检索结果-内蒙古大学图书馆

Light steering projection systems and attributes for HDR displays

学校读者我要写书评

暂无评论

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

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

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

学校读者我要写书评

暂无评论

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

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

End-to-end, single-stream temporal action detection in untrimmed videos 28

学校读者我要写书评

暂无评论

End-to-end, single-stream temporal action detection in untri...

28th British Machine Vision Conference, BMVC 2017

作者： Buch, Shyamal Escorcia, Victor Ghanem, Bernard Fei-Fei, Li Niebles, Juan Carlos Stanford Vision and Learning Lab. Dept. of Computer Science Stanford University United States Image and Video Understanding Lab. Visual Computing Center KAUST Saudi Arabia

ISBN: (纸本)190172560X

In this work, we present a new intuitive, end-to-end approach for temporal action detection in untrimmed videos. We introduce our new architecture for Single-Stream Temporal Action Detection (SS-TAD), which effectively integrates joint action detection with its semantic sub-tasks in a single unifying end-to-end framework. We develop a method for training our deep recurrent architecture based on enforcing semantic constraints on intermediate modules that are gradually relaxed as learning progresses. We find that such a dynamic learning scheme enables SS-TAD to achieve higher overall detection performance, with fewer training epochs. By design, our single-pass network is very efficient and can operate at 701 frames per second, while simultaneously outperforming the state-of-the-art methods for temporal action detection on THUMOS’14. © 2017. The copyright of this document resides with its authors.

关键词： Semantics

Deep learning on image denoising: An overview

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Tian, Chunwei Fei, Lunke Zheng, Wenxian Xu, Yong Zuo, Wangmeng Lin, Chia-Wen Bio-Computing Research Center Harbin Institute of Technology Shenzhen Shenzhen Guangdong518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen Guangdong518055 China School of Computers Guangdong University of Technology Guangzhou Guangdong510006 China Tsinghua Shenzhen International Graduate School Shenzhen Guangdong518055 China Peng Cheng Laboratory Shenzhen Guangdong518055 China School of Computer Science and Technology Harbin Institute of Technology Harbin Heilongjiang150001 China Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan

Deep learning techniques have obtained much attention in image denoising. However, deep learning methods of different types deal with the noise have enormous differences. Specifically, discriminative learning based on deep learning can well address the Gaussian noise. Optimization model methods based on deep learning have good effect on estimating of the real noise. So far, there are little related researches to summarize different deep learning techniques for image denoising. In this paper, we make such a comparative study of different deep techniques in image denoising. We first classify the (1) deep convolutional neural networks (CNNs) for additive white noisy images, (2) deep CNNs for real noisy images, (3) deep CNNs for blind denoising and (4) deep CNNs for hybrid noisy images, which is the combination of noisy, blurred and low-resolution images. Then, we analyze the motivations and principles of deep learning methods of different types. Next, we compare and verify the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analysis. Finally, we point out some potential challenges and directions of future research. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

A two-stage subspace trust region approach for deep neural network training

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Dudar, Viacheslav Chierchia, Giovanni Chouzenoux, Emilie Pesquet, Jean-Christophe Semenov, Vladimir Taras Shevchenko National University of Kyiv Faculty of Computer Science and Cybernetics Ukraine Université Paris Est LIGM UMR 8049 CNRS ENPC ESIEE Paris UPEM Noisy-le-GrandF-93162 France Center for Visual Computing CentraleSupelec University Paris-Saclay Chatenay-Malabry France

—In this paper, we develop a novel second-order method for training feed-forward neural nets. At each iteration, we construct a quadratic approximation to the cost function in a low-dimensional subspace. We minimize this approximation inside a trust region through a two-stage procedure: first inside the embedded positive curvature subspace, followed by a gradient descent step. This approach leads to a fast objective function decay, prevents convergence to saddle points, and alleviates the need for manually tuning parameters. We show the good performance of the proposed algorithm on benchmark datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Gradient methods

A probabilistic incremental proximal gradient method

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Akyildiz, Ömer Deniz Chouzenoux, Émilie Elvira, Víctor Míguez, Joaquín Dept. of Computer Science Dept. of Statistics University of Warwick Alan Turing Institute London United Kingdom Center for Visual Computing INRIA Saclay CentraleSupélec Gif-sur-Yvette France Villeneuve d’Ascq France Dept. of Signal Theory and Communications Universidad Carlos III de Madrid Leganés28912 Spain

In this paper, we propose a probabilistic optimization method, named probabilistic incremental proximal gradient (PIPG) method, by developing a probabilistic interpretation of the incremental proximal gradient algorithm. We explicitly model the update rules of the incremental proximal gradient method and develop a systematic approach to propagate the uncertainty of the solution estimate over iterations. The PIPG algorithm takes the form of Bayesian filtering updates for a state-space model constructed by using the cost function. Our framework makes it possible to utilize well-known exact or approximate Bayesian filters, such as Kalman or extended Kalman filters, to solve large-scale regularized optimization problems. Copyright © 2018, The Authors. All rights reserved.

