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Frontiers of Information Technology & Electronic Engineering 2022年第3期23卷 479-487页

作者： Miklos HOFFMANN Imre JUHASZ Ede TROLL Institute of Mathematics and Computer Science Eszterhazy Karoly UniversityEger 3300Hungary Department of Computer Graphics and Image Processing University of DebrecenDebrecen 4010Hungary Department of Descriptive Geometry University of MiskolcMiskolc 3515Hungary

While considering a mirror and light rays coming either from a point source or from infinity,the reflected light rays may have an envelope,called a caustic *** this paper,we study developable surfaces as *** caustic surfaces,described in a closed form,are also developable surfaces of the same type as the original mirror *** provide efficient,algorithmic computation to find the caustic surface of each of the three types of developable surfaces(cone,cylinder,and tangent surface of a spatial curve).We also provide a potential application of the results in contemporary free-form architecture design.

关键词： Caustics Developable surface Reflected light rays Curve of regression

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CANDID: Correspondence AligNment for Deep-burst Image Denoising

CANDID: Correspondence AligNment for Deep-burst Image Denois...

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Canadian Conference on computer and Robot Vision, CRV

作者： Arijit Mallick Raphael Braun Hendrik PA Lensch Department of Computer Graphics University of Tübingen Germany TWT GmbH Science & Innovation Germany

With the advent of mobile phone photography and point-and-shoot cameras, deep-burst imaging is widely used for a number of photographic effects such as depth of field, super-resolution, motion deblurring, and image denoising. In this work, we propose to solve the problem of deep-burst image denoising by including an optical flow-based correspondence estimation module which aligns all the input burst images with respect to a reference frame. In order to deal with varying noise levels the individual burst images are pre-filtered with different settings. Exploiting the established correspondences one network block predicts a pixel-wise spatially-varying filter kernel to smooth each image in the original and prefiltered bursts before fusing all images to generate the final denoised output. The resulting pipeline achieves state-of-the-art results by combining all available information provided by the burst.

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Perceptual navigation in absorption-scattering space 29

Perceptual navigation in absorption-scattering space

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29th Color and Imaging Conference - Color science and Engineering Systems, Technologies, and Applications, CIC 2021

作者： Gigilashvili, Davit Urban, Philipp Thomas, Jean-Baptiste Pedersen, Marius Hardeberg, Jon Yngve Norwegian University of Science and Technology Department of Computer Science Gjøvik Norway Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany

ISBN: (纸本)9780892083572

Translucency optically results from subsurface light transport and plays a considerable role in how objects and materials appear. Absorption and scattering coefficients parametrize the distance a photon travels inside the medium before it gets absorbed or scattered, respectively. Stimuli produced by a material for a distinct viewing condition are perceptually non-uniform w.r.t. these coefficients. In this work, we use multi-grid optimization to embed a non-perceptual absorption-scattering space into a perceptually more uniform space for translucency and lightness. In this process, we rely on A (alpha) as a perceptual translucency metric. Small Euclidean distances in the new space are roughly proportional to lightness and apparent translucency differences measured with A. This makes picking A more practical and predictable, and is a first step toward a perceptual translucency space. © 2021 Society for Imaging science and Technology. All rights reserved.

关键词： Color

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Estimation of the Impact of Dimensionality Reduction of the Feature Space on the Efficiency of Movements Classification Based on Surface Electromyography

Estimation of the Impact of Dimensionality Reduction of the ...

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Neural Networks and Neurotechnologies (NeuroNT), II International Conference on

作者： Ivan V. Kuznetsov Yulia V. Ponomarchuk Department of Computer Science and Computer Graphics Far Eastern State Transport University Khabarovsk Russia Faculty of Control Automation and Telecommunications Far Eastern State Transport University Khabarovsk Russia

ISBN: (数字)9798350363739

ISBN: (纸本)9798350363746

One of the actual tasks of movement classification based on electromyography is the choice of the most informative set of features characterizing the movements. Increasing the number of features may decrease the learning performance of classifiers and reduce the efficiency of gesture classification. This paper is devoted to study the possibilities of improving the movement's classification efficiency by using the feature space dimensionality reduction methods. Classifiers based on Support Vector Machines (SVM)., Decision Trees (DT)., Random Forest (RF) and K-nearest neighbors (KNN) were considered., and Principal Component Analysis method (PCA) was used to dimensionality reduction. 3% improvement in classification performance was achieved using PCA and KNN.

