Design and analysis of directional front projection screens

Piovarči, Michal; Wessely, Michael; Jagielski, Michał; Alexa, Marc; Matusik, Wojciech; Didyk, Piotr

doi:10.1016/j.cag.2018.05.010

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…With the rapid development of optoelectronic technology, computer technology and machine vision technology, the resolution and contrast achievable by projectors are gradually improving, and they are widely used in education 1 , audiovisual entertainment 2 , scientific research 3 and cutting-edge military fields 4 . However, in order to achieve the desired resolution and contrast, existing projection systems generally require a high-quality screen 5 placed over the entire projected area, whose surface is usually white flat, specular-free, uniform, and texture-free under ambient lighting. As shown in Figure 1, when the brightness distribution on the projection surface is uneven or there are color textures, the projected image will be distorted and strongly color mixing by the influence of complex brightness and chrominance information.…”

Section: Introductionmentioning

confidence: 99%

Correction of images projected on non-white surfaces based on deep neural network

Wang

Zhang²,

Du³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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When projecting onto a non-white surface, the projected image is distorted or color mixing by complex luminance and chrominance information, which makes the projection result different from the visual perception of the human eye. The purpose of projection image correction is to remove these effects, and traditional solutions usually estimate parameters from the collected projection samples, compute an inverse model of the projection imaging process, and try to fit a correction function. In this paper, a deep neural network-based projection image correction network (PICN) is designed to implicitly learn complex correction functions. PICN consists of a U-shaped backbone network, a convolutional neural network that extracts projected surface features, and a perceptual loss network that optimizes the correction results. Such a structure can not only extract the deep features and surface interference features of the projected image, but also make the corrected projected image more in line with human visual perception. In addition, we built a projector-camera system under the condition of a fixed global illumination environment for verification experiment, and proved the effectiveness of the proposed method by calculating the evaluation metrics of projected images before and after correction.

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Section: Introductionmentioning

confidence: 99%

Correction of images projected on non-white surfaces based on deep neural network

Wang

Zhang²,

Du³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

View full text Add to dashboard Cite

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Foreword to the Special Section on Computational Fabrication

Coros

Mueller

2018

Computers & Graphics

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Wrinkle analysis and optimization of large-scale membrane

Zhao

Yujia

Zhang

2021

J. Phys.: Conf. Ser.

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Wrinkles greatly affect the viewing quality of projection screens. Based on the bifurcation buckling theory, a modified model of screen was established. Various methods were introduced to improve the convergence and accuracy of the model. The causes of wrinkles were analyzed. It is found that the crescent wrinkles on side edges were caused by the free boundary conditions while the divergent wrinkles were resulted from the non-uniform displacement of the bottom edge. The simulation and measured results fit well on wave number, amplitude, wavelength, etc. The screen was optimized according to the analysis results and a perfect wrinkle-free screen was achieved after optimization. The method in this paper is proved applicable, accurate and convenient, which can greatly save time and cost for engineering design of large-scale wrinkle-free membrane.

show abstract

Design and analysis of directional front projection screens

Cited by 3 publications

References 28 publications

Correction of images projected on non-white surfaces based on deep neural network

Correction of images projected on non-white surfaces based on deep neural network

Foreword to the Special Section on Computational Fabrication

Wrinkle analysis and optimization of large-scale membrane

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