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29th International Conference in Central Europe on computer Graphics, Visualization and computer Vision, WSCG 2021

作者： Miller, Markus Nischwitz, Alfred Westermann, Rüdiger Dept. of Computer Science and Mathematics University of Applied Sciences Munich Lothstr. 64 Bavaria MunichD-80335 Germany Chair of Computer Graphics and Visualization Technical University Munich Boltzmannstr. 3/II Bavaria GarchingD-85748 Germany

ISBN: (纸本)9788086943343

In augmented reality applications, consistent illumination between virtual and real objects is important for creating an immersive user experience. Consistent illumination can be achieved by appropriate parameterisation of the virtual illumination model, that is consistent with real-world lighting conditions. In this study, we developed a method to reconstruct the general light direction from red-green-blue (RGB) images of real-world scenes using a modified VGG-16 neural network. We reconstructed the general light direction as azimuth and elevation angles. To avoid inaccurate results caused by coordinate uncertainty occurring at steep elevation angles, we further introduced stereographically projected coordinates. Unlike recent deep-learning-based approaches for reconstructing the light source direction, our approach does not require dept. information and thus does not rely on special red-green-blue-dept. (RGB-D) images as input. © 2021 University of West Bohemia. All rights reserved.

关键词： Image reconstruction

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Light Direction Reconstruction Analysis and Improvement using XAI and CG 30

Light Direction Reconstruction Analysis and Improvement usin...

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30th International Conference in Central Europe on computer Graphics, Visualization and computer Vision, WSCG 2022

作者： Miller, Markus Ronczka, Stefan Nischwitz, Alfred Westermann, Rüdiger Dept. of Computer Science and Mathematics University of Applied Sciences Munich Lothstr. 64 Bavaria MunichD-80335 Germany Computer Graphics and Visualization Technical University Munich Boltzmannstr. 3/II Bavaria GarchingD-85748 Germany w&co MediaServices GmbH & Co KG Charles-de-Gaulle-Str. 8 Bavaria MunichD-81737 Germany

ISBN: (纸本)9788086943336

With rapid advances in the field of deep learning, explainable artificial intelligence (XAI) methods were introduced to gain insight into internal procedures of deep neural networks. Information gathered by XAI methods can help to identify shortcomings in network architectures and image datasets. Recent studies, however, advise to handle XAI interpretations with care, as they can be unreliable. Due to this unreliability, this study uses meta information that is produced when applying XAI to enhance the architecture - and thus the prediction performance - of a recently published regression model. This model aimed to contribute to solving the photometric registration problem in the field of augmented reality by regressing the dominant light direction in a scene. Bypassing misleading XAI interpretations, the influence of synthetic training data, generated with different rendering techniques, is furthermore evaluated empirically. In conclusion, this study demonstrates how the prediction performance of the recently published model can be increased by improving the network architecture and training dataset. © 2022 University of West Bohemia. All rights reserved.

关键词： Network architecture

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Graph Fourier MMD for Signals on Graphs

Graph Fourier MMD for Signals on Graphs

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International Conference on Sampling Theory and Applications (SampTA)

作者： Samuel Leone Aarthi Venkat Guillaume Huguet Alexander Tong Guy Wolf Smita Krishnaswamy Applied Math Program New Haven Connecticut Computational Biology & Bioinformatics Program Yale University New Haven Connecticut Dept. of Math. & Stat Mila - Quebec AI Institute Université de Montréal Montreal Canada Dept. of Comp. Sci. & Oper. Res. Mila-Quebec AI Institute Université de Montréal Montreal Canada Department of Computer Science Department of Genetics Yale University New Haven Connecticut

While numerous methods have been proposed for computing distances between probability distributions in Euclidean space, relatively little attention has been given to computing such distances for distributions on graphs. However, there has been a marked increase in data that either lies on graph (such as protein interaction networks) or can be modeled as a graph (single cell data), particularly in the biomedical sciences. Thus, it becomes important to find ways to compare signals defined on such graphs. Here, we propose Graph Fourier MMD (GFMMD), a novel distance between distributions and signals on graphs. GFMMD is defined via an optimal witness function that is both smooth on the graph and maximizes the difference in expectation between the pair of distributions on the graph. We find an analytical solution to this optimization problem as well as an embedding of distributions that results from this method. We also prove several properties of this method including scale invariance and applicability to disconnected graphs. We showcase it on graph benchmark datasets as well on single cell RNA-sequencing data analysis. In the latter, we use the GFMMD-based gene embeddings to find meaningful gene clusters. We also propose a novel type of score for gene selection called gene localization score which helps select genes for cellular state space characterization.

