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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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On the (Limited) Generalization of MasterFace Attacks and Its Relation to the Capacity of Face Representations

arXiv

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arXiv 2022年

作者： Terhörst, Philipp Bierbaum, Florian Huber, Marco Damer, Naser Kirchbuchner, Florian Raja, Kiran Kuijper, Arjan Norwegian University of Science and Technology Gjøvik Norway Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Technical University of Darmstadt Darmstadt Germany

A MasterFace is a face image that can successfully match against a large portion of the population. Since their generation does not require access to the information of the enrolled subjects, MasterFace attacks represent a potential security risk for widely-used face recognition systems. Previous works proposed methods for generating such images and demonstrated that these attacks can strongly compromise face recognition. However, previous works followed evaluation settings consisting of older recognition models, limited cross-dataset and cross-model evaluations, and the use of low-scale testing data. This makes it hard to state the generalizability of these attacks. In this work, we comprehensively analyse the generalizability of MasterFace attacks in empirical and theoretical investigations. The empirical investigations include the use of six state-of-the-art FR models, cross-dataset and cross-model evaluation protocols, and utilizing testing datasets of significantly higher size and variance. The results indicate a low generalizability when MasterFaces are training on a different face recognition model than the one used for testing. In these cases, the attack performance is similar to zero-effort imposter attacks. In the theoretical investigations, we define and estimate the face capacity and the maximum MasterFace coverage under the assumption that identities in the face space are well separated. The current trend of increasing the fairness and generalizability in face recognition indicates that the vulnerability of future systems might further decrease. Future works might analyse the utility of MasterFaces for understanding and enhancing the robustness of face recognition models. © 2022, CC BY.

关键词： Face recognition

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First Competition on Presentation Attack Detection on ID Card

First Competition on Presentation Attack Detection on ID Car...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Juan E. Tapia Naser Damer Christoph Busch Juan M. Espin Javier Barrachina Alvaro S. Rocamora Krištof Ocvirk Leon Alessio Borut Batagelj Sushrut Patwardhan Raghavendra Ramachandra Raghavendra Mudgalgundurao Kiran Raja Daniel Schulz Carlos Aravena Hochschule Darmstadt (h_da) da/sec-Biometrics and Internet Security Research Germany Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Facephi Company Spain Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Norwegian University of Science and Technology (NTNU) Gjøvik Norway ID VisionCenter (IDVC) Santiago Chile

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

This paper summarises the Competition on Presentation Attack Detection on ID Cards (PAD-IDCard) held at the 2024 International Joint Conference on Biometrics (IJCB 2024). The competition attracted a total of ten registered teams, both from academia and industry. In the end, the participating teams submitted five valid submissions, with eight models to be evaluated by the organisers. The competition presented an independent assessment of current state-of-the-art algorithms. Today, no independent evaluation on cross-dataset is available; therefore, this work determined the state-of-the-art on ID cards. To reach this goal, a sequestered test set and baseline algorithms were used to evaluate and compare all the proposals. The sequestered test dataset contains ID cards from four different countries. In summary, a team that chose to be "Anonymous" reached the best average ranking results of 74.80%, followed very closely by the "IDVC" team with 77.65%.

关键词： Biometrics Training Metalearning Industries Accuracy Biological system modeling Benchmark testing Proposals Few shot learning Stress

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

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

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Federated Conference on computer Science and Information Systems (FedCSIS)

作者： Przemysław Głomb Michał Cholewa Paweł Foszner Jakub Bularz Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland AIUT Sp. z o.o. ul Gliwice Poland

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.

