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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

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AB1556 MACHINE LEARNING APPROACH FOR CLASSIFICATION OF ARTHRITIS ACTIVITY STATE, USING DATA FROM A SINGLE ACCELEROMETER

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Annals of the Rheumatic Diseases 2023年 82卷 2012-2012页

作者： P. Mielnik A. Myhre Hjelle A. Traseth K. Tokarz B. Pollen M. Fojcik Helse Forde Section for Rheumatology； Department for Neurology Rheumatology and Physical Medicine Forde Norway Institute of Informatics Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland Western Norway University of Applied Sciences Faculty of Engineering and Science Forde Norway

Background Machine learning (ML) potential is not fully exploited in diagnostics and follow up of autoimmune inflammatory rheumatic disease (AIIRD). It is despite the broader use of ML in, e.g. imaging diagnostics. The specific tools for AIIRD are lacking. Objectives This is an interim analysis of data from the first checkpoint of the proof-of-concept study on using accelerometer (ACC) data in follow up of patients with AIIRD. The main goal of the study was to investigate the value of single ACC data in the classification of arthritis activity status. Methods Subjects with AIIRD are enrolled in the study when they start of new treatment due to the disease activity. Several comorbidities, such as severe neurological and cardiological disorders, as wells as impaired mobility, are part of the protocol exclusion criteria. Volunteers without AIIRD who fulfil other inclusion/exclusion criteria are included as controls. The study was approved by local competent authorities and informed consent was given by all participants. We used data collected up till the first study checkpoint. This analysis covers nine patients with AIIRD (5 rheumatoid and four psoriasis arthritis) and 13 controls. Five patients had 3 study visits, 1 had 2 visits and 3 only one. Controls have only one visit per protocol. We analysed ACC data from 3 minutes of clapping using a home-brewed device based on Arduino nano 33 BLE with 6-axis MTU by Nordic Semiconductor. Data was divided into 6-second chunks that were found optimal in our prior study. We conducted binary classification between any/ no arthritis in any of the upper extremities. We used accuracy and area under curve (AUC) as an efficacy function derived from the receiver operating characteristic curve (ROC). We extracted 54 features from 3 ACC axis signal. The features encompassed, among others, Fourier's components, autoregression coefficients, median absolute deviation (MAD), variance, Fourier's entropy, etc. We built linear discriminant anal

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Force Field-Based Indirect Manipulation Of UAV Flight Trajectories*

Force Field-Based Indirect Manipulation Of UAV Flight Trajec...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Werner Alexander Isop Friedrich Fraundorfer Department of Computer Science Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

For a variety of applications remote navigation of an unmanned aerial vehicle (UAV) along a flight trajectory is an essential task. For instance, during search and rescue missions in outdoor scenes, an important goal is to ensure safe navigation. Assessed by the remote operator, this could mean avoiding collisions with obstacles, but moreover avoiding hazardous flight areas. State of the art approaches enable navigation along trajectories, but do not allow for indirect manipulation during motion. In addition, they suggest to use egocentric views which could limit understanding of the remote scene. With this work we introduce a novel indirect manipulation method, based on gravitational law, to recover safe navigation in the presence of hazardous flight areas. The indirect character of our method supports manipulation at far distances where common direct manipulation methods typically fail. We combine it with an immersive exocentric view to improve understanding of the scene. We designed three flavors of our method and compared them during a user study in a simulated scene. While with this method we present a first step towards a more extensive navigation interface, as future work we plan experiments in dynamic real-world scenes.

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Sparse inpainting with smoothed particle hydrodynamics

arXiv

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arXiv 2020年

作者： Daropoulos, Viktor Augustin, Matthias Weickert, Joachim Interactive Graphics and Simulation Group Department of Computer Science Universität Innsbruck Technikerstraße 21a Innsbruck6020 Austria Mathematical Image Analysis Group Faculty of Mathematics and Computer Science Saarland University Campus E1.7 Saarbrücken66041 Germany

