A fast color constancy scheme for automobile video cameras

Garud, Hrushikesh; Pudipeddi, Uday Kiran; Desappan, Kumar; Nagori, Soyeb

doi:10.1109/spcom.2014.6983981

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…H. Garud et al (2014) presented a fast and effective CC scheme specifically suited for automobile video cameras. The proposed CC scheme uses combination of a static method for illuminant estimation called White Patch Retinex (WPR) and a computationally efficient linear transformation model for WB correction of the images.…”

Section: Literature R Eviewmentioning

confidence: 99%

Analysis on Artificial Recurrent Neural Network for Sequence Classification of Audio Data

Singh¹,

Singh²,

Kaur³

et al. 2021

Middle East Res J Engr Technol

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Recurrent neural networks (RNNs) are capable of learning features and long-term dependencies from sequential and time-series data. The RNNs have a stack of non-linear units where at least one connection between units forms a directed cycle. A well-trained RNN can model any dynamical system; however, training RNNs is mostly plagued by issues in learning long-term dependencies. In this paper, we present a survey on RNNs and several new advances for newcomers and professionals in the field. In this paper literature survey on Artificial Recurrent Neural Network has been presented.

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Section: Literature R Eviewmentioning

confidence: 99%

Analysis on Artificial Recurrent Neural Network for Sequence Classification of Audio Data

Singh¹,

Singh²,

Kaur³

et al. 2021

Middle East Res J Engr Technol

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“…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%

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)

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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.

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