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arXiv 2024年

作者： Stekovic, Sinisa Ainetter, Stefan D’Urso, Mattia Fraundorfer, Friedrich Lepetit, Vincent Inst. for Computer Graphics and Vision Graz Univ. of Technology Graz Austria LIGM École des Ponts Univ Gustave Eiffel CNRS Marne-la-Vallée France

We propose PyTorchGeoNodes, a differentiable module for reconstructing 3D objects from images using interpretable shape programs. In comparison to traditional CAD model retrieval methods, the use of shape programs for 3D reconstruction allows for reasoning about the semantic properties of reconstructed objects, editing, low memory footprint, etc. However, the utilization of shape programs for 3D scene understanding has been largely neglected in past works. As our main contribution, we enable gradient-based optimization by introducing a module that translates shape programs designed in Blender, for example, into efficient PyTorch code. We also provide a method that relies on PyTorchGeoNodes and is inspired by Monte Carlo Tree Search (MCTS) to jointly optimize discrete and continuous parameters of shape programs and reconstruct 3D objects for input scenes. In our experiments, we apply our algorithm to reconstruct 3D objects in the ScanNet dataset and evaluate our results against CAD model retrieval-based reconstructions. Our experiments indicate that our reconstructions match well the input scenes while enabling semantic reasoning about reconstructed objects. © 2024, CC BY.

关键词： Semantics

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Depth-guided Robust Face Morphing Attack Detection

Depth-guided Robust Face Morphing Attack Detection

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Harsh Rachalwar Meiling Fang Naser Damer Abhijit Das Birla Institute of Technology and Science Pilani (BITS Pilani) Secunderabad Telangana India Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Recently, morphing attack detection (MAD) solutions have achieved remarkable success with the aid of deep learning techniques. Despite the good performance achieved by binary label or binary pixel-wise supervised MAD models, the robustness of such models drops when facing variations in morphing attacks. In this work, we propose a novel process that leverages facial depth information to build a robust and generalized MAD. The depth map, representing the 3D shape of the face in a 2D image, is more informative compared to binary and binary pixel-wise map labels. To validate the idea we synthetically generated 3D depth map ground truth. Furthermore, we introduce a novel MAD architecture designed to capture subtle information from the 3D depth data. In addition, we analyze the training loss formulation to further enhance the MAD performance. Driven by the need for developing MAD solutions while preserving the privacy of individuals for legal and ethical reasons, we conduct our experiments on privacy-friendly synthetic training data and authentic evaluation data. The experimental results on existing public datasets in SYN-MAD 22 competition demonstrate the effectiveness of our proposed solution in terms of both robustness and generalization.

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Product of Gaussian Mixture Diffusion Models

arXiv

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arXiv 2023年

作者： Zach, Martin Kobler, Erich Chambolle, Antonin Pock, Thomas Institute of Computer Graphics and Vision Graz University of Technology Austria Klinik für Neuroradiologie Universitätsklinikum Bonn Germany and Mokaplan INRIA Paris France

In this work we tackle the problem of estimating the density fX of a random variable X by successive smoothing, such that the smoothed random variable Y fulfills the diffusion partial differential equation (∂t − ∆1)fY (·, t) = 0 with initial condition fY (·, 0) = fX. We propose a product-of-experts-type model utilizing Gaussian mixture experts and study configurations that admit an analytic expression for fY (·, t). In particular, with a focus on image processing, we derive conditions for models acting on filter-, wavelet-, and shearlet-responses. Our construction naturally allows the model to be trained simultaneously over the entire diffusion horizon using empirical Bayes. We show numerical results for image denoising where our models are competitive while being tractable, interpretable, and having only a small number of learnable parameters. As a byproduct, our models can be used for reliable noise level estimation, allowing blind denoising of images corrupted by heteroscedastic noise. © 2023, CC BY.

关键词： Random variables

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Surgical Phase Recognition for different hospitals

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Current Directions in Biomedical Engineering 2023年第1期9卷 315-318页

作者： Wisotzky, Eric L. Beckmann, Sophie Eisert, Peter Renz-Kiefel, Lasse Hilsmann, Anna Lünse, Sebastian Mantke, René Fraunhofer Heinrich-Hertz-Institute HHI & Humboldt-University Berlin Germany Computer Vision & Graphics Fraunhofer Heinrich-Hertz-Institute HHI Berlin Germany Brandenburg Medical School Department of Surgery University Hospital Brandenburg Germany Faculty of Health Sciences Joint Faculty of the Brandenburg University of Technology Cottbus - Senftenberg Brandenburg Medical School Theodor Fontane Neuruppin Germany University of Potsdam & Department of Surgery University Hospital Brandenburg Germany

