2,538 research outputs found
Compressed Video Action Recognition
Training robust deep video representations has proven to be much more
challenging than learning deep image representations. This is in part due to
the enormous size of raw video streams and the high temporal redundancy; the
true and interesting signal is often drowned in too much irrelevant data.
Motivated by that the superfluous information can be reduced by up to two
orders of magnitude by video compression (using H.264, HEVC, etc.), we propose
to train a deep network directly on the compressed video.
This representation has a higher information density, and we found the
training to be easier. In addition, the signals in a compressed video provide
free, albeit noisy, motion information. We propose novel techniques to use them
effectively. Our approach is about 4.6 times faster than Res3D and 2.7 times
faster than ResNet-152. On the task of action recognition, our approach
outperforms all the other methods on the UCF-101, HMDB-51, and Charades
dataset.Comment: CVPR 2018 (Selected for spotlight presentation
Cellular neural networks, Navier-Stokes equation and microarray image reconstruction
Copyright @ 2011 IEEE.Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the NavierâStokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time
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