Digital image inpainting refers to techniques used to reconstruct a damaged or incomplete image by exploiting available image information. The main goal of this work is to perform the image inpainting process from a set of sparsely distributed image samples with the Smoothed Particle Hydrodynamics (SPH) technique. As, in its naive formulation, the SPH technique is not even capable of reproducing constant functions, we modify the approach to obtain an approximation which can reproduce constant and linear functions. Furthermore, we examine the use of Voronoi tessellation for defining the necessary parameters in the SPH method as well as selecting optimally located image samples. In addition to this spatial optimization, optimization of data values is also implemented in order to further improve the results. Apart from a traditional Gaussian smoothing kernel, we assess the performance of other kernels on both random and spatially optimized masks. Since the use of isotropic smoothing kernels is not optimal in the presence of objects with a clear preferred orientation in the image, we also examine anisotropic smoothing kernels. Our final algorithm can compete with well-performing sparse inpainting techniques based on homogeneous or anisotropic diffusion processes as well as with exemplar-based *** Codes 68U10 (Primary) 65D18, 76M28, 94A08 (Secondary) Copyright © 2020, The Authors. All rights reserved.

关键词： Hydrodynamics

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Total Variation-Based Image Decomposition and Denoising for Microscopy Images

arXiv

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arXiv 2025年

作者： Corrias, Marco Franceschi, Giada Riva, Michele Tampieri, Alberto Föttinger, Karin Diebold, Ulrike Pock, Thomas Franchini, Cesare Faculty of Physics Center for Computational Materials Science University of Vienna Austria Vienna Doctoral School in Physics University of Vienna Austria Institute of Applied Physics TU Wien Austria Institute of Materials Chemistry TU Wien Austria Institute of Computer Graphics and Vision TU Graz Austria Department of Physics and Astronomy University of Bologna Italy

Experimentally acquired microscopy images are unavoidably affected by the presence of noise and other unwanted signals, which degrade their quality and might hide relevant features. With the recent increase in image acquisition rate, modern denoising and restoration solutions become necessary. This study focuses on image decomposition and denoising of microscopy images through a workflow based on total variation (TV), addressing images obtained from various microscopy techniques, including atomic force microscopy (AFM), scanning tunneling microscopy (STM), and scanning electron microscopy (SEM). Our approach consists in restoring an image by extracting its unwanted signal components and subtracting them from the raw one, or by denoising it. We evaluate the performance of TV-L1, Huber-ROF, and TGV-L1 in achieving this goal in distinct study cases. Huber-ROF proved to be the most flexible one, while TGV-L1 is the most suitable for denoising. Our results suggest a wider applicability of this method in microscopy, restricted not only to STM, AFM, and SEM images. The Python code used for this study is publicly available as part of AiSurf. It is designed to be integrated into experimental workflows for image acquisition or can be used to denoise previously acquired images. © 2025, CC BY.

关键词： Atomic force microscopy

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SHREC'19 track: Extended 2D scene image-based 3D scene retrieval 12

SHREC'19 track: Extended 2D scene image-based 3D scene retri...

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12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual Conference of the European Association for computer graphics, EG 2019

作者： Abdul-Rashid, Hameed Yuan, Juefei Li, Bo Lu, Yijuan Schreck, Tobias Bui, Ngoc-Minh Do, Trong-Le Holenderski, Mike Jarnikov, Dmitri Le, Khiem T. Menkovski, Vlado Nguyen, Khac-Tuan Nguyen, Thanh-An Nguyen, Vinh-Tiep Ninh, Tu V. Rey, Perez Tran, Minh-Triet Wang, Tianyang School of Computer Science and Computer Engineering University of Southern Mississippi United States Department of Computer Science Texas State University United States Institue of Computer Graphics and Knowledge Visualization Graz University of Technology Austria Faculty of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Software Engineering Lab Vietnam National University Ho Chi Minh City Viet Nam Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands University of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Department of Computer Science and Information Technology Austin Peay State University United States

ISBN: (纸本)9783038680772

In the months following our SHREC 2018 - 2D Scene Image-Based 3D Scene Retrieval (SceneIBR2018) track, we have extended the number of the scene categories from the initial 10 classes in the SceneIBR2018 benchmark to 30 classes, resulting in a new benchmark SceneIBR2019 which has 30,000 scene images and 3,000 3D scene models. For that reason, we seek to further evaluate the performance of existing and new 2D scene image-based 3D scene retrieval algorithms using this extended and more comprehensive new benchmark. Three groups from the Netherlands, The United States and Vietnam participated and collectively submitted eight runs. This report documents the evaluation of each method based on seven performance metrics, offers an in-depth discussion as well as analysis on the methods employed and discusses future directions that have the potential to address this task. Again, deep learning techniques have demonstrated notable performance in terms of both accuracy and scalability when applied to this exigent retrieval task. To further enrich the current state of 3D scene understanding and retrieval, our evaluation toolkit, all participating methods' results and the comprehensive 2D/3D benchmark have all been made publicly available. © 2019 The Author(s)

关键词： Three dimensional computer graphics

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Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries

Improving Surgical Training Phantoms by Hyperrealism: Deep U...

