Dichromatic Model Based Temporal Color Constancy for AC Light Sources

Yoo, Jun-Sang; Kim, Jong-Ok

doi:10.1109/cvpr.2019.01261

Cited by 19 publications

(38 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Red points indicate the intensities with frame index at a fixed location and the blue represents the sinusoidal curve that is fit with the red samples. With AC fitting [12] of intensity, it is confirmed that the intensity on regions affected by AC light bulbs varies sinusoidally. These variations of intensity provide useful information for illuminant estimation.…”

Section: Introductionmentioning

confidence: 71%

“…Conventional methods [2][3][4][5][6][7][8][9][10][11][12] for color constancy can be classified into two groups: statistics-based and physics-based. The statistics-based approach estimates the illuminant based on an assumption about statistical properties of natural images [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…The statistics-based approach estimates the illuminant based on an assumption about statistical properties of natural images [2][3][4][5][6]. Meanwhile, the physics-based approach uses physics models with respect to surface reflection of objects [9,12]. In this paper, we study the physics-based approach, specially concentrating on temporal domain.…”

Section: Introductionmentioning

confidence: 99%

“…The more scenes are strongly dominant by AC-powered illuminant, the more fluctuation is observed remarkably through high-speed camera. We previously proposed a dichromatic model based color constancy method to exploit the AC fluctuation [12]. Unlike our previous vision-based work, deep learning is adopted in this paper.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Deep Color Constancy Using Temporal Gradient Under Ac Light Sources

Yoo

Kim

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

With the invention of electric bulbs, human has been living under various illuminant environments. Since the alternative current (AC) power is used for supplier of electric bulbs, intensity difference between consecutive video frames can be captured with high-speed camera. While most of conventional methods focus on only spatial information of a single image, we propose a deep spatio-temporal color constancy method. To exploit the temporal feature from highspeed video, maximum gradient map is fed into the proposed network. It can easily identify image regions which are significantly illuminated by light bulbs and be a useful information for estimating the illuminant. The experimental results demonstrate that using temporal features leads to better performance of illuminant estimation rather than conventional spatial methods.

show abstract

Section: Introductionmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Color Constancy Using Temporal Gradient Under Ac Light Sources

Yoo

Kim

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

show abstract

“…A different research direction in computational color constancy is the multi-frame illumination estimation [44], [45]. Several video-based datasets have been proposed to incorporate the temporal information in the learning process [46]- [48]. Another noteworthy type of datasets are the multiillumination datasets [49]- [52].…”

Section: Previously Published Color Constancy Datasetsmentioning

confidence: 99%

INTEL-TAU: A Color Constancy Dataset

Laakom

Raitoharju

Nikkanen³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

In this paper, we describe a new large dataset for illumination estimation. This dataset, called INTEL-TAU, contains 7022 images in total, which makes it the largest available high-resolution dataset for illumination estimation research. The variety of scenes captured using three different camera models, namely Canon 5DSR, Nikon D810, and Sony IMX135, makes the dataset appropriate for evaluating the camera and scene invariance of the different illumination estimation techniques. Privacy masking is done for sensitive information, e.g., faces. Thus, the dataset is coherent with the new General Data Protection Regulation (GDPR). Furthermore, the effect of color shading for mobile images can be evaluated with INTEL-TAU dataset, as both corrected and uncorrected versions of the raw data are provided. Furthermore, this paper benchmarks several color constancy approaches on the proposed dataset.

show abstract

A Benchmark for Burst Color Constancy

Qian

Käpylä

Kämäräinen

et al. 2020

Computer Vision – ECCV 2020 Workshops

View full text Add to dashboard Cite

Temporal Color Constancy (CC) is a recently proposed approach that challenges the conventional single-frame color constancy. The conventional approach is to use a single frame -shot frame -to estimate the scene illumination color. In temporal CC, multiple frames from the view finder sequence are used to estimate the color. However, there are no realistic large scale temporal color constancy datasets for method evaluation. In this work, a new temporal CC benchmark is introduced. The benchmark comprises of (1) 600 real-world sequences recorded with a high-resolution mobile phone camera, (2) a fixed train-test split which ensures consistent evaluation, and (3) a baseline method which achieves high accuracy in the new benchmark and the dataset used in previous works. Results for more than 20 well-known color constancy methods including the recent state-of-the-arts are reported in our experiments.

show abstract

Dichromatic Model Based Temporal Color Constancy for AC Light Sources

Cited by 19 publications

References 37 publications

Deep Color Constancy Using Temporal Gradient Under Ac Light Sources

Deep Color Constancy Using Temporal Gradient Under Ac Light Sources

INTEL-TAU: A Color Constancy Dataset

A Benchmark for Burst Color Constancy

Contact Info

Product

Resources

About