2016年11月23日 · View a PDF of the paper titled Fast Fourier Color Constancy, by Jonathan T. Barron and 1 other authors View PDF Abstract: We present Fast Fourier Color Constancy (FFCC), a color constancy algorithm which solves illuminant estimation by reducing it to a spatial localization task on a torus.

2019年9月15日 · PDF WITH TEXT download. download 1 file . SINGLE PAGE PROCESSED JP2 ZIP download. download 1 file . TORRENT download. download 14 Files download 6 Original. SHOW ALL. IN COLLECTIONS Strategy Guides Folkscanomy: Games ...

and, by extension, our FFCC technique; see [4] for a thor-ough explanation. The primary difference between FFCC is that instead of performing an expensive localization on a large log-chroma plane, we perform a cheap localization on a small log-chroma torus. At a high level, CCC reduces color constancy to object

Additionally, FFCC lacks mechanisms to ensure smooth spatial transitions un-der rapidly changing lighting, which can result in abrupt shifts in white balance. To overcome FFCC’s limitations in multi-illuminant sce-narios, we propose Integral Fast Fourier Color Constancy (IFFCC), a local extension of FFCC [3] designed for white

Our implementation of FFCC runs at ~700 frames per second on a mobile device, allowing it to be used as an accurate, real-time, temporally-coherent automatic white balance algorithm. PDF Abstract CVPR 2017 PDF CVPR 2017 Abstract

FFCC is capable of running at ~ 700 frames per second on a mobile phone, making it a viable solution to the problem of constructing an effective, real-time, temporally-coherent automatic white balance algorithm.

2017年7月26日 · Our implementation of FFCC runs at ~700 frames per second on a mobile device, allowing it to be used as an accurate, real-time, temporally-coherent automatic white balance algorithm. Published in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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2021年4月7日 · 卷积结构与FFCC相似，但与FFCC不同，C5不包括“增益G”乘法，因为它不会导致性能的一致改进： 正如在FFCC中所做的那样，这种卷积是通过使用FFTs来加速的，不过，与FFCC不同的是，C5使用了non-wrapped装直方图，因此，使用了non-wrapped过滤器和偏差。

2018年1月12日 · proach presents a number of benefits. FFCC improves accu-racy relative to CCC by 17 − 24% while retaining its flex-ibility, and allows us to construct priors over illuminants (Section 5). By learning in the frequency-domain we can. construct a novel method for fast frequency-domain regu-larization and preconditioning, making FFCC training 20×