Surgical phase recognition is an important aspect of surgical workflow analysis, as it allows an automatic analysis of the performance and efficiency of surgical procedures. A big challenge for training a neural network for surgical phase recognition is the availability of training data and the large (visual) variability in procedures of different surgeons. Hence, a network must be able to generalize to new data. In this paper, we present an adaptation of a Temporal Convolutional Network for surgical phase recognition in order to ensure the generalization of the network to new scenes with different conditions on the example of cholecystectomy. We used publicly available datasets of 104 surgeries from four different centers for training. The results showed that the network was able to generalize to new scenes and we obtained recognition results with accuracy up to 82% on our own six captured surgeries, performed in a different hospital. This performance is similar for test data from the hospitals of the training data, suggesting that the network can well generalize to new surgical rooms and surgeons. The findings have important implications for the development of automated surgical decision support systems that can be applied in a variety of real-world surgical settings. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Hospitals

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Automated 3D mass digitization for the GLAM sector

Automated 3D mass digitization for the GLAM sector

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Archiving 2020 Online: Digitization, Preservation, and Access

作者： Santos, Pedro Tausch, Reimar Domajnko, Matevz Ritz, Martin Knuth, Martin Fellner, Dieter Fraunhofer Institute for Computer Graphics Research TU-Darmstadt Germany Graz University of Technology Institute of Computer Graphics and Knowledge Visualization Austria

The European Cultural Heritage Strategy for the 21st century has led to an increased demand for fast, efficient and faithful 3D digitization technologies for cultural heritage artefacts. Yet, unlike the digital acquisition of cultural goods in 2D which is widely used and automated today, 3D digitization often still requires significant manual intervention, time and money. To overcome this, the authors have developed CultLab3D, the world's first fully automatic 3D mass digitization technology for collections of three-dimensional objects. 3D scanning robots such as the CultArm3D-P are specifically designed to automate the entire 3D digitization process thus allowing to capture and archive objects on a large-scale and produce highly accurate photo-realistic representations. © 2020 Society for Imaging Science and technology

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Continual Learning of a Time Series Model Using a Mixture of HMMs with Application to the IoT Fuel Sensor Verification 18

Continual Learning of a Time Series Model Using a Mixture of...

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18th Conference on computer Science and Intelligence Systems, FedCSIS 2023

作者： Glomb, Przemyslaw Cholewa, Michal Foszner, Pawel Bularz, Jakub Institute of Theoretical and Applied Informatics Polish Academy of Sciences Batycka 5 Gliwice44-100 Poland Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland Aiut Sp. Z O.o. ul. Wyczókowskiego 113 Gliwice44-109 Poland

ISBN: (纸本)9788396744784

This paper presents an application of a mixture of Hidden Markov Models (HMMs) as a tool for verification of IoT fuel sensors. The IoT fuel sensors report the level of fuel in tanks of a petrol station, and are a key component for monitoring system reliability (billing), safety (fuel/oil leak detection) and security (theft prevention). We propose an algorithm for learning a mixture of HMMs based on a continual learning principle, i.e. it adapts the model while monitoring a sensor over time, signalling unexpected or anomalous sensor reports. We have tested the proposed approach on a real-life data of 15 fuel tanks being monitored with the FuelPrime system, where it has shown a very good performance (average area under ROC curve of 0.94) of detecting anomalies in the sensor data. Additionally we show that the proposed method can be used for trend monitoring and present qualitative analysis of the short and long term learning performance. The proposed method has promising performance score, the resulting model has a high degree of explainability, limited memory and computation requirements and can be easily generalized to other domains of sensor verification. © 2023 Polish Information Processing Society.

关键词： Hidden Markov models

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A Visual Surveillance System to Observe Realistic Road User Behavior for Improved Pedestrian and Cyclist Safety at Crossroads

A Visual Surveillance System to Observe Realistic Road User ...

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

作者： Nadezda Kirillova Horst Possegger Horst Bischof Institute of Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Semantic 3D Computer Vision

ISBN: (数字)9781665463829

ISBN: (纸本)9781665463836

Pedestrians and cyclists suffer the most serious injuries in traffic accidents. Existing Pedestrian Protection Systems and Road Safety Systems rely on an ideal model of pedestrian behavior and do not consider that people tend to take shortcuts, appear at unexpected places or can be distracted on the road, for example, by using a smartphone or wearing headphones. Collecting and analyzing realistic road user behavior is a crucial component to improve pedestrian and cyclist safety. However, such real-world data is still missing. To address this, we propose a visual surveillance system with two perpendicular partially overlapping fields of view, combined with a fully automated deep learning-based pipeline to process and collect video observations, detect and extract road user trajectories in real-world coordinates and estimate human attributes, such as age, gender, smartphone usage, etc. We demonstrate our prototype by deploying it in two locations in a European city.

