Abstract
Existing dichromatic color constancy approach commonly requires a number of spatial pixels which have high specularity. In this paper, we propose a novel approach to estimate the illuminant chromaticity of AC light source using high-speed camera. We found that the temporal observations of an image pixel at a fixed location distribute on an identical dichromatic plane. Instead of spatial pixels with high specularity, multiple temporal samples of a pixel are exploited to determine AC pixels for dichromatic plane estimation, whose pixel intensity is sinusoidally varying well. A dichromatic plane is calculated per each AC pixel, and illuminant chromaticity is determined by the intersection of dichromatic planes. From multiple dichromatic planes, an optimal illuminant is estimated with a novel MAP framework. It is shown that the proposed method outperforms both existing dichromatic based methods and temporal color constancy methods, irrespective of the amount of specularity.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 |
| Publisher | IEEE Computer Society |
| Pages | 12321-12330 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781728132938 |
| DOIs | |
| Publication status | Published - 2019 Jun |
| Event | 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States Duration: 2019 Jun 16 → 2019 Jun 20 |
Publication series
| Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
|---|---|
| Volume | 2019-June |
| ISSN (Print) | 1063-6919 |
Conference
| Conference | 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 |
|---|---|
| Country/Territory | United States |
| City | Long Beach |
| Period | 19/6/16 → 19/6/20 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- Computer Vision Theory
- Low-level Vision
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
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