关键词： Gradient methods

A two-stage subspace trust region approach for deep neural network training

学校读者我要写书评

暂无评论

A two-stage subspace trust region approach for deep neural n...

European Signal Processing Conference (EUSIPCO)

作者： Viacheslav Dudar Giovanni Chierchia Emilie Chouzenoux Jean-Christophe Pesquet Vladimir Semenov Faculty of Computer Science and Cybernetics Taras Shevchenko National University of Kyiv Ukraine Universite Paris Est UPEM Noisy-le-Grand France Center for Visual Computing University Paris-Saclay Chatenay-Malabry France Center for Visual Computing CentraleSupelec University Paris-Saclay Chatenay-Malabry France

In this paper, we develop a novel second-order method for training feed-forward neural nets. At each iteration, we construct a quadratic approximation to the cost function in a low-dimensional subspace. We minimize this approximation inside a trust region through a two-stage procedure: first inside the embedded positive curvature subspace, followed by a gradient descent step. This approach leads to a fast objective function decay, prevents convergence to saddle points, and alleviates the need for manually tuning parameters. We show the good performance of the proposed algorithm on benchmark datasets.

关键词： Training Neural networks Signal processing algorithms Optimization Eigenvalues and eigenfunctions Europe Signal processing

Automatic image cropping for visual aesthetic enhancement using deep neural networks and cascaded regression

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Guo, Guanjun Wang, Hanzi Shen, Chunhua Yan, Yan Liao, Hong-Yuan Mark Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University Xiamen Fujian361005 China Australian Center for Visual Technologies School of Computer Science University of Adelaide SA5005 Australia Institute of Information Science Academia Sinica Taipei115 Taiwan

Despite recent progress, computational visual aesthetic is still challenging. Image cropping, which refers to the removal of unwanted scene areas, is an important step to improve the aesthetic quality of an image. However, it is challenging to evaluate whether cropping leads to aesthetically pleasing results because the assessment is typically subjective. In this paper, we propose a novel cascaded cropping regression (CCR) method to perform image cropping by learning the knowledge from professional photographers. The proposed CCR method improves the convergence speed of the cascaded method, which directly uses random-ferns regressors. In addition, a two-step learning strategy is proposed and used in the CCR method to address the problem of lacking labelled cropping data. Specifically, a deep convolutional neural network (CNN) classifier is first trained on large-scale visual aesthetic datasets. The deep CNN model is then designed to extract features from several image cropping datasets, upon which the cropping bounding boxes are predicted by the proposed CCR method. Experimental results on public image cropping datasets demonstrate that the proposed method significantly outperforms several state-of-the-art image cropping methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Regression analysis

A novel dual ascent algorithm for solving the min-cost flow problem 18

学校读者我要写书评

暂无评论

A novel dual ascent algorithm for solving the min-cost flow ...

18th Workshop on Algorithm Engineering and Experiments 2016, ALENEX 2016

作者： Becker, Ruben Fickert, Maximilian Karrenbauer, Andreas Max Planck Institute for Informatics Saarbriicken Germany Max Planck Center for Visual Computing and Communication Saarbriicken Germany Saarbriicken Graduate School of Computer Science Saarbriicken Germany

ISBN: (纸本)9781510819689

We present a novel alg orithm for the min-cost flow problem that is competitive with recent third-party implementations of well-known algorithms for this problem and even outperforms them on certain realistic instances. We formally prove correctness of our algorithm and show that the worst-case running time is in 0(||β||i(m + ηlogη)) where b is the vector of demands. Combined with standard scaling techniques, this pseudo-polynomial bound can be made polynomial in a straightforward way. Furthermore, we evaluate our approach experimentally. Our empirical findings indeed suggest that the running time does not significantly depend on the costs and that a linear dependence on ||β||i is overly pessimistic.

关键词： Cost accounting