检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Tian, Chunwei Zhuge, Ruibin Wu, Zhihao Xu, Yong Zuo, Wangmeng Chen, Chen Lin, Chia-Wen Bio-Computing Research Center Harbin Institute of Technology Shenzhen ShenzhenGuangdong518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition ShenzhenGuangdong518055 China Peng Cheng Laboratory ShenzhenGuangdong518055 China School of Computer Science and Technology Harbin Institute of Technology HarbinHeilongjiang150001 China Department of Electrical and Computer Engineering University of North Carolina CharlotteNC28223 United States Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan

Deep convolutional neural networks (CNNs) with strong expressive ability have achieved impressive performances on single image super-resolution (SISR). However, their excessive amounts of convolutions and parameters usually consume high computational cost and more memory storage for training a SR model, which limits their applications to SR with resource-constrained devices in real world. To resolve these problems, we propose a lightweight enhanced SR CNN (LESRCNN) with three successive sub-blocks, an information extraction and enhancement block (IEEB), a reconstruction block (RB) and an information refinement block (IRB). Specifically, the IEEB extracts hierarchical low-resolution (LR) features and aggregates the obtained features step-by-step to increase the memory ability of the shallow layers on deep layers for SISR. To remove redundant information obtained, a heterogeneous architecture is adopted in the IEEB. After that, the RB converts low-frequency features into high-frequency features by fusing global and local features, which is complementary with the IEEB in tackling the long-term dependency problem. Finally, the IRB uses coarse high-frequency features from the RB to learn more accurate SR features and construct a SR image. The proposed LESRCNN can obtain a high-quality image by a model for different scales. Extensive experiments demonstrate that the proposed LESRCNN outperforms state-of-the-arts on SISR in terms of qualitative and quantitative evaluation. The code of LESRCNN is accessible on https://***/hellloxiaotian/LESRCNN. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Single image reflection removal via learning with multi-image constraints

arXiv

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arXiv 2019年

作者： Yin, Yingda Fan, Qingnan Chen, Dongdong Wang, Yujie Aviles-Rivero, Angelica I. Li, Ruoteng Schönlieb, Carola-Bibiane Chen, Baoquan The Center on Frontiers of Computing Studies Peking University Beijing100871 China The Visual Computing Center of Tencent AI Lab Tencent Shenzhen518000 China The Computer Science and Technology Department Shandong University Shandong Qingdao266237 China Microsoft Cloud AI RedmondWA98052 United States The DAMTP and DPMMS University of Cambridge United Kingdom The Electrical and Computer Engineering Department National University of Singapore Singapore

Reflections are very common phenomena in our daily photography, which distract people's attention from the scene behind the glass. The problem of removing reflection artifacts is important but challenging due to its ill-posed nature. The traditional approaches solve an optimization problem over the constraints induced from multiple images, at the expense of large computation costs. Recent learning-based approaches have demonstrated a significant improvement in both performance and running time for single image reflection removal, but are limited as they require a large number of synthetic reflection/clean image pairs for direct supervision to approximate the ground truth, at the risk of overfitting in the synthetic image domain and degrading in the real image domain. In this paper, we propose a novel learning-based solution that combines the advantages of the aforementioned approaches and overcomes their drawbacks. Our algorithm works by learning a deep neural network to optimize the target with joint constraints enhanced among multiple input images during the training phase, but is able to eliminate reflections only from a single input for evaluation. Our algorithm runs in real-time and achieves state-of-the-art reflection removal performance on real images. We further propose a strong network backbone that disentangles the background and reflection information into separate latent codes, which are embedded into a shared one-branch deep neural network for both background and reflection predictions. The proposed backbone experimentally performs better than the other common network implementations, and provides insightful knowledge to understand the reflection removal task. Copyright © 2019, The Authors. All rights reserved.

关键词： Image enhancement

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Designing and Training of A Dual CNN for Image Denoising

arXiv

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arXiv 2020年

作者： Zuo, Wangmeng Zhang, David Lin, Chia-Wen Xu, Yong Tian, Chunwei Du, Bo Bio-Computing Research Center Harbin Institute of Technology Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition ShenzhenGuangdong518055 China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science and Technology Harbin Institute of Technology 150001 HarbinHeilongjiang China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science Wuhan University 430072 WuhanHubei China Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan 518172 ShenzhenGuangdong China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Deep convolutional neural networks (CNNs) for image denoising have recently attracted increasing research interest. However, plain networks cannot recover fine details for a complex task, such as real noisy images. In this paper, we propose a Dual denoising Network (DudeNet) to recover a clean image. Specifically, DudeNet consists of four modules: a feature extraction block, an enhancement block, a compression block, and a reconstruction block. The feature extraction block with a sparse mechanism extracts global and local features via two sub-networks. The enhancement block gathers and fuses the global and local features to provide complementary information for the latter network. The compression block refines the extracted information and compresses the network. Finally, the reconstruction block is utilized to reconstruct a denoised image. The DudeNet has the following advantages: (1) The dual networks with a parse mechanism can extract complementary features to enhance the generalized ability of denoiser. (2) Fusing global and local features can extract salient features to recover fine details for complex noisy images. (3) A Small-size filter is used to reduce the complexity of denoiser. Extensive experiments demonstrate the superiority of DudeNet over existing current state-of-the-art denoising methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Recovery