关键词： Visualization Smart cities Surveillance Prototypes Streaming media Road safety Behavioral sciences

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Why are thermal images blurry

arXiv

引用

arXiv 2023年

作者： Bao, Fanglin Jape, Shubhankar Schramka, Andrew Wang, Junjie McGraw, Tim E. Jacob, Zubin Birck Nanotechnology Center School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Department of Computer Graphics Technology Purdue University West LafayetteIN47907 United States

The resolution of optical imaging is limited by diffraction as well as detector noise. However, thermal imaging exhibits an additional unique phenomenon of ghosting which results in blurry and low-texture images. Here, we provide a detailed view of thermal physics-driven texture and explain why it vanishes in thermal images capturing heat radiation. We show that spectral resolution in thermal imagery can help recover this texture, and we provide algorithms to recover texture close to the ground truth. Using a simulator for complex 3D scenes, we discuss the interplay of geometric textures and non-uniform temperatures which is common in real-world thermal imaging. We demonstrate the failure of traditional thermal imaging to recover ground truth in multiple scenarios while our thermal perception approach successfully recovers geometric textures. Finally, we put forth an experimentally feasible infrared Bayer-filter approach to achieve thermal perception in pitch darkness as vivid as optical imagery in broad daylight. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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Research on Chinese Summary Generation Based on Pointer Key Information 5

Research on Chinese Summary Generation Based on Pointer Key ...

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5th International Conference on computer Information Science and Application technology, CISAT 2022

作者： Huang, Wenming Bu, Xianghui Xiao, Yannan Wen, Yayuan Deng, Zhenrong School of Computer and Information Security Guilin University of Electronic Technology Guangxi Guilin China Guangxi Key Laboratory of Image and Graphics Intelligent Processing Guangxi Guilin China College of Electronic Engineering Guangxi Normal University Guangxi Guilin China

ISBN: (纸本)9781510660076

Sequence-to-sequence models provide a feasible new approach for generative text summarization, but these models are not able to accurately reproduce factual details and subject information. To address the problem of unconstrained and uncontrollable content generation of generative text summarization models, this paper proposes a generative summarization method KGIT that uses Transformer as a skeleton and incorporates both BERT pre-training model and keyword information. The model uses a comprehensive keyword extraction algorithm, uses two results extracted by LSTM and TextRank as vocabularies respectively, and uses pointers keywords are selected and the extracted keywords are used as the guiding information to generate the summary based on the guiding information. KGIT model can associate the source text and keywords to avoid generating a summary of irrelevant topics. The ROUGE value is used as the evaluation criterion for text summaries, and the summaries generated by the KGIT model can contain more key information and are more accurate and readable when compared with the mainstream summary generation models on the NLPCC2017 Chinese news summary dataset. © 2022 SPIE.

关键词： Natural language processing systems

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FusionINN: Decomposable Image Fusion for Brain Tumor Monitoring

arXiv

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arXiv 2024年

作者： Kumar, Nishant Tao, Ziyan Singh, Jaikirat Li, Yang Sun, Peiwen Zhao, Binghui Gumhold, Stefan Chair of Computer Graphics and Visualization Faculty of Computer Science Technische Universität Dresden Dresden Germany School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Radiology Shanghai Tenth People’s Hospital Tongji University Medical School Shanghai China

Image fusion typically employs non-invertible neural networks to merge multiple source images into a single fused image. However, for clinical experts, solely relying on fused images may be insufficient for making diagnostic decisions, as the fusion mechanism blends features from source images, thereby making it difficult to interpret the underlying tumor pathology. We introduce FusionINN, a novel decomposable image fusion framework, capable of efficiently generating fused images and also decomposing them back to the source images. FusionINN is designed to be bijective by including a latent image alongside the fused image, while ensuring minimal transfer of information from the source images to the latent representation. To the best of our knowledge, we are the first to investigate the decomposability of fused images, which is particularly crucial for life-sensitive applications such as medical image fusion compared to other tasks like multi-focus or multiexposure image fusion. Our extensive experimentation validates FusionINN over existing discriminative and generative fusion methods, both subjectively and objectively. Moreover, compared to a recent denoising diffusion-based fusion model, our approach offers faster and qualitatively better fusion results. The source code of the FusionINN framework is available at: https://***/nish03/FusionINN. © 2024, CC BY.

关键词： Image fusion

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