关键词： Dimensionality reduction Support vector machines Radio frequency Thumb Electromyography Sensors Task analysis

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Challenges of Distance Learning During the Coronavirus (COVID-19) Pandemic A Case Study of the University of Petra

Challenges of Distance Learning During the Coronavirus (COVI...

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Cyber Resilience (ICCR), International Conference on

作者： Khalil Omar Yaser Saleh Ghassan Issa Computer Science Department University of Petra Amman Jordan Department of Computer Graphics and Animation Princes Sumaya University for Technology Amman Jordan School of Information Technology Skyline University College Sharjah UAE

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

The Coronavirus (COVID-19) pandemic has affected all aspects of our daily life and imposed social distancing among people. In higher education, the shift from traditional learning (TL) to the distance learning (DL) model has become inevitable to continue academic life. However, this unexpected shift is complex and poses new challenges and obstacles for the stakeholders of the DL system. In this research, the DL system's stakeholders have been classified into students, instructors, technical administrators, and educational administrators. This research study aims to gather the perceptions of and satisfaction level of these stakeholders with DL and explore the challenges encountered by these stakeholders during the shift to DL during the COVID-19 pandemic. Therefore, four questionnaires were developed and distributed to the identified stakeholders at the University of Petra, which has been adopted as a case study for this research.

关键词： COVID-19 computer aided instruction Pandemics Education Human factors Social factors Stakeholders

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FX-MAD: Frequency-Domain Explainability and Explainability-Driven Unsupervised Detection of Face Morphing Attacks

FX-MAD: Frequency-Domain Explainability and Explainability-D...

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IEEE Winter Applications and computer Vision Workshops (WACVW)

作者： Marco Huber Pedro C. Neto Ana F. Sequeira Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science Technische Universität Darmstadt Germany INESC TEC Porto Portugal Faculry of Engineering University of Porto Portugal

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Face recognition (FR) systems are vulnerable to morphing attacks, which refer to face images created by morphing the facial features of two different identities into one face image to create an image that can match both identities, allowing serious security breaches. In this work, we apply a frequency-based explanation method from the area of explainable face recognition to shine a light on how FR models behave when processing a bona fide or attack pair from a frequency perspective. In extensive experiments, we used two different state-of-the-art FR models and six different morphing attacks to investigate possible differences in behavior. Our results show that FR models rely differently on different frequency bands when making decisions for bona fide pairs and morphing attacks. In the following step, we show that this behavioral difference can be used to detect morphing attacks in an unsupervised setup solely based on the observed frequency-importance differences in a generalizable manner.

关键词： computer vision Correlation Face recognition Soft sensors Frequency-domain analysis Conferences Benchmark testing Security Facial features

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A proposal to measure the understanding of data visualization elements in visual analytics applications 10

A proposal to measure the understanding of data visualizatio...

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10th Learning Analytics Summer Institute Spain, LASI Spain 2022

作者： Vázquez-Ingelmo, Andrea García-Peñalvo, Francisco José Therón, Roberto Byrd, Vetria Camba, Jorge D. GRIAL Research Group University of Salamanca Paseo de Canalejas 169 Salamanca Spain VisUSAL Computer Science Department Universidad de Salamanca Salamanca Spain Computer Graphics Technology Purdue University West LafayetteIN United States

Data visualizations and information dashboards are useful but complex tools. They must be fully understood to draw proper insights and to avoid misleading conclusions. However, several elements and factors are involved in this domain, which makes it difficult to learn. In previous works, we proposed a meta-model to capture the primitive elements that compose visualizations and dashboards. This meta-model has served as a framework for conducting data visualization research, but also to develop a graphical tool for generating data visualizations and dashboards. This tool (namely MetaViz) enables users to create data visualizations through fine-grained components based on the entities represented in the meta-model. The main goal of the system is to provide a learning experience in which users can freely add and configure elements to understand how they influence the final display. This work describes work in progress to validate the pedagogical value of MetaViz in terms of the understanding of data visualization concepts. © 2022 Copyright for this paper by its authors.

关键词： Data visualization

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Duplicate Detection Using Unsupervised Random Forests: A Preliminary Analysis 3

Duplicate Detection Using Unsupervised Random Forests: A Pre...

