Color changes of ash wood (Fraxinus excelsior L.) caused by thermal modification in air and steam

Color changes of ash wood (Fraxinus excelsior L.) caused by thermal modification in air and steam. Ash wood samples of 20x20x30 mm were subjected to thermal modification in different conditions. The thermal modification was conducted in air at 190 °C and in steam at 160 °C. For both environments modification lasted 2, 6 and 10 hours. Samples color parameters were measured before and after thermal modification on the basis of the mathematical CIELab color space model. Changes in all parameters (L, a and b) were observed, the highest in lightness (L) - darker color. The total color difference (ΔE) and chromaticity change (ΔC) were calculated for all samples. The highest value of ΔE was obtained for wood modified in the air at 190 °C for 10 h. The highest value of ΔC was obtained for wood modified in steam at 160 °C for 10 h. However, the value obtained for wood modified in the air at 190 °C for 10 h were only slightly lower.

Effect of Issatchenkia terricola WJL-G4 on Deacidification Characteristics and Antioxidant Activities of Red Raspberry Wine Processing

Our previous study isolated a novel Issatchenkia terricola WJL-G4, which exhibited a potent capability of reducing citric acid. In the current study, I. terricola WJL-G4 was applied to decrease the content of citric acid in red raspberry juice, followed by the red raspberry wine preparation by Saccharomyces cerevisiae fermentation, aiming to investigate the influence of I. terricola WJL-G4 on the physicochemical properties, organic acids, phenolic compounds and antioxidant activities during red raspberry wine processing. The results showed that after being treated with I. terricola WJL-G4, the citric acid contents in red raspberry juice decreased from 19.14 ± 0.09 to 6.62 ± 0.14 g/L, which was further declined to 5.59 ± 0.22 g/L after S. cerevisiae fermentation. Parameters related to CIELab color space, including L*, a*, b*, h°, and ∆E* exhibited the highest levels in samples after I. terricola WJL-G4 fermentation. Compared to the red raspberry wine pretreated without deacidification (RJO-SC), wine pretreated by I. terricola WJL-G4 (RJIT-SC) exhibited significantly decreased contents of gallic acid, cryptochlorogenic acid, and arbutin, while significantly increased contents of caffeic acid, sinapic acid, raspberry ketone, quercitrin, quercetin, baicalein, and rutin. Furthermore, the antioxidant activities including DPPH· and ABTS+· radical scavenging were enhanced in RJIT-SC group as compared to RJO-SC. This work revealed that I. terricola WJL-G4 had a great potential in red raspberry wine fermentation.

U-CIE [/juː ’siː/]: Color encoding of high-dimensional data

U-CIE is a visualization method that encodes arbitrary high-dimensional data as colors using a combination of dimensionality reduction and the CIELAB color space to retain the original structure to the extent possible. We illustrate its broad applicability by visualizing single-cell data on a protein network and metagenomic data on a world map and on scatter plots. U-CIE is available as a web resource at https://u-cie.jensenlab.org/ and as an R package.

Quantitative Weathering Assessment of Rock Slope using CIELAB Color Space and Image Analysis Technique

Quantitative weathering assessment using color changes is one of the new tools for slope stability assessment. In many other engineering field, CIELAB color space and image analytical tools have aided in enhancing the conventional method or inaccuracy due to the subjective and qualitative nature of visual assessment. This study focuses on the granitic rock slope surface assessment because of the predominant rock formation of granite in Malaysia. The 3D model of the rock slope was analyzed to extract the geological planes using compass plugin in Cloud Compare software and verified by manual compass mapping via scanline survey. Findings show that the a* and b* values increased with an increase in weathering results. This study focuses on the 50 points of measurement of rock slope and indicated a positive correlation of a* and b* with R2=0.9027. The image analysis result of the rock slope shows that major zoning (74%) is susceptible to failure due to structural control whilst another 26% are controlled by significant weathering in the grade IV-VI. This outcome is strongly verified via a geomechanical test, geological structure, and mineralogical assessment. The aforementioned mechanism is recommended in any geotechnical and technical purpose in enhancing the method of weathering assessment because image analysis provides reliable measurements in addition to the manual visual inspection.