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De-homogenization using convolutional neural networks

arXiv

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arXiv 2021年

作者： Elingaard, Martin O. Aage, Niels Bærentzen, J. Andreas Sigmund, Ole Dept. of Mechanical Engineering Dept. of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark

This paper presents a deep learning-based de-homogenization method for structural compliance minimization. By using a convolutional neural network to parameterize the mapping from a set of lamination parameters on a coarse mesh to a one-scale design on a fine mesh, we avoid solving the least square problems associated with traditional de-homogenization approaches and save time correspondingly. To train the neural network, a two-step custom loss function has been developed which ensures a periodic output field that follows the local lamination orientations. A key feature of the proposed method is that the training is carried out without any use of or reference to the underlying structural optimization problem, which renders the proposed method robust and insensitive wrt. domain size, boundary conditions, and loading. A post-processing procedure utilizing a distance transform on the output field skeleton is used to project the desired lamination widths onto the output field while ensuring a predefined minimum length-scale and volume fraction. To demonstrate that the deep learning approach has excellent generalization properties, numerical examples are shown for several different load and boundary conditions. For an appropriate choice of parameters, the de-homogenized designs perform within 7 − 25% of the homogenization-based solution at a fraction of the computational cost. With several options for further improvements, the scheme may provide the basis for future interactive high-resolution topology optimization. Copyright © 2021, The Authors. All rights reserved.

关键词： Homogenization method

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Iterative Feedback Tuning Algorithm for Tower Crane Systems

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Procedia computer science 2022年 199卷 157-165页

作者： Raul-Cristian Roman Radu-Emil Precup Elena-Lorena Hedrea Stefan Preitl Iuliu Alexandru Zamfirache Claudia-Adina Bojan-Dragos Emil M Petriu Politehnica University of Timişoara Dept. Automation and Applied Informatics Bd. V. Parvan 2 300223 Timişoara Romania University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada

This paper proposes validates an Iterative Feedback Tuning (IFT) algorithm, which is a classical and also popular data-driven algorithm, on tower crane systems. The IFT algorithm is applied to improve the performances of three proportional-integral (PI) controllers by solving an optimization problem. The IFT algorithm is validated using experiments to control the cart position, the arm angular position and the payload position. The experiments are conducted on a tower crane system in authors’ laboratory, and the experimental setup is laboratory equipment that has strong nonlinearities. Ten iterations are performed to highlight the control system performance using IFT controllers.

关键词： Iterative Feedback Tuning control algorithms tower crane systems

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Robust and Reliable Liveness Detection Models for Facial Recognition Systems

Robust and Reliable Liveness Detection Models for Facial Rec...

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Frontiers of Information Technology (FIT)

作者： Haris Anjum Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Abu Bakar Siddique Muhammad Imad Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain

Facial Liveness Detection is instrumental in combating fraudulent practices and identity theft by differentiating genuine faces from forgeries. Given that facial recognition is now an integral part of many sectors like banking and law enforcement, liveness detection has become a vital aspect for maintaining the trustworthiness of these applications. Unfortunately, the utility of prevalent facial liveness detection models is constrained by their lack of generalizability to novel, unseen data, which presents a substantial impediment to practical applications. To mitigate this issue, our research endeavored to construct a reliable and precise facial liveness detection model that can effectively navigate this challenge and ensure consistent performance across diverse datasets. The devised model exhibited excellent accuracy, which attests to its robustness. This research underscores the advantages of employing deep learning methods such as Convolutional Neural Networks (CNNs) as they significantly enhance the model’s accuracy. We intend for our findings to drive further research and progress in this critical field, by laying the groundwork for future work that integrates multiple liveness detection methods and collects more varied training data to develop an even more resilient and efficient system.

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Tracking Fluorescent Artificial Features on Cardiac Surface for Evaluating Heart Function

Tracking Fluorescent Artificial Features on Cardiac Surface ...

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IEEE Instrumentation and Measurement Technology Conference

作者： Chi Hung Hwang RongQing Qiu Chun-Wei Lai Yu-Jen Chen Yu-Hsin Yang Hsin-Ping Peng Yen-Pei Lu Rui-Cian Weng Taiwan Instrumrnt Research Institute National Applied Research Lab. Hsinchu Taiwan Department of Power Mechanical Eng. National Tsing Hua University Hsinchu Taiwan Dept. of Computer Science and Information Eng. National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

This study proposes a solution for accessing the heart functions after cardiac surgery by integrating a self- developed NIR imaging system and optical flow method to track the NIR fluorescent artificial features on the cardiac surface. An animal test is performed to evaluate the imaging system and the possible approaches to extract heart performance from the features. This study adopted two approaches: the movement of a single cardiac surface feature point and the evaluation of distance changes of two feature points, which have been implemented for analyzing the features. The results of tracking the movement of a single cardiac surface feature point indicate the heartbeat and respiratory rates can be successfully determined, and an irregular myocardium shrink has been revealed. As for adopting the distance changes of two feature points, results show that respiratory-introduced motion disturbance can be suppressed, and the heartbeat rates can be determined.