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Design activity worksheets for developing research questions

Design activity worksheets for developing research questions

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2020 ASEE Virtual Annual Conference, ASEE 2020

作者： Byrd, Vetria Camba, Jorge D. Purdue University-Main Campus College of Engineering West Lafayette United States Department of Computer Graphics Technology Polytechnic Institute Purdue University West LafayetteIN United States Department of Computer Graphics Technology Purdue University West LafayetteIN United States

Developing good research questions is a skill that develops over time and is often difficult for students. More often than not, it is up to the student to determine what to research. Where do I start? What do I look for? Is this a good research question? These are just a few of the questions students ask and should ask in the early stages of their research process. This work describes a progressive plan for developing research questions using a series of four design activity worksheets to guide students through the cognitive and metacognitive processes of choosing topics, asking questions, identifying an appropriate problem and locating adequate data sources. The worksheets are introduced in a linear manner and completed in order. This progressive path is intended to provide a logical and methodical approach for developing research questions. However, each worksheet can be completed independently. The worksheets can also be used as a pedagogical approach to introduce research methods into the classroom. The design activity worksheets have been used in multiple venues: undergraduate and graduate data visualization courses, workshops and more recently, a graduate seminar designed to help students identify their thesis topics in partial fulfillment of their graduate degree. In this work, we describe the development and characteristics of the worksheets and report some preliminary results of a study designed to assess their perceived impact and usefulness from a student's perspective. © American Society for Engineering Education 2020.

关键词： Students

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MD-Net: Multi-Detector for Local Feature Extraction

arXiv

引用

arXiv 2022年

作者： Santellani, Emanuele Sormann, Christian Rossi, Mattia Kuhn, Andreas Fraundorfer, Friedrich Institute of Computer Graphics and Vision Graz University of Technology Austria R&D Center Stuttgart Laboratory 1 Sony Europe B.V. Germany

Establishing a sparse set of keypoint correspondences between images is a fundamental task in many computer vision pipelines. Often, this translates into a computationally expensive nearest neighbor search, where every keypoint descriptor at one image must be compared with all the descriptors at the others. In order to lower the computational cost of the matching phase, we propose a deep feature extraction network capable of detecting a predefined number of complementary sets of keypoints at each image. Since only the descriptors within the same set need to be compared across the different images, the matching phase computational complexity decreases with the number of sets. We train our network to predict the keypoints and compute the corresponding descriptors jointly. In particular, in order to learn complementary sets of keypoints, we introduce a novel unsupervised loss which penalizes intersections among the different sets. Additionally, we propose a novel descriptor-based weighting scheme meant to penalize the detection of keypoints with non-discriminative descriptors. With extensive experiments we show that our feature extraction network, trained only on synthetically warped images and in a fully unsupervised manner, achieves competitive results on 3D reconstruction and re-localization tasks at a reduced matching complexity. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions

arXiv

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arXiv 2023年

作者： Leitner, Stefan Mirza, M. Jehanzeb Lin, Wei Micorek, Jakub Masana, Marc Kozinski, Mateusz Possegger, Horst Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Austria Christian Doppler Laboratory for Semantic 3D Computer Vision Austria TU Graz SAL Dependable Embedded Systems Lab Silicon Austria Labs Austria

In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. However, their performance deteriorates significantly when tested in degrading weather conditions. In addition, even when adapted to perform robustly in a sequence of different weather conditions, they are often unable to perform well in all of them and suffer from catastrophic forgetting. To efficiently mitigate forgetting, we propose Domain-Incremental Learning through Activation Matching (DILAM), which employs unsupervised feature alignment to adapt only the affine parameters of a clear weather pre-trained network to different weather conditions. We propose to store these affine parameters as a memory bank for each weather condition and plug-in their weather-specific parameters during driving (i.e. test time) when the respective weather conditions are encountered. Our memory bank is extremely lightweight, since affine parameters account for less than 2% of a typical object detector. Furthermore, contrary to previous domain-incremental learning approaches, we do not require the weather label when testing and propose to automatically infer the weather condition by a majority voting linear classifier. © 2023, CC BY.

