Results 341 to 350 of about 1,634,168 (383)
Some of the next articles are maybe not open access.
Hierarchical Cross-Modal Talking Face Generation With Dynamic Pixel-Wise Loss
Computer Vision and Pattern Recognition, 2019We devise a cascade GAN approach to generate talking face video, which is robust to different face shapes, view angles, facial characteristics, and noisy audio conditions.
Lele Chen +3 more
semanticscholar +1 more source
Hyperspectral Image Classification Using Deep Pixel-Pair Features
IEEE Transactions on Geoscience and Remote Sensing, 2017Wei Li, Fan Zhang, Qian Du
exaly +2 more sources
Defocus Deblurring Using Dual-Pixel Data
European Conference on Computer Vision, 2020Defocus blur arises in images that are captured with a shallow depth of field due to the use of a wide aperture. Correcting defocus blur is challenging because the blur is spatially varying and difficult to estimate.
Abdullah Abuolaim, M. S. Brown
semanticscholar +1 more source
Comput. Aided Civ. Infrastructure Eng., 2019
Deep learning‐based structural damage detection methods overcome the limitation of inferior adaptability caused by extensively varying real‐world situations (e.g., lighting and shadow changes).
Shengyuan Li, Xuefeng Zhao, Guangyi Zhou
semanticscholar +1 more source
Deep learning‐based structural damage detection methods overcome the limitation of inferior adaptability caused by extensively varying real‐world situations (e.g., lighting and shadow changes).
Shengyuan Li, Xuefeng Zhao, Guangyi Zhou
semanticscholar +1 more source
IEEE Transactions on Geoscience and Remote Sensing, 2020
Recently, the graph convolutional network (GCN) has drawn increasing attention in the hyperspectral image (HSI) classification. Compared with the convolutional neural network (CNN) with fixed square kernels, GCN can explicitly utilize the correlation ...
Qichao Liu +3 more
semanticscholar +1 more source
Recently, the graph convolutional network (GCN) has drawn increasing attention in the hyperspectral image (HSI) classification. Compared with the convolutional neural network (CNN) with fixed square kernels, GCN can explicitly utilize the correlation ...
Qichao Liu +3 more
semanticscholar +1 more source
Guided Collaborative Training for Pixel-wise Semi-Supervised Learning
European Conference on Computer Vision, 2020We investigate the generalization of semi-supervised learning (SSL) to diverse pixel-wise tasks. Although SSL methods have achieved impressive results in image classification, the performances of applying them to pixel-wise tasks are unsatisfactory due ...
Zhanghan Ke +4 more
semanticscholar +1 more source
Range From Focus Pixel-by -Pixel
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005We describe a nd demonstrate an ambient lllumlnation method for range Imaging In parallel with and using the same sensor that is used for Intensity Imaging. Our method automates the well known "range-from-focus" method. It makes use of our reaiizatlon that range can be calculated directly from a focus error signal: It is not necessary to actually focus
M.W. Siegel, M.L. Leary
openaire +1 more source
ACS Nano, 2019
Information display utilizing plasmonic color generation has recently emerged as an alternative paradigm to traditional printing and display technologies. However, many implementations so far have either presented static pixels with a single display state or rely on relatively slow switching mechanisms such as chemical transformations or liquid crystal
Nicholas J. Greybush +9 more
openaire +2 more sources
Information display utilizing plasmonic color generation has recently emerged as an alternative paradigm to traditional printing and display technologies. However, many implementations so far have either presented static pixels with a single display state or rely on relatively slow switching mechanisms such as chemical transformations or liquid crystal
Nicholas J. Greybush +9 more
openaire +2 more sources
ACM SIGGRAPH 2011 Talks, 2011
We present a new video mode for television sets that we refer to as display pixel caching (DPC). It fills empty borders with spatially and temporally consistent information while preserving the original video format. Unlike related video modes, such as stretching, zooming, and video retargeting, DPC does not scale or stretch individual frames. Instead,
Clemens Birklbauer +4 more
openaire +1 more source
We present a new video mode for television sets that we refer to as display pixel caching (DPC). It fills empty borders with spatially and temporally consistent information while preserving the original video format. Unlike related video modes, such as stretching, zooming, and video retargeting, DPC does not scale or stretch individual frames. Instead,
Clemens Birklbauer +4 more
openaire +1 more source