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Reduct-based ranking of attributes 24

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Reduct-based ranking of attributes

24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Zielosko, Beata Stanczyk, Urszula Institute of Computer Science University of Silesia in Katowice Bedzi nska 39 Sosnowiec41-200 Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The paper is dedicated to the area of feature selection, in particular a notion of attribute rankings that allow to estimate importance of variables. In the research presented for ranking construction a new weighting factor was defined, based on relative reducts. A reduct constitutes an embedded mechanism of feature selection, specific to rough set theory. The proposed factor takes into account the number of reducts in which a given attribute exists, as well as lengths of reducts. Two approaches for reduct generation were employed and compared, with search executed by a genetic algorithm. To validate the usefulness of the reduct-based rankings in the process of feature reduction, for gradually decreasing subsets of attributes, selected through rankings, sets of decision rules were induced in classical rough set approach. The performance of all rule classifiers was evaluated, and experimental results showed that the proposed rankings led to at least the same, or even increased classification accuracy for reduced sets of features than in the case of operating on the entire set of condition attributes. The experiments were performed on datasets from stylometry domain, with treating authorship attribution as a classification task, and stylometric descriptors as characteristic features defining writing styles. © 2020 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

GEASI: Geodesic-based earliest activation sites identification in cardiac models

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arXiv 2021年

作者： Grandits, Thomas Effland, Alexander Pock, Thomas Krause, Rolf Plank, Gernot Pezzuto, Simone Institute of Computer Graphics and Vision Tu Graz Inffeldgasse 16 Graz8010 Austria Center for Computational Medicine in Cardiology Euler Institute Università della Svizzera Italiana via la Santa 1 Lugano6900 Switzerland Gottfried Schatz Research Center Division of Biophysics Medical University of Graz Neue Stiftingtalstraße 6/IV Graz8010 Austria BioTechMed-Graz Graz Austria Graz Austria Institute for Applied Mathematics University of Bonn Endenicher Allee 60 Bonn53115 Germany

The identification of the initial ventricular activation sequence is a critical step for the correct personalization of patient-specific cardiac models. In healthy conditions, the Purkinje network is the main source of the electrical activation, but under pathological conditions the so-called earliest activation sites (EASs) are possibly sparser and more localized. Yet, their number, location and timing may not be easily inferred from remote recordings, such as the epicardial activation or the 12-lead electrocardiogram (ECG), due to the underlying complexity of the model. In this work, we introduce GEASI (Geodesic-based Earliest Activation Sites Identification) as a novel approach to simultaneously identify all EASs. To this end, we start from the anisotropic eikonal equation modeling cardiac electrical activation and exploit its Hamilton-Jacobi formulation to minimize a given objective function, e.g. the quadratic mismatch to given activation measurements. This versatile approach can be extended to estimate the number of activation sites by means of the topological gradient, or fitting a given ECG. We conducted various experiments in 2D and 3D for in-silico models and an in-vivo intracardiac recording collected from a patient undergoing cardiac resynchronization therapy. The results demonstrate the clinical applicability of GEASI for potential future personalized models and clinical *** Codes 92B05, 35Q93, 65K10, 35F21, 35F20 Copyright © 2021, The Authors. All rights reserved.

关键词： Chemical activation

SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

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arXiv 2023年

作者： Fang, Meiling Huber, Marco Fierrez, Julian Ramachandra, Raghavendra Damer, Naser Alkhaddour, Alhasan Kasantcev, Maksim Pryadchenko, Vasiliy Yang, Ziyuan Huangfu, Huijie Chen, Yingyu Zhang, Yi Pan, Yuchen Jiang, Junjun Liu, Xianming Sun, Xianyun Wang, Caiyong Liu, Xingyu Chang, Zhaohua Zhao, Guangzhe Tapia, Juan Gonzalez-Soler, Lazaro Aravena, Carlos Schulz, Daniel Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norway ID R&D Inc. New York United States School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks. © 2023, CC BY-NC-SA.

关键词： Competition

PIEMAP: Personalized inverse eikonal model from cardiac electro-anatomical maps

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arXiv 2020年

作者： Grandits, Thomas Pezzuto, Simone Lubrecht, Jolijn M. Pock, Thomas Plank, Gernot Krause, Rolf Institute of Computer Graphics and Vision Graz University of Technology Austria Center for Computational Medicine in Cardiology Institute of Computational Science Università della Svizzera italiana Lugano Switzerland Institute of Biophysics Medical University of Graz Austria BioTechMed-Graz Austria

Electroanatomical mapping, a keystone diagnostic tool in cardiac electrophysiology studies, can provide high-density maps of the local electric properties of the tissue. It is therefore tempting to use such data to better individualize current patient-specific models of the heart through a data assimilation procedure and to extract potentially insightful information such as conduction properties. Parameter identification for state-of-the-art cardiac models is however a challenging task. In this work, we introduce a novel inverse problem for inferring the anisotropic structure of the conductivity tensor, that is fiber orientation and conduction velocity along and across fibers, of an eikonal model for cardiac activation. The proposed method, named PIEMAP, performed robustly with synthetic data and showed promising results with clinical data. These results suggest that PIEMAP could be a useful supplement in future clinical workflows of personalized therapies. Copyright © 2020, The Authors. All rights reserved.

