Estimation of Illuminants From Projections on the Planckian Locus

Mazin, Baptiste; Delon, Julie; Gousseau, Yann

doi:10.1109/tip.2015.2405414

Cited by 16 publications

(5 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…reddish or a cooler i.e. blueish illumination, which is effectively a simplification of the Planckian locus [50] that has already been used for illumination estimation in several methods [27], [46]. A somewhat similar rough division to an indoor and outdoor type illumination has been successfully used for a slightly different purpose in [51].…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…As for the first question, the ground-truth illuminations or their approximations for many images can reveal in which chromaticity space regions are future illumination estimations of new images most likely to appear. There are several methods that rely on such kind of information [27], [46], [31], [29] with probably the least demanding one being the Color Dog method [29]. During the training phase it clusters the groundtruth illuminations by using the k-means clustering [47] with the angular instead of the Euclidean distance.…”

Section: Figmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised Learning for Color Constancy

Banić

Lončarić

2018

Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

View full text Add to dashboard Cite

Most digital camera pipelines use color constancy methods to reduce the influence of illumination and camera sensor on the colors of scene objects. The highest accuracy of color correction is obtained with learning-based color constancy methods, but they require a significant amount of calibrated training images with known ground-truth illumination. Such calibration is time consuming, preferably done for each sensor individually, and therefore a major bottleneck in acquiring high color constancy accuracy. Statistics-based methods do not require calibrated training images, but they are less accurate. In this paper an unsupervised learning-based method is proposed that learns its parameter values after approximating the unknown ground-truth illumination of the training images, thus avoiding calibration. In terms of accuracy the proposed method outperforms all statisticsbased and many learning-based methods. An extension of the method is also proposed, which learns the needed parameters from non-calibrated images taken with one sensor and which can then be successfully applied to images taken with another sensor. This effectively enables inter-camera unsupervised learning for color constancy. Additionally, a new high quality color constancy benchmark dataset with 1707 calibrated images is created, used for testing, and made publicly available. The results are presented and discussed. The source code and the dataset are available at

show abstract

Section: The Proposed Methodsmentioning

confidence: 99%

Section: Figmentioning

confidence: 99%

Unsupervised Learning for Color Constancy

Banić

Lončarić

2018

Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

View full text Add to dashboard Cite

show abstract

“…Extracted low-level features discussed in [23], such as Scale Invariant Features Transform (SIFT) [31], Space-Time Interest Points (STIP) [32], and OpponentSIFT [33], are inadequate for comprehending semantic nuances in complex situations.…”

Section: B Low-level Features Extractionmentioning

confidence: 99%

A Systematic Review of Rare Events Detection Across Modalities using Machine Learning and Deep Learning

Abubakar,

OTHMANI,

Sabri

et al. 2023

Preprint

View full text Add to dashboard Cite

<p>Rare event detection (RED) involves the identification and detection of events characterized by low frequency of occurrences, but of high importance or impact. This paper presents a Systematic Review (SR) of rare event detection across various modalities using Machine Learning (ML) and Deep Learning (DL) techniques. This review comprehensively outlines techniques and methods best suited for rare event detection across various modalities, while also highlighting future research prospects. To the extent of our knowledge, this paper is a pioneering SR dedicated to exploring this specific research domain. This SR identifies the employed methods and techniques, the datasets utilized, and the effectiveness of these methods in detecting rare events. Four modalities concerning RED are reviewed in this SR: video, sound, image, and time series. The corresponding performances for the different ML and DL techniques for RED are discussed comprehensively, together with the associated RED challenges and limitations as well as the directions for future research are highlighted. This SR aims to offer a comprehensive overview of the existing methods in RED, serving as a valuable resource for researchers and practitioners working in the respective field.</p>

show abstract

“…Researchers have proposed various colour constancy adjustment methods to address the problem of colour constancy in the presence of both single and multiple light sources in digital images [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45]. The existing colour constancy techniques can be grouped into five categories: statistics-based, gamut-based, physics-based, learning-based and biologically inspired methods.…”

Section: Introductionmentioning

confidence: 99%

“…They choose the best estimates based on the error statistics and combine the estimates for per superpixel using a machine-learning-based regression algorithm. Mazin et al [34] proposed a technique to extract a set of gray pixels from the image to estimate a set of possible light source for the pixels by using the Planckian locus of black-body radiators. In [35], Bianco and Schettini proposed a method to estimate the colour of the light from human faces within the image by using a scale-space histogram filtering method.…”

Section: Introductionmentioning

confidence: 99%

Colour Constancy for Image of Non-Uniformly Lit Scenes

Hussain

Sheikh-Akbari

Mporas

2019

Sensors

View full text Add to dashboard Cite

Digital camera sensors are designed to record all incident light from a captured scene, but they are unable to distinguish between the colour of the light source and the true colour of objects. The resulting captured image exhibits a colour cast toward the colour of light source. This paper presents a colour constancy algorithm for images of scenes lit by non-uniform light sources. The proposed algorithm uses a histogram-based algorithm to determine the number of colour regions. It then applies the K-means++ algorithm on the input image, dividing the image into its segments. The proposed algorithm computes the Normalized Average Absolute Difference (NAAD) for each segment and uses it as a measure to determine if the segment has sufficient colour variations. The initial colour constancy adjustment factors for each segment with sufficient colour variation is calculated. The Colour Constancy Adjustment Weighting Factors (CCAWF) for each pixel of the image are determined by fusing the CCAWFs of the segments, weighted by their normalized Euclidian distance of the pixel from the center of the segments. Results show that the proposed method outperforms the statistical techniques and its images exhibit significantly higher subjective quality to those of the learning-based methods. In addition, the execution time of the proposed algorithm is comparable to statistical-based techniques and is much lower than those of the state-of-the-art learning-based methods.

show abstract

Estimation of Illuminants From Projections on the Planckian Locus

Cited by 16 publications

References 50 publications

Unsupervised Learning for Color Constancy

Unsupervised Learning for Color Constancy

A Systematic Review of Rare Events Detection Across Modalities using Machine Learning and Deep Learning

Colour Constancy for Image of Non-Uniformly Lit Scenes

Contact Info

Product

Resources

About