Color and color models

AbstractThe potential use of colorimetric sensors has received significant attention due to its feasibility for use in various applications. After reacting with a sample, the image of the colorimetric sensor can be captured and converted into digital data using several different color models. The analytical data can then be processed with various chemometric methods. This research study investigated the predictive performance of calibration models established using color models commonly used in analytical chemistry including RGB, CMYK, HSV and CIELAB. A total of eight commercially available colorimetric sensors were used to determine the presence of manganese (Mn2+), copper (Cu2+), iron (Fe2+/Fe3+), nitrate (NO3–), phosphate (PO43–), sulfate (SO42–), as well as total hardness and pH values. As external validation tests, real water samples collected in Chiang Mai, Thailand were used. Based on the resulting data obtained using the synthetic test samples, the color that was most similar to the appearing color of the chemical sensor could offer satisfactory results. However, it was not always the case especially when the strips composed of multiple colorimetric sensors or sensor array were used. When tested with external validation, the predictive performance could be improved using appropriate data preprocessing and, in this research study, a normalization method was recommended to guarantee the accuracy of the calibration models.

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The Application of Computer Image Analysis Based on Textural Features for the Identification of Barley Kernels Infected with Fungi of the Genus Fusarium

Agricultural Engineering ◽

10.1515/agriceng-2018-0026 ◽

2018 ◽

Vol 22 (3) ◽

pp. 49-56 ◽

Cited By ~ 1

Author(s):

Ewa Ropelewska

Keyword(s):

Image Analysis ◽

Selection Method ◽

Color Model ◽

Textural Features ◽

Discriminative Power ◽

Selection Methods ◽

Computer Image Analysis ◽

Genus Fusarium ◽

Color Models ◽

Lab Color Model

AbstractThe aim of this study was to develop discrimination models based on textural features for the identification of barley kernels infected with fungi of the genus Fusarium and healthy kernels. Infected barley kernels with altered shape and discoloration and healthy barley kernels were scanned. Textures were computed using MaZda software. The kernels were classified as infected and healthy with the use of the WEKA application. In the case of RGB, Lab and XYZ color models, the classification accuracies based on 10 selected textures with the highest discriminative power ranged from 95 to 100%. The lowest result (95%) was noted in XYZ color model and Multi Class Classifier for the textures selected using the Ranker method and the OneR attribute evaluator. Selected classifiers were characterized by 100% accuracy in the case of all color models and selection methods. The highest number of 100% results was obtained for the Lab color model with Naive Bayes, LDA, IBk, Multi Class Classifier and J48 classifiers in the Best First selection method with the CFS subset evaluator.

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High Capacity Reversible Steganography on CMY and HSI Color Images Using Image Interpolation

Webology ◽

10.14704/web/v18si04/web18119 ◽

2021 ◽

Vol 18 (Special Issue 04) ◽

pp. 133-148

Author(s):

D. Shahi ◽

R.S. Vinod Kumar ◽

V.K. Reshma

Keyword(s):

High Capacity ◽

Similarity Index ◽

Image Interpolation ◽

Median Plane ◽

Research Area ◽

Secret Message ◽

Color Models ◽

Image Models ◽

Surface Similarity ◽

New Research

Steganography using image interpolation has created a new research area in multimedia communication. A reversible data concealing in HSI and CMY color models using image interpolation is proposed in this paper. The HSI and CMY image models are interpolated using High Capacity Reversible Steganography (CRS) technique. The median plane of both HSI and CMY color models are selected for secret message bit concealing. The secret message bits are concealed in the cover plane by Exclusive OR (XOR) operation. Since the cover image is recovered after secret message bit retrieval, this finds application in military and medical imaging applications. The experimental results of proposed scheme showed very high embedding capacity of about 16 bits in each pixel location of calculated pixel value, good image quality with a surface similarity index measure (SSIM) value 1 and high PSNR. Also, high robustness is achieved on comparing with the existing works.

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