关键词： Heart

Minimal Cases for Computing the Generalized Relative Pose using Affine Correspondences

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arXiv 2020年

作者： Guan, Banglei Zhao, Ji Barath, Daniel Fraundorfer, Friedrich College of Aerospace Science and Engineering National University of Defense Technology China Computer Vision and Geometry Group Department of Computer Science ETH Zürich Institute for Computer Graphics and Vision Graz University of Technology Austria Remote Sensing Technology Institute German Aerospace Center Germany

We propose three novel solvers for estimating the relative pose of a multi-camera system from affine correspondences (ACs). A new constraint is derived interpreting the relationship of ACs and the generalized camera model. Using the constraint, we demonstrate efficient solvers for two types of motions assumed. Considering that the cameras undergo planar motion, we propose a minimal solution using a single AC and a solver with two ACs to overcome the degenerate case. Also, we propose a minimal solution using two ACs with known vertical direction, e.g., from an IMU. Since the proposed methods require significantly fewer correspondences than state-of-the-art algorithms, they can be efficiently used within RANSAC for outlier removal and initial motion estimation. The solvers are tested both on synthetic data and on real-world scenes from the KITTI odometry benchmark. It is shown that the accuracy of the estimated poses is superior to the state-of-the-art techniques. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion estimation

ALCN: Adaptive Local Contrast Normalization

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arXiv 2020年

作者： Rad, Mahdi Roth, Peter M. Lepetit, Vincent Institute of Computer Graphics and Vision Graz University of Technology Graz Austria LIGM IMAGINE Ecole des Ponts Univ Gustave Eiffel CNRS Marne-la-Vallée France

To make Robotics and Augmented Reality applications robust to illumination changes, the current trend is to train a Deep Network with training images captured under many different lighting conditions. Unfortunately, creating such a training set is a very unwieldy and complex task. We therefore propose a novel illumination normalization method that can easily be used for different problems with challenging illumination conditions. Our preliminary experiments show that among current normalization methods, the Difference-of-Gaussians method remains a very good baseline, and we introduce a novel illumination normalization model that generalizes it. Our key insight is then that the normalization parameters should depend on the input image, and we aim to train a Convolutional Neural Network to predict these parameters from the input image. This, however, cannot be done in a supervised manner, as the optimal parameters are not known a priori. We thus designed a method to train this network jointly with another network that aims to recognize objects under different illuminations: The latter network performs well when the former network predicts good values for the normalization parameters. We show that our method significantly outperforms standard normalization methods and would also be appear to be universal since it does not have to be re-trained for each new application. Our method improves the robustness to light changes of state-of-the-art 3D object detection and face recognition methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

Fast decomposable submodular function minimization using constrained total variation 33

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Fast decomposable submodular function minimization using con...

33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019

作者： Sesh Kumar, K.S. Bach, Francis Pock, Thomas Data Science Institute Imperial College London United Kingdom INRIA Ecole Normale Superieure PSL Research University Paris France Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

We consider the problem of minimizing the sum of submodular set functions assuming minimization oracles of each summand function. Most existing approaches reformulate the problem as the convex minimization of the sum of the corresponding Lovász extensions and the squared Euclidean norm, leading to algorithms requiring total variation oracles of the summand functions;without further assumptions, these more complex oracles require many calls to the simpler minimization oracles often available in practice. In this paper, we consider a modified convex problem requiring a constrained version of the total variation oracles that can be solved with significantly fewer calls to the simple minimization oracles. We support our claims by showing results on graph cuts for 2D and 3D graphs. © 2019 Neural information processing systems foundation. All rights reserved.

关键词： Graphic methods

REGULARIZATION OF BUILDING BOUNDARIES IN SATELLITE IMAGES USING ADVERSARIAL AND REGULARIZED LOSSES

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REGULARIZATION OF BUILDING BOUNDARIES IN SATELLITE IMAGES US...

IEEE International Geoscience and Remote Sensing Symposium

作者： Stefano Zorzi Friedrich Fraundorfer Institute of Computer Graphics and Vision Graz University of Technology

In this paper we present a method for building boundary refinement and regularization in satellite images using a fully convolutional neural network trained with a combination of adversarial and regularized losses. Compared to a pure Mask R-CNN model, the overall algorithm can achieve equivalent performance in terms of accuracy and completeness. However, unlike Mask R-CNN that produces irregular footprints, our framework generates regularized and visually pleasing building boundaries which are beneficial in many applications.

关键词： Buildings Image reconstruction Image segmentation Training Satellites Erbium Pipelines

Depth-Awareness in a System for Mixed-Reality Aided Surgical Procedures 15th

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Depth-Awareness in a System for Mixed-Reality Aided Surgical...

15th International Conference on Intelligent Computing, ICIC 2019

作者： Sylos Labini, Mauro Gsaxner, Christina Pepe, Antonio Wallner, Juergen Egger, Jan Bevilacqua, Vitoantonio Institute for Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Electrical and Information Engineering Polytechnic University of Bari Bari Italy Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria

ISBN: (纸本)9783030267650

computer-assisted surgery is a trending topic in research, with many different approaches which aim at supporting surgeons in the operating room. Existing surgical planning and navigation solutions are often considered to be distracting, unintuitive or hard to interpret. In this work, we address this issue with an approach based on mixed reality devices like Microsoft HoloLens. We assess the depth sensing capabilities of Microsoft HoloLens, and the potential benefit they could bring to computer-assisted surgery applications. © 2019, Springer Nature Switzerland AG.

关键词： Mixed reality