关键词： Object detection

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Fire Segmentation in Still Images 20th

Fire Segmentation in Still Images

引用

20th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2020

作者： Mlích, Jozef Koplík, Karel Hradiš, Michal Zemčík, Pavel Department of Computer Graphics and Multimedia Faculty of Information Technology Brno University of Technology Božetěchova 2 Brno Czech Republic

ISBN: (纸本)9783030406042

In this paper, we propose a novel approach to fire in images based on a state of the art semantic segmentation method DeepLabV3. We compiled a data set of 1775 images containing fire from various sources for which we created polygon annotations. The data set is augmented with hard non-fire images from SUN397 data set. The segmentation method trained on our data set achieved results better than state of the art results on BowFire data set. We believe the created data set(http://***/research/view_***?id=12124) will facilitate further development of fire detection and segmentation methods, and that the methods should be based on general purpose segmentation networks. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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CC-DCNet: Dynamic Convolutional Neural Network with Contrastive Constraints for Identifying Lung Cancer Subtypes on Multi-modality Images

arXiv

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arXiv 2024年

作者： Jin, Yuan Ma, Gege Chen, Geng Lyu, Tianling Egger, Jan Lyu, Junhui Zhang, Shaoting Zhu, Wentao Zhejiang Lab 311121 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi’an710072 China The Zhejiang University School of Medicine Sir Run Run Shaw Hospital Hangzhou310016 China Shanghai Artificial Intelligence Laboratory Shanghai200120 China

The accurate diagnosis of pathological subtypes of lung cancer is of paramount importance for follow-up treatments and prognosis managements. Assessment methods utilizing deep learning technologies have introduced novel approaches for clinical diagnosis. However, the majority of existing models rely solely on single-modality image input, leading to limited diagnostic accuracy. To this end, we propose a novel deep learning network designed to accurately classify lung cancer subtype with multi-dimensional and multi-modality images, i.e., CT and pathological images. The strength of the proposed model lies in its ability to dynamically process both paired CT-pathological image sets as well as independent CT image sets, and consequently optimize the pathology-related feature extractions from CT images. This adaptive learning approach enhances the flexibility in processing multi-dimensional and multi-modality datasets and results in performance elevating in the model testing phase. We also develop a contrastive constraint module, which quantitatively maps the cross-modality associations through network training, and thereby helps to explore the "gold standard" pathological information from the corresponding CT scans. To evaluate the effectiveness, adaptability, and generalization ability of our model, we conducted extensive experiments on a large-scale multi-center dataset and compared our model with a series of state-of-the-art classification models. The experimental results demonstrated the superiority of our model for lung cancer subtype classification, showcasing significant improvements in accuracy metrics such as ACC, AUC, and F1-score. Copyright © 2024, The Authors. All rights reserved.

关键词： Lung cancer

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DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo

arXiv

引用

arXiv 2022年

作者： Sormann, Christian Santellani, Emanuele Rossi, Mattia Kuhn, Andreas Fraundorfer, Friedrich Graz University of Technology Institute of Computer Graphics and Vision Austria Sony Europe B.V. R&D Center Stuttgart Laboratory 1 Germany

We propose a novel approach for deep learning-based Multi-View Stereo (MVS). For each pixel in the reference image, our method leverages a deep architecture to search for the corresponding point in the source image directly along the corresponding epipolar line. We denote our method DELS-MVS: Deep Epipolar Line Search Multi-View Stereo. Previous works in deep MVS select a range of interest within the depth space, discretize it, and sample the epipolar line according to the resulting depth values: this can result in an uneven scanning of the epipolar line, hence of the image space. Instead, our method works directly on the epipolar line: this guarantees an even scanning of the image space and avoids both the need to select a depth range of interest, which is often not known a priori and can vary dramatically from scene to scene, and the need for a suitable discretization of the depth space. In fact, our search is iterative, which avoids the building of a cost volume, costly both to store and to process. Finally, our method performs a robust geometry-aware fusion of the estimated depth maps, leveraging a confidence predicted alongside each depth. We test DELS-MVS on the ETH3D, Tanks and Temples and DTU benchmarks and achieve competitive results with respect to state-of-the-art approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

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