关键词： Surface reconstruction Three-dimensional displays Tracking Heart beat Animals Surgery Fluorescence

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Modelling and Mining Brain Network Data for Diagnosis of Neurodegenerative Diseases 6

Modelling and Mining Brain Network Data for Diagnosis of Neu...

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6th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2020

作者： Madhushree, B.A. Gangadhar, Nandyala D. Prafulla Kumari, K.S. M. S. Ramaiah University of Applied Sciences Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781728168289

Connectomes are brain networks represented as a graph with the vertices being the regions of the brain and weighted edges representing strength of connections between the regions inferred from brain imaging techniques such a Functional MRI (fMRI). An intense research activity is to use the connectomes to identify markers for brain disorders, especially neuro-degenerative diseases such as Autism Spectrum Disorder (ASD) by studying the differences in the connectomes of healthy subjects and patients. This paper presents a novel data model for the connectome data and analyzes its efficacy in the classification of ASD and Typically Developing (TD) (healthy) connectomes. The proposed data modelling begins by clustering the vertices (brain regions) using the Graph Spectral Clustering into fixed number of clusters, the number of clusters chosen as four based on the empirical evidence. The resulting clustering is used to map the vertices into a binary matrix which is then converted into a binary row vector to form a vector space model of the connectome data that is employed to classify the connectome data. The developed model is first validated using Human Connectome Protocol (HCP) FMRI derived connectome data of 812 healthy patents. Binary data models of the UCLA Autism dataset with fMRI and DTI scans of 42 ASD and 37 TD subjects are generated and employed for their classification. Different classification algorithms are trained, tested and their performance evaluated using the resulting dataset. Cross Validation (CV) estimates identified the best performance (83% recall and 83% precision) for DTI data and (73% recall and 89% precision) for the fMRI data achieved using Logistic Regression. © 2020 IEEE.

关键词： Magnetic resonance imaging

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A Novel Machine Learning Approach for Fast and Efficient Detection of Mango Leaf Diseases

A Novel Machine Learning Approach for Fast and Efficient Det...

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Computing and Machine Intelligence (ICMI), International Conference on

作者： S M Masfequier Rahman Swapno Sm Nuruzzaman Nobel Md Babul Islam R. Haque V. P. Meena F. Benedetto Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Department of EEE Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Economics Dept SP4TE Laboratory University of ROMA TRE Italy

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Monitoring plantation diseases is a fundamental issue for the agricultural industry. Early diagnosis of plant illnesses dramatically affects food quantity and quality. Here we focus on the case of Mangoes, whose demand is very high. To maximize earnings, mango plant diseases must be managed well. Manual mango leaf disease diagnosis is impractical in today's computerized world due to its high cost, shortage of mango experts, and variable symptoms. In this work, we provide a novel machine Learning-based mango leaf disease classification method. We build a reliable categorization system using 4000 photographs from the Kaggle dataset grouped into 8 sickness categories. Our model reaches a remarkable accuracy of 96.87% throughout the training phase, and an accuracy rate of 93.12% during the validation phase. Finally, using previously unknown data, our model demonstrates an exceptional testing accuracy rate of 94.99 %. Our solution outperforms other existing machine learning detection methods, thus proving to be very effective in enhancing the categorization of Mango Leaf disease. and, more generally, offering improved and enduring food security and safety in the future.

关键词： Training Plant diseases Accuracy Plantations Machine learning Safety Reliability

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Optimal Polynomial Approximation to Rational Matrix Functions Using the Arnoldi Algorithm

arXiv

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arXiv 2023年

作者： Chen, Tyler Greenbaum, Anne Wellen, Natalie NYU Courant Department of Mathematics 251 Mercer St. New YorkNY10003 United States NYU Tandon Department of Computer Science and Engineering 370 Jay St. New YorkNY11201 United States University of Washington Applied Math Dept. Box 353925 SeattleWA98195 United States

Given an n by n matrix A and an n-vector b, along with a rational function R(z):= D(z)−1N (z), we show how to find the optimal approximation to R(A)b from the Krylov space, span(b, Ab, . . ., Ak−1b), using the basis vectors produced by the Arnoldi algorithm. To find this optimal approximation requires running max{deg(D), deg(N)} − 1 extra Arnoldi steps and solving a k + max{deg(D), deg(N)} by k least squares problem. Here optimal is taken to mean optimal in the D(A)∗D(A)-norm. Similar to the case for linear systems, we show that eigenvalues alone cannot provide information about the convergence behavior of this algorithm and we discuss other possible error bounds for highly nonnormal *** Codes 65F10 (Primary), 65F60, 47A12, 47A25 (Secondary) © 2023, CC BY.

关键词： Rational functions

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