atrial fibrillation (AF) represents the most prevalent type of cardiac arrhythmia for which treatment may require patients to undergo ablation therapy. In this surgery cardiac tissues are locally scarred on purpose to prevent electrical signals from causing arrhythmia. Patient-specific cardiac digital twin models show great potential for personalized ablation therapy, however, they demand accurate semantic segmentation of healthy and scarred tissue typically obtained from late gadolinium enhanced (LGE) magnetic resonance (MR) scans. In this work we propose the Left Atrial Cascading Refinement CNN (LA-CaRe-CNN), which aims to accurately segment the left atrium as well as left atrial scar tissue from LGE MR scans. LA-CaRe-CNN is a 2-stage CNN cascade that is trained end-to-end in 3D, where Stage 1 generates a prediction for the left atrium, which is then refined in Stage 2 in conjunction with the original image information to obtain a prediction for the left atrial scar tissue. To account for domain shift towards domains unknown during training, we employ strong intensity and spatial augmentation to increase the diversity of the training dataset. Our proposed method based on a 5-fold ensemble achieves great segmentation results, namely, 89.21% DSC and 1.6969 mm ASSD for the left atrium, as well as 64.59% DSC and 91.80% G-DSC for the more challenging left atrial scar tissue. Thus, segmentations obtained through LA-CaRe-CNN show great potential for the generation of patient-specific cardiac digital twin models and downstream tasks like personalized targeted ablation therapy to treat AF. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ablation

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Augmentation-Based Domain Generalization and Joint Training from Multiple Source Domains for Whole Heart Segmentation 1st

Augmentation-Based Domain Generalization and Joint Training...

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ISBN: (纸本)9783031870088

As the leading cause of death worldwide, cardiovascular diseases motivate the development of more sophisticated methods to analyze the heart and its substructures from medical images like Computed Tomography (CT) and Magnetic Resonance (MR). Semantic segmentations of important cardiac structures that represent the whole heart are useful to assess patient-specific cardiac morphology and pathology. Furthermore, accurate semantic segmentations can be used to generate cardiac digital twin models which allows e.g. electrophysiological simulation and personalized therapy planning. Even though deep learning-based methods for medical image segmentation achieved great advancements over the last decade, retaining good performance under domain shift - i.e. when training and test data are sampled from different data distributions - remains challenging. In order to perform well on domains known at training-time, we employ a (1) balanced joint training approach that utilizes CT and MR data in equal amounts from different source domains. Further, aiming to alleviate domain shift towards domains only encountered at test-time, we rely on (2) strong intensity and spatial augmentation techniques to greatly diversify the available training data. Our proposed whole heart segmentation method, a 5-fold ensemble with our contributions, achieves the best performance for MR data overall and a performance similar to the best performance for CT data when compared to a model trained solely on CT. With 93.33% DSC and 0.8388 mm ASSD for CT and 89.30% DSC and 1.2411 mm ASSD for MR data, our method demonstrates great potential to efficiently obtain accurate semantic segmentations from which patient-specific cardiac twin models can be generated. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Clustering Single-Cell RNA-seq Data: Impact of Data Binarization on Algorithmic Performance

Clustering Single-Cell RNA-seq Data: Impact of Data Binariza...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Widzisz, Karolina Kania, Mateusz Zyla, Joanna Polański, Andrzej Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice Poland Department of Applied Informatics Silesian University of Technology Akademicka 16 Gliwice Poland Department of Data Science and Engineering Silesian University of Technology Akademicka 16 Gliwice Poland

The primary objective of this study was to test the hypothesis that the binary information on the presence or absence of gene expression can sufficiently capture the inherent heterogeneity within single-cell RNA se quencing (scRNA-seq) data. This hypothesis posits that even without detailed expression levels, valuable insights about cellular diversity can be obtained. Utilizing this method can be particularly advantageous when analyzing large datasets, a common scenario in the field of scRNA-seq. In this paper, we evaluate clustering performance and cluster separability of a variety of model-based algorithms and distance-based methods to an alyze both expression level data and threshold-encoded binarized data. We examined the performance of the Bernoulli-mixture model and Gaussian-mixture model. These were compared against traditional clustering techniques such as hierarchical clustering, K-means, and the Louvain algorithm on a range of scRNA-seq datasets. Our findings reveal that mixture models exhibit a lower dependence on the specific dataset compared to distance-based methods. Mixture models, particularly, demonstrate greater efficacy in accurately estimat ing the number of clusters present within the data. Among analyzed algorithms, the Bernoulli-mixture model stands out, outperforming distance-based approaches in several key aspects. Binary data, presence/absence of gene expression, seem to be indeed adequate to capture the heterogeneity of scRNA-seq data when clus tering with methods specifically designed for binary datasets. The implications of this finding are significant, as it opens up new possibilities for simplifying data analysis in scRNA-seq studies without compromising the accuracy of the results. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Data accuracy

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Fabric Hairiness Analysis for Quality Inspection of Pile Fabric Products Using computer vision Technology

Fabric Hairiness Analysis for Quality Inspection of Pile Fab...