CMYK-CIELAB Color Space Transformation Using Machine Learning Techniques

In this work four different machine learning approaches have been implemented to perform the color space transformation between CMYK and CIELAB color spaces. We have explored the performance of Support-Vector Regression (SVR), Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Radial Basis Function (RBF) models to achieve this color space transformation, both AToB and BToA direction. The data set used for this work was FOGRA53 which is composed of 1617 color samples represented both in CMYK and CIELAB color space values. The accuracy of the transformation models was measured in terms of ΔE* color difference. Moreover, the proposed models were compared, in practical terms, with the performance of the standard ICC profile for this color space transformation. The results showed that, for the forward transformation (CMYK to CIELAB), the highest accuracy was obtained using RBF. While, for the backward transformation (CIELAB to CMYK), the highest accuracy was obtained with DNN.

Leaf Segmentation of Rosette Plants using Rough K-Means in CIELab Color Space

Segmentation of Plant Images plays an important role in modern agriculture where it can provide accurate analysis of a plant’s growth and possi-ble anomalies. In this paper, rough set based partitional clustering technique called Rough K-Means has been utilized in CIELab color space for the proper leaf segmentation of rosette plants. The eÿcacy of the proposed technique have been analysed by comparing it with the results of tra-ditional K-Means and Fuzzy C-Means clustering algorithms. The visual and numerical results re-veal that the RKM in CIELab provides the near-est result to the ideal ground truth, hence the most eÿcient one.

Bootstrap Empirical Mode Decomposition with Application to CIELAB Color Images

This paper proposes an alternative optimization-based EMD based on the notions of: 1. <i>local mean points </i>that impose mode symmetry via a Tikhonov regularized least-square (RLS) problem, and 2. efficient <i>bootstrap sifting</i> that guarantees asymptotic convergence of the mean envelope to the local mean points, regardless of regularization. Mathematical proof of convergence and a straightforward extension to the 2D-multivariate setting and CIELAB color image sare presented. Performance is demonstrated with a univariate signal and two images. Spectral analysis confirms coordinated feature extraction among image components, and separation of spatial spectra among the intrinsic mode functions.

Bootstrap Empirical Mode Decomposition with Application to CIELAB Color Images

This paper proposes an alternative optimization-based EMD based on the notions of: 1. <i>local mean points </i>that impose mode symmetry via a Tikhonov regularized least-square (RLS) problem, and 2. efficient <i>bootstrap sifting</i> that guarantees asymptotic convergence of the mean envelope to the local mean points, regardless of regularization. Mathematical proof of convergence and a straightforward extension to the 2D-multivariate setting and CIELAB color image sare presented. Performance is demonstrated with a univariate signal and two images. Spectral analysis confirms coordinated feature extraction among image components, and separation of spatial spectra among the intrinsic mode functions.

EFFICACY OF CIELAB AND CMYK COLOR SPACES IN LEUKEMIA IMAGE ANALYSIS: A COMPARISON BY STATISTICAL TECHNIQUES

There are several color models available and which color model to be used is a question to many researchers in the field of Medical Image Processing and Analysis. In this paper, the suitability of using the CIELAB and CMYK color spaces in processing and analyzing blood smear images of leukemia is assessed for comparison. Leukemia is commonly known as blood cancer and it usually leads to an abnormal proliferation of leukocytes (White Blood Cells) in the bone marrow and blood. The diagnosis of leukemia is primarily done by Pathologists by microscopically examining the blood and bone marrow smears of the suspected patient. Image processing-based methods can be used for automated detection and classification of leukemia to assist the Pathologists for a speedy diagnosis. The proposed study used [Formula: see text]-means clustering to segment the leukocytes and leukemic blast cells. The microscopic smear images in RGB color format were transformed and processed in CIELAB as well as CMYK color spaces for comparison. Statistical analysis was performed using Student’s [Formula: see text] test. The visual observation of the segmented images revealed that, processing the images in the CIELAB color space is comparatively better than the CMYK color space and it is reflected in the results of statistical comparison by Student’s [Formula: see text] test. The study assessed the suitability of using the CIELAB and CMYK color spaces in segmentation and analysis of microscopic smear images of Leukemic and Normal cases and found that processing the images in CIELAB color space is comparatively better than CMYK and such comparative analysis is not available in the literature.