A Survey of Color Mapping and its Applications

Faridul, Hasan Sheikh; Pouli, Tania; Chamaret, Christel; Stauder, Jürgen; Trémeau, Alain; Reinhard, Erik

doi:10.2312/egst.20141035

Cited by 13 publications

(2 citation statements)

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“…The body of work in recolouring is very large and the reader is referred to this recent exhaustive review by Faridul et al (2014). Early work on colour transfer started with registering statistical moments of colour distributions (Section 2.1) using a parametric affine formulation of the transfer function 𝜙.…”

Section: State Of the Artmentioning

confidence: 99%

L2 Divergence for robust colour transfer

Grogan

Dahyot

2019

Computer Vision and Image Understanding

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Section: State Of the Artmentioning

confidence: 99%

L2 Divergence for robust colour transfer

Grogan

Dahyot

2019

Computer Vision and Image Understanding

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“…Vice versa, colors that are identified by different names, apparently have similarities [12]. The real problem is related to the identification of a color that refers to the name given, while there are many colors that are similar, approaching and appearing to be almost the same [13]. This happens because the process of color recognition itself, namely by referring to the primary color, and then the primary color is segmented and recognized by the identity of a particular name [14].…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation Based on Color Dissimilarity

Karma¹,

Putra

Sudarma

et al. 2022

IJCAI

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This study aims to develop a segmentation technique that can be used to detect objects in an image. The concept used is to imitate the human concept of recognizing an object based on its color difference. A color is considered different if it has different R, G or B values. Humans can only distinguish two colors clearly if they both have a DeltaE value of at least 8. The difference in DeltaE values is obtained from differences in the values of R, G or B, either alone or in pairs. The application of this concept to the segmentation technique has shown good results.This technique is able to give the same results and is good for images with JPG, PNG and BMP file types. To provide confidence in the performance of this method, a comparison is made with several segmentation methods. By measuring the similarity index using the Jaccard index, Sørensen-Dice index and BF Score, this color dissimilarity-based segmentation method has the best performance. Meanwhile, when measuring the processing time, this method has the second best performance. Thus, it can be concluded that this method is very suitable for detecting objects in an image. Povzetek: Tehnika segmentacije je bila razvita z imitacijo človeškega koncepta prepoznavanja predmeta, in sicer na podlagi barvnih razlik. Barve se štejejo za različne, če imajo vrednost DeltaE najmanj 8, ki jo dobimo iz razlik v vrednostih R, G ali B barv samih ali v parih. Izvajanje te tehnike kaže enake rezultate za datoteke JPG, PNG in BMP in je v veliko pomoč pri postopku identifikacije predmeta.

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