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3rd International Conference on Artificial Intelligence and Data sciences, AiDAS 2022

作者： Omar, Zaturrawiah Ali Abu Bakar, Mohd Aftar Zamzuri, Zamira Hasanah Ariff, Noratiqah Mohd Universiti Malaysia Sabah Mathematics with Computer Graphics Programme Faculty of Science and Natural Resources Sabah Malaysia Universiti Kebangsaan Malaysia Faculty of Science and Technology Department of Mathematical Sciences Selangor Malaysia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

Detecting duplicate records is an essential step in data preparation for producing high-quality data for analysis. In statistics, it is commonly referred to as record linkage, but it goes by other names depending on the field of study. This study attempted to conduct duplicate detection on RLdata500 data using unsupervised Random Forests. In general, unsupervised machine learning algorithms are inferior to supervised learning algorithms. However, supervised learning requires labelled training data, which is absent from the majority of real-world datasets. Therefore, unsupervised learning is still applicable, and the performance of unsupervised learning algorithms can be enhanced through proper data conditioning. This paper reports the outcome of such conditioning by using bins and string encoding (Onca and Soundex). The similarity weight produced by the pairwise proximity matrix was used as the matching measure, and an optimal threshold was decided to be the cut-off point for classifying the record pairs as a match (duplicate detected) or non-match. Since the duplicate records in this dataset are known, the performances of the conditions can be compared using recall, precision, and F-measure, and the optimal threshold was determined using the maximum F -measure. Additional precision-recall graphs, as well as an F -measure graph that was transformed for a fair comparison, were used. The results then determined that the optimal conditions (2, 6 and 7) were the ones where the number values were treated as continuous. The performance of the employed encoding methods was equal when applied to the dataset and, together with binning, those conditions were able to detect more perfect matches. In the future, more testing and comparing will be done with different datasets and methods. © 2022 IEEE.

关键词： Supervised learning

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Automatic Animations to Analyze Blood Flow Data

Automatic Animations to Analyze Blood Flow Data

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2021 Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2021

作者： Apilla, V. Behrendt, B. Lawonn, K. Preim, B. Meuschke, M. Department of Simulation and Graphics University of Magdeburg Germany Institute of Computer Science University of Jena Germany

ISBN: (纸本)9783038681403

We present an approach for computing camera animations composed of optimal views to support the visual exploration of blood flow data using cerebral aneurysms as major example. Medical researchers are interested in hemodynamic parameters and vessel wall characteristics. The time-dependent character of blood flow data complicates the visual analysis. Our approach is modeled as an optimization problem to automatically determine camera paths during the cardiac cycle. These consist of optimal viewpoints showing regions with suspicious characteristics of wall- and flow-related parameters. This provides medical researchers with an efficient method of obtaining a fast overview of patient-specific blood flow data. © 2021 The Author(s) Eurographics Proceedings © 2021 The Eurographics Association.

关键词： Cameras

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P2LNet: HD Map Validation Using Graph Neural Networks

P2LNet: HD Map Validation Using Graph Neural Networks

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Robotics, Engineering, science, and Technology (RESTCON), International Conference on

作者： Jeevan Reji Vaibhav Omanwar Department of Computer Science Birla Institute of Technology & Sciences Pilani Hyderabad Campus India Nvidia Graphics Pvt Ltd Pune India

HD Maps are a highly important part of the autonomous driving stack to perform the activities of localization and route planning on the road. Therefore, HD Map Validation is crucial to ensure the HD Maps represent the road information as accurately as possible. While heuristics have been developed to validate HD Maps with reasonable accuracy, they are unable to solve the challenging path to traffic light validation problem of HD Map junctions. Based on the success of Graph Neural Network approaches on HD Map problems, such as Vector net, we propose the P2LNet. This P2LNet architecture consists of a fully connected subgraph followed by a Graph Encoder-Decoder Architecture that finally predicts the correct associations that would exist between paths and traffic lights in the junction. We trained and evaluated P2LNet on our in-house HD Map junction dataset, with P2LNet showing 94% accuracy on predicting the correct labels for the associations from a test set. While the results could be further improved by using edge features, P2LNet provides a significant accuracy in validating incorrect light associations. It also shows how GNN based approaches can be used to solve other significant HD Map and validation issues.

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