Blight segmentation on corn crop leaf using connected component extraction and CIELAB color space transformation

2017 ◽  
Author(s):  
Septian Enggar Sukmana ◽  
Farah Zakiyah Rahmanti
Keyword(s):  
Color Space ◽  
Connected Component ◽  
Cielab Color Space ◽  
Corn Crop ◽  
Space Transformation ◽  
Color Space Transformation ◽  
Component Extraction ◽  
Cielab Color

scholarly journals CMYK-CIELAB Color Space Transformation Using Machine Learning Techniques

2021 ◽  
Vol 2021 (1) ◽  
pp. 73-77
Author(s):  
Ronny Velastegui ◽  
Marius Pedersen
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Color Space ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Set ◽  
Cielab Color Space ◽  
Space Transformation ◽  
Color Space Transformation ◽  
Cielab Color

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.

Color Space Transformation using Neural Networks

2019 ◽  
Vol 2019 (1) ◽  
pp. 153-158
Author(s):  
Lindsay MacDonald
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Color Space ◽  
Reflectance Spectra ◽  
Network Architectures ◽  
Color Spaces ◽  
Natural Materials ◽  
Space Transformation ◽  
Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

A Correspondence-Free Color Chart Design for Color Calibration

2020 ◽  
Vol 2020 (1) ◽  
pp. 100-104
Author(s):  
Hakki Can Karaimer ◽  
Rang Nguyen
Keyword(s):  
Color Space ◽  
Fixed Number ◽  
New Approach ◽  
Color Chart ◽  
Color Calibration ◽  
Space Transformation ◽  
Color Space Transformation ◽  
Chart Design ◽  
Calibration Approach

Colorimetric calibration computes the necessary color space transformation to map a camera's device-specific color space to a device-independent perceptual color space. Color calibration is most commonly performed by imaging a color rendition chart with a fixed number of color patches with known colorimetric values (e. g., CIE XYZ values). The color space transformation is estimated based on the correspondences between the camera's image and the chart's colors. We present a new approach to colorimetric calibration that does not require explicit color correspondences. Our approach computes a color space transformation by aligning the color distributions of the captured image to the known distribution of a calibration chart containing thousands of colors. We show that a histogram-based colorimetric calibration approach provides results that are onpar with the traditional patch-based method without the need to establish correspondences.

Changes in the content of chlorophylls and carotenoids in the rind of Fino 49 lemons during maturation and their relationship with parameters from the CIELAB color space

2019 ◽  
Vol 243 ◽  
pp. 252-260 ◽  
Author(s):  
A. Conesa ◽  
F.C. Manera ◽  
J.M. Brotons ◽  
J.C. Fernandez-Zapata ◽  
I. Simón ◽  
...  
Keyword(s):  
Color Space ◽  
Cielab Color Space ◽  
Cielab Color

Development of a customized whiteness index for dentistry based on CIELAB color space

2016 ◽  
Vol 32 (3) ◽  
pp. 461-467 ◽  
Author(s):  
María del Mar Pérez ◽  
Razvan Ghinea ◽  
María José Rivas ◽  
Ana Yebra ◽  
Ana María Ionescu ◽  
...  
Keyword(s):  
Color Space ◽  
Whiteness Index ◽  
Cielab Color Space ◽  
Cielab Color

A Novel Piece-Wise Linear Algorithm for Human Skin Segmentation

2015 ◽  
Vol 743 ◽  
pp. 317-320
Author(s):  
Ravi Subban ◽  
Pasupathi Perumalsamy ◽  
G. Annalakshmi
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Skin Detection ◽  
Color Spaces ◽  
Color Component ◽  
Skin Segmentation ◽  
Space Transformation ◽  
Color Space Transformation ◽  
Comprehensive Knowledge ◽  
Novel Method

This paper presents a novel method for skin segmentation in color images using piece-wise linear bound skin detection. Various color schemes are investigated and evaluated to find the effect of color space transformation over the skin detection performance. The comprehensive knowledge about the various color spaces helps in skin color modeling evaluation. The absence of the luminance component increases performance, which also supports in finding the appropriate color space for skin detection. The single color component produces the better performance than combined color component and reduces computational complexity.

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