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Correction: Comparative assessment of breast volume using a smartphone device versus MRI

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Breast cancer (Tokyo, Japan) 2025年第3期32卷 615页

作者： Annika S Behrens Hanna Huebner Lothar Häberle Marc Stamminger Daniel Zint Felix Heindl Julius Emons Carolin C Hack Naiba Nabieva Michael Uder Matthias Wetzl Marius Wunderle Matthias W Beckmann Peter A Fasching Sabine Ohlmeyer Department of Gynecology and Obstetrics Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN) Erlangen University Hospital Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany. annika.behrens@uk-erlangen.de. Department of Gynecology Erlangen University Hospital University Breast Center for Franconia Comprehensive Cancer Center Erlangen European Metropolitan Area Nuremberg (CCC ER-EMN) Friedrich Alexander University of Erlangen-Nuremberg Unversitätsstrasse 21-23 91054 Erlangen Germany. annika.behrens@uk-erlangen.de. Department of Gynecology and Obstetrics Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN) Erlangen University Hospital Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany. Biostatistics Unit Department of Gynecology and Obstetrics Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN) Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany. Department of Computer Science Visual Computing Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany. Department of Radiology Erlangen University Hospital Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany.

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Modelling and Simulation of Lily flowers using PDE Surfaces

Modelling and Simulation of Lily flowers using PDE Surfaces

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International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA)

作者： Ehtzaz Chaudhry Jian Chang Hassan Ugail Alexander Malyshev Alfonso Carriazo Andres Iglesias Zulfiqar Habib Allah Bux Sargano Habibollah Haron Algirdas Noreika Lihua You Jian Jun Zhang NCCA Bournemouth University Poole UK Center for Visual Computing University of Bradford Bradford UK Department of Mathematics University of Bergen Bergen Norway Faculty of Mathematics University of Seville Sevilla Spain Department of Applied Mathematics and Computational Sciences University of Cantabria Spain Department of Computer Science COMSATS University Islamabad Lahore Pakistan Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Malaysia Indeform Ltd Kaunas Lithuania

This paper presents a partial differential equation (PDE)-based surface modelling and simulation framework for lily flowers. We use a PDE-based surface modelling technique to represent shape of a lily flower and PDE-based dynamic simulation to animate blossom and decay processes of lily flowers. To this aim, we first automatically construct the geometry of lily flowers from photos to obtain feature curves. Second, we apply a PDE-based surface modelling technique to generate sweeping surfaces to obtain geometric models of the flowers. Then, we use a physics-driven and data-based method and introduce the flower shapes at the initial and final positions into our proposed dynamic deformation model to generate a realistic deformation of flower blossom and decay. The results demonstrate that our proposed technique can create realistic flower models and their movements and shape changes against time efficiently with a small data size.

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Biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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Harnessing multimodal approaches for depression detection using large language models and facial expressions

Npj mental health research

引用

Npj mental health research 2024年第1期3卷 66页

作者： Misha Sadeghi Robert Richer Bernhard Egger Lena Schindler-Gmelch Lydia Helene Rupp Farnaz Rahimi Matthias Berking Bjoern M Eskofier Machine Learning and Data Analytics Lab (MaD Lab) Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany. misha.sadeghi@fau.de. Machine Learning and Data Analytics Lab (MaD Lab) Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany. Chair of Visual Computing (LGDV) Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91058 Germany. Chair of Clinical Psychology and Psychotherapy (KliPs) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany. Translational Digital Health Group Institute of AI for Health Helmholtz Zentrum München - German Research Center for Environmental Health Neuherberg 85764 Germany.

Detecting depression is a critical component of mental health diagnosis, and accurate assessment is essential for effective treatment. This study introduces a novel, fully automated approach to predicting depression severity using the E-DAIC dataset. We employ Large Language Models (LLMs) to extract depression-related indicators from interview transcripts, utilizing the Patient Health Questionnaire-8 (PHQ-8) score to train the prediction model. Additionally, facial data extracted from video frames is integrated with textual data to create a multimodal model for depression severity prediction. We evaluate three approaches: text-based features, facial features, and a combination of both. Our findings show the best results are achieved by enhancing text data with speech quality assessment, with a mean absolute error of 2.85 and root mean square error of 4.02. This study underscores the potential of automated depression detection, showing text-only models as robust and effective while paving the way for multimodal analysis.

