Design of a color image processing algorithm using online arithmetic modules

2004 ◽  
Author(s):  
Mohammed H. Sinky ◽  
Alexandre F. Tenca ◽  
Ajay C. Shantilal ◽  
Luca Lucchese
Keyword(s):  
Image Processing ◽  
Color Image ◽  
Processing Algorithm ◽  
Color Image Processing ◽  
Image Processing Algorithm ◽  
Online Arithmetic

scholarly journals The use of color distribution analysis for ripeness prediction of Golden Apollo melon

2017 ◽  
Author(s):  
The Journal of Applied Horticulture ◽  
Usman Ahmad
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Skin Color ◽  
Color Image ◽  
Age Groups ◽  
Processing Algorithm ◽  
Distribution Analysis ◽  
Human Visual Perception ◽  
Image Processing Algorithm ◽  
Color Distribution

Human visual perception on color of melon fruit for ripeness judgement is a complex phenomenon that depends on many factors, making the judgement is often inaccurate and inconsistent. The objective of this study is to develop an image processing algorithm that can be used for distinguishing ripe melons from unripe ones based on their skin color. The image processing algorithm could then be used as a pre-harvest tool to facilitate farmers with enough information for making decisions about whether or not the melon is ready to harvest. Four sample groups of Golden Apollo melon were harvested at four different harvesting age, with 55 fruits in each group. The color distribution as results of the image analysis can be separated at the first two groups from other groups with minimal overlap, but they cannot be separated from the other two groups. The color image analysis of the melons in combination with discriminant analysis could be used to distinguish between harvesting age groups with an average accuracy of 86%.

New method of morphological color image processing

2010 ◽  
Vol 30 (8) ◽  
pp. 2101-2104
Author(s):  
Hong-zhong TANG ◽  
Hui-xian HUANG ◽  
Xue-feng GUO ◽  
Ye-wei XIAO
Keyword(s):  
Image Processing ◽  
Color Image ◽  
New Method ◽  
Color Image Processing

Internal quality evaluation of chestnut using nuclear magnetic resonance

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Soo Hyun Park ◽  
Sang Ha Noh ◽  
Michael J. McCarthy ◽  
Seong Min Kim
Keyword(s):  
Image Processing ◽  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Processing Algorithm ◽  
Internal Quality ◽  
Soluble Solid Content ◽  
Mr Images ◽  
Image Processing Algorithm ◽  
Internal Defects ◽  
Nuclear Magnetic

AbstractThis study was carried out to develop a prediction model for soluble solid content (SSC) of intact chestnut and to detect internal defects using nuclear magnetic resonance (NMR) relaxometry and magnetic resonance imaging (MRI). Inversion recovery and Carr–Purcell–Meiboom–Gill (CPMG) pulse sequences used to determine the longitudinal (T1) and transverse (T2) relaxation times, respectively. Partial least squares regression (PLSR) was adopted to predict SSCs of chestnuts with NMR data and histograms from MR images. The coefficient of determination (R2), root mean square error of prediction (RMSEP), ratio of prediction to deviation (RPD), and the ratio of error range (RER) of the optimized model to predict SSC were 0.77, 1.41 °Brix, 1.86, and 11.31 with a validation set. Furthermore, an image-processing algorithm has been developed to detect internal defects such as decay, mold, and cavity using MR images. The classification applied with the developed image processing algorithm was over 94% accurate to classify. Based on the results obtained, it was determined that the NMR signal could be applied for grading several levels by SSC, and MRI could be used to evaluate the internal qualities of chestnuts.

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