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Workshop on Bildverarbeitung fur die Medizin, 2019

作者： Engelhardt, Sandy De Simone, Raffaele Full, Peter M. Karck, Matthias Wolf, Ivo Faculty of Computer Science Mannheim University of Applied Sciences Mannheim Germany Department of Cardiac Surgery Heidelberg University Hospital Heidelberg Germany Dep. of and Graphics Magdeburg University Magdeburg Germany

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Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance

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Journal of Shanghai Jiaotong university(science) 2018年第5期23卷 636-642页

作者： Shaochun PANG Yijie SHAO Sen JIANG Keyuan School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Department of Computer Information Technology ＆ Graphics Purdue University NorthwestHammond 46323USA

This paper presents an advanced fuzzy C-means(FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines the distance with density and improves the objective function so that the performance of the algorithm can be improved. The experimental results show that the proposed FCM algorithm requires fewer iterations yet provides higher accuracy than the traditional FCM algorithm. The advanced algorithm is applied to the influence of stars' box-office data, and the classification accuracy of the first class stars achieves 92.625%.

关键词： objective function clustering center fuzzy C-means （FCM） clustering algorithm degree of member-ship

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Progressive Refinement Imaging

arXiv

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arXiv 2019年

作者： Kluge, M. Weyrich, T. Kolb, A. Computer Graphics Group University of Siegen Germany Department of Computer Science University College London United Kingdom

This paper presents a novel technique for progressive online integration of uncalibrated image sequences with substantial geometric and/or photometric discrepancies into a single, geometrically and photometrically consistent image. Our approach can handle large sets of images, acquired from a nearly planar or infinitely distant scene at different resolutions in object domain and under variable local or global illumination conditions. It allows for efficient user guidance as its progressive nature provides a valid and consistent reconstruction at any moment during the online refinement process. Our approach avoids global optimization techniques, as commonly used in the field of image refinement, and progressively incorporates new imagery into a dynamically extendable and memory-efficient Laplacian pyramid. Our image registration process includes a coarse homography and a local refinement stage using optical flow. Photometric consistency is achieved by retaining the photometric intensities given in a reference image, while it is being refined. Globally blurred imagery and local geometric inconsistencies due to, e.g. motion are detected and removed prior to image fusion. We demonstrate the quality and robustness of our approach using several image and video sequences, including handheld acquisition with mobile phones and zooming sequences with consumer cameras. Copyright © 2019, The Authors. All rights reserved.

关键词： Photometry

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Generative Adversarial Network with Policy Gradient for Text Summarization

Generative Adversarial Network with Policy Gradient for Text...

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International Conference on Semantic Computing

作者： Banafsheh Rekabdar Christos Mousas Bidyut Gupta Department of Computer Science Southern Illinois University Carbondale IL Computer Graphics Technology Purdue University West Lafayette IN

Abstractive text summarization is the task of generating meaningful summary from a given document (short or long). This is a very challenging task for longer documents, since they suffer from repetitions (redundancy) when the given document is long and the generated summary should contain multi-sentences. In this paper we present an approach for applying generative adversarial networks in abstractive text summarization tasks with a novel time-decay attention mechanism. The data generator is modeled as a stochastic policy in reinforcement learning. The generator's goal is to generate summaries which are difficult to be discriminated from real summaries. The discriminator aims to estimate the probability that a summary came from the training data rather than the generator to guide the training of the generative model. This framework corresponds to a minimax two-player game. Qualitatively and quantitatively experimental results (human evaluations and ROUGE scores) show that our model can generate more relevant, less repetitive, grammatically correct, preferable by humans and is promising in solving the abstractive text summarization task.

关键词： Generators Decoding Task analysis Generative adversarial networks Reinforcement learning Training Mathematical model

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