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Biasedwalk: Biased sampling for representation learning on graphs

arXiv

引用

arXiv 2018年

作者： Nguyen, Duong Malliaros, Fragkiskos D. Department of Computer Science and Engineering UC San Diego La JollaCA United States Center for Visual Computing CentraleSupélec and Inria Saclay Gif-Sur-Yvette France

Network embedding algorithms are able to learn latent feature representations of nodes, transforming networks into lower dimensional vector representations. Typical key applications, which have effectively been addressed using network embeddings, include link prediction, multilabel classification and community detection. In this paper, we propose BiasedWalk, a scalable, unsupervised feature learning algorithm that is based on biased random walks to sample context information about each node in the network. Our random-walk based sampling can behave as Breath-First-Search (BFS) and Depth-First-Search (DFS) samplings with the goal to capture homophily and role equivalence between the nodes in the network. We have performed a detailed experimental evaluation comparing the performance of the proposed algorithm against various baseline methods, on several datasets and learning tasks. The experiment results show that the proposed method outperforms the baseline ones in most of the tasks and datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Network embeddings

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Light steering projection systems and attributes for HDR displays

Light steering projection systems and attributes for HDR dis...

引用

SID Symposium, Seminar, and Exhibition 2017, Display Week 2017

作者： Damberg, Gerwin Boitard, Ronan Ballestad, Anders Heidrich, Wolfgang Department of Computer Science University of British Columbia Vancouver Canada Visual Computing Center King Abdullah University of Science and Technology Saudi Arabia MTT Innovation Inc. Vancouver Canada

ISBN: (纸本)9781510848757

New light steering projectors in cinema form images by moving light away from dark regions into bright areas of an image. In these systems, the peak luminance of small features can far exceed full screen white luminance. In traditional projectors where light is filtered or blocked in order to give shades of gray (or colors), the peak luminance is fixed. The luminance of chromatic features benefit in the same way as white features, and chromatic image details can be reproduced at high brightness leading to a much wider overall color gamut coverage than previously possible. Projectors of this capability are desired by the I creative community to aid in and enhance storytelling. Furthermore, reduced light source power requirements of light steering projectors provide additional economic and environmental benefits. While the dependency of peak luminance level on (bright) image feature size is new in the digital cinema space, display technologies with identical characteristics such as OLED, LED LCD and Plasma TVs are well established in the home. Similarly, direct view LED walls are popular in events, advertising and architectural markets. To enable consistent color reproduction across devices in today’s content production pipelines, models that describe modem projectors and display attributes need to evolve together with HDR standards and available metadata. This paper is a first step towards rethinking legacy display descriptors such as contrast, peak luminance and color primaries in light of new display technology. We first summarize recent progress in the field of light steering projectors in cinema and then, based on new projector and existing display characteristics propose the inclusion of two simple display attributes: Maximum Average Luminance and Peak (Color) Primary Luminance. We show that the proposed attributes allow a better prediction of content reproducibility on HDR displays. To validate this assertion, we test professional content on a commercial HDR te

关键词： Luminance

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Skin color simulation - Review and analysis of available Monte Carlo-based photon transport simulation models 25

Skin color simulation - Review and analysis of available Mon...

引用

25th Color and Imaging Conference: Color science and Engineering Systems, Technologies, and Applications, CIC 2017

作者： Bauer, Jacob R. Pedersen, Marius Hardeberg, Jon Y. Verdaasdonk, Rudolf NTNU Department of Computer Science Norwegian Colour and Visual Computing Laboratory Gjøvik Norway VU University Medical Center Dept. of Physics and Medical Technology Amsterdam Netherlands

ISBN: (纸本)9780892083282

Optical assessment is a useful tool for non-invasive skin assessment avoiding scarring, time delayed diagnosis, hurting, and inconvenience for patient and practitioner. This has led to wide adaption of digital imaging and other optical technologies in dermatology. Many of these optical technologies lack quantifiability, therefore, the reproduction, comparison or absolute meaning of measurements or images is an open challenge. Monte Carlo simulation for multi-layered turbid media provides an accurate tool for simulating the optical path of photons traversing in the skin and the diffuse spectral reflectance of skin. With this tool at hand the missing link between health metrics and measurable optical phenomena can be provided and it can help to establish optical assessment and digital images as a standard for health monitoring of skin. A number of publicly available simulation codes and several different approaches have been proposed. In this work we give an overview of three Monte Carlo simulation tools and compare the different approaches. Furthermore, we will use Monte Carlo Simulations to generate different spectra based on varying optical properties and use these spectra to generate colour patches to analyse the impact of different optical properties on the resulting RGB colour patches.

关键词： Monte Carlo methods

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