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2022 International Conference on Industry Sciences and computer Science Innovation, iSCSi 2022

作者： Khanal, Salik Ram Silva, Jorge Gonzalez, Dibet Garcia Castella, Chavez Perez, Jose Ruben Pozo Ferreira, Manuel J. Neadvance Machine Vision SA Braga Portugal Center for Computer Graphics Guimaraes Portugal

The textile industry is one of the important industries which can enhance their productivity using Artificial Intelligence technology. Most of the textile industries inspect the product manually, however, the development of AI-driven technology pushes manufacturing industries into a new era. In this paper, a computer vision-based approach for hairiness analysis in pile textile is proposed. The proposed system detects and highlights the hairy area of the fabric where the images were acquired by using an RGB camera. The experimental images were collected in our laboratory using a stereo ZED camera, but only the RGB images were processed to detect the hairiness of the fabric. The proposed hardware architecture consists mainly of camera, processing device, robotics, camera control system, while the software prototype consists of image processing techniques. In software architecture, image acquisition systems, image pre-processing, image classification, and image visualization are the major steps. A CNN-based deep learning architecture was used to classify the hairier textile and the test was carried out in big images using sliding window techniques. The classification accuracy was 99% during the training and test accuracy was 92%. © 2022 Elsevier B.V.. All rights reserved.

关键词： Textile industry

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Intraoperative perfusion assessment by continuous, low-latency hyperspectral light-field imaging: development, methodology, and clinical application

Intraoperative perfusion assessment by continuous, low-laten...

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Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXIII 2025

作者： Kray, Stefan Schmid, Andreas Wisotzky, Eric L. Gerlich, Moritz Apweiler, Sebastian Hilsmann, Anna Greiner, Thomas Eisert, Peter Kneist, Werner Institute of Smart Systems and Services Pforzheim University Pforzheim Germany Computer Vision & Graphics Vision & Imaging Technologies Fraunhofer Heinrich-Hertz-Institute HHI Berlin Germany Humboldt-University Berlin Germany Department of General- Visceral- and Thoracic Surgery Klinikum Darmstadt GmbH Darmstadt Germany

ISBN: (纸本)9781510683600

Accurate assessment of tissue perfusion is crucial in visceral surgery, especially during anastomosis. Currently, subjective visual judgment is commonly employed in clinical settings. Hyperspectral imaging (HSI) offers a non-invasive, quantitative alternative. However, HSI imaging lacks continuous integration into the clinical workflow. This study presents a hyperspectral light field system for intraoperative tissue oxygen saturation (SO2) analysis and visualization. We present a correlation method for determining SO2 saturation with low computational demands. We demonstrate clinical application, with our results aligning with the perfusion boundaries determined by the surgeon. We perform and compare continuous perfusion analysis using two hyperspectral cameras (Cubert S5, Cubert X20), achieving processing times of © 2025 SPIE.

关键词： Cameras Surgery Oxygenation Hyperspectral imaging Tissues Reflectivity Resection Perfusion imaging Visualization Image segmentation

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Evaluation of convex optimization techniques for the weighted graph-matching problem in computer vision

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23rd German Association for Pattern Recognition Symposium, DAGM 2001

作者： Schellewald, Christian Roth, Stefan Schnörr, Christoph Computer Vision Graphics and Pattern Recognition Group Dept Mathematics and Computer Science University of Mannheim MannheimD-68131 Germany

ISBN: (纸本)3540425969

We present a novel approach to the weighted graph-matching problem in computer vision, based on a convex relaxation of the underlying combinatorial optimization problem. The approach always computes a lower bound of the objective function, which is a favorable property in the context of exact search algorithms. Furthermore, no tuning parameters have to be selected by the user, due to the convexity of the relaxed problem formulation. For comparison, we implemented a recently published deterministic annealing approach and conducted numerous experiments for both established benchmark experiments from combinatorial mathematics, and for random ground-truth experiments using computer-generated graphs. Our results show similar performance for both approaches. In contrast to the convex approach, however, four parameters have to be determined by hand for the annealing algorithm to become competitive. © Springer-Verlag Berlin Heidelberg 2001.

关键词： computer vision

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