A Comprehensive Review of the Impact of Color Space on Image Segmentation

Ganesan, P.; Sathish, B. S.; Vasanth, K.; Sivakumar, V.; Vadivel, M.; Ravi, C.

doi:10.1109/icaccs.2019.8728392

Cited by 7 publications

(7 citation statements)

References 8 publications

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“…K-means segmentation was performed on a variety of color spaces and Gabor filter images. Color can be described by its colorfulness, brightness, and hue, which makes it an important attribute in image segmentation as this can allow discrimination between objects (Ganesan et al, 2019). Hence, utilising the different color spaces of RGB, LAB, HSV and YCbCr can be an effective approach for image segmentation.…”

Section: Object-based Image Analysis Of Orchids Using Rgb Photographsmentioning

confidence: 99%

Use of remote sensing and image processing for identification of wild orchids

Ahmed,

Lightbown,

Rutter

et al. 2024

Front. Environ. Sci.

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A novel multi-technique approach has been applied for the identification and mapping of wild orchids using a combination of remote sensing and spectral image analysis. The five orchid species identified were the common spotted-orchid (Dactylorhiza fuchsia), heath spotted-orchid (Dactylorhiza maculata), pyramidal orchid (Anacamptis pyramidalis), heath fragrant-orchid (Gymnadenia borealis), and the dark-red helleborine (Epipactis atrorubens). Field studies have been done using a hand-held spectrometer operating in the 400–700 nm visible spectrum, photogrammetry using a digital camera as well as a multispectral image camera operating at the specific spectral bands of 450 nm (blue), 560 nm (green), 650 nm (red), 730 nm (red edge) and 840 nm (near-infrared) attached to an unmanned aerial vehicle Data analysis, using the hand-held spectrometer, followed by pattern recognition using principal component analysis and partial least squares-discriminant analysis, have identified the key distinguishing wavelengths for identification of the 5 orchid types as 400, 410, 420 and 560 nm. The use of remote sensing, using the UAV-MSI, and application of a dedicated spectral index has enabled field identification of the orchids. Finally, object-based image analysis of field gathered photogrammetry imagery, has enabled use of shape, size, and color to identify and distinguish orchid species. The developed data analytic tool, using random forest classification, can be used to identify and characterize wild orchids across multiple sites within their short lifespan with an accuracy of 86%. Any longer-term study would provide invaluable information on the diversity and complexity of orchid habitat, population variation both intra- and inter-site location, as well as the impact of climate change.

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Section: Object-based Image Analysis Of Orchids Using Rgb Photographsmentioning

confidence: 99%

Use of remote sensing and image processing for identification of wild orchids

Ahmed,

Lightbown,

Rutter

et al. 2024

Front. Environ. Sci.

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“…Linearly or nonlinearly transformations of the RGB space generate other color spaces. Figure 1 provides a classification of color spaces into (i) the primary spaces (based on the trichromatic theory and representing any color by combining the right amounts of the three primary colors), (ii) the luminance-chrominance spaces (in which one component represents the luminance and two others the chrominances), (iii) the perceptual spaces (that quantify the color perception using intensity, hue, and saturation), and (iv) the independent axis spaces (providing as less correlated components as possible in some statistical sense) [10,[12][13][14].…”

Section: Color Spacesmentioning

confidence: 99%

“…Figure 1. A rough classification of color space based on its characteristics (a more complete illustration can be found in [14]).…”

Section: Color Spacesmentioning

confidence: 99%

Defining a Metacolor Space Representation to Perform Image Segmentation

Castiello,

Del Buono,

Esposito

2024

Preprint

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In this paper, we use a machine learning method, known as Nonnegative Matrix Factorization, to extract from a given image some new information obtained from a number of color space representations. Such kind of information, termed ``metacolor'', can be considered as a particular color space representation of the image, which can be used to obtain a binary segmentation of the investigated image by distinguishing foreground and background pixels. Some numerical experiments prove that the proposed method shows improved results when compared with common simpler thresholding algorithms.

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“…These steps are important for the preparation of the RGB images of eyes with LSCD for image The eyes RGB images are converted into normalized RGB encoding. We chose the RGB color space because of its ability to differentiate the color and intensity information [29]. Normalized RGB has the ability to decrease the distortions due to shadows and lights in an image.…”

Section: The Preprocessing Phasementioning

confidence: 99%

Computerized Detection of Limbal Stem Cell Deficiency from Digital Cornea Images

Mahmoud¹,

Khafga²,

Alharbi³

2022

Computer Systems Science and Engineering

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Limbal Stem Cell Deficiency (LSCD) is an eye disease that can cause corneal opacity and vascularization. In its advanced stage it can lead to a degree of visual impairment. It involves the changing in the semispherical shape of the cornea to a drooping shape to downwards direction. LSCD is hard to be diagnosed at early stages. The color and texture of the cornea surface can provide significant information about the cornea affected by LSCD. Parameters such as shape and texture are very crucial to differentiate normal from LSCD cornea. Although several medical approaches exist, most of them requires complicated procedure and medical devices. Therefore, in this paper, we pursued the development of a LSCD detection technique (LDT) utilizing image processing methods. Early diagnosis of LSCD is very crucial for physicians to arrange for effective treatment. In the proposed technique, we developed a method for LSCD detection utilizing frontal eye images. A dataset of 280 eye images of frontal and lateral LSCD and normal patients were used in this research. First, the cornea region of both frontal and lateral images is segmented, and the geometric features are extracted through the automated active contour model and the spline curve. While the texture features are extracted using the feature selection algorithm. The experimental results exhibited that the combined features of the geometric and texture will exhibit accuracy of 95.95%, sensitivity of 97.91% and specificity of 94.05% with the random forest classifier of n = 40. As a result, this research developed a Limbal stem cell deficiency detection system utilizing features' fusion using image processing techniques for frontal and lateral digital images of the eyes.

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A Comprehensive Review of the Impact of Color Space on Image Segmentation

Cited by 7 publications

References 8 publications

Use of remote sensing and image processing for identification of wild orchids

Use of remote sensing and image processing for identification of wild orchids

Defining a Metacolor Space Representation to Perform Image Segmentation

Computerized Detection of Limbal Stem Cell Deficiency from Digital Cornea Images

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