A novel digital imaging method for measuring cashmere color and its application

2021 ◽  
pp. 004051752110086
Author(s):  
Chong Heng ◽  
Hua Shen ◽  
Fumei Wang
Keyword(s):  
Image Analysis ◽  
Correlation Coefficients ◽  
Visual Assessment ◽  
New Method ◽  
Color Measurement ◽  
Inspection System ◽  
Subjective Time ◽  
Imaging Method ◽  
Optical Index ◽  
Fixed Weight

The quality of cashmere, such as color and length, determines its price and application. In the current cashmere inspection system, color and length are tested by visual assessment, which is a subjective, time- and labor-consuming process. Herein, the goal of this research is to develop a new method of testing cashmere color using image analysis, and to study the application of color in length measurement. During the color measurement, cashmere was prepared under two sample placement methods, and color features including RGB, XYZ and Lab obtained by the new method were compared with the standard. The calculation method of optical index used in length testing was determined based on theoretical and experimental analysis. Experiments show that fixed weight and pressure are suited for cashmere color measurement. In RGB space, the correlation coefficients ( R2) between the two devices were calculated and were 0.990, 0.995 and 0.996 for parameters R, G and B, respectively. Good agreement also exhibited in XYZ space, with R2 equal to 0.994, 0.996 and 0.999 for X, Y and Z, respectively. This confirmed the accuracy of the proposed color measurement method in RGB and XYZ space. Finally, an accurate fibrogram was obtained by the proposed conversion model for calculating optical index from color values, which is the key curve to testing cashmere length. This study emphasis on methodological aspects and the results acquired are regarded as preliminary, as the experiments studied compose the first stage of research on the exploration of the application of image analysis on cashmere color measurement.

scholarly journals Digital Imaging as a Possible Approach in Evaluation of Islet Yield

2003 ◽  
Vol 12 (2) ◽  
pp. 129-133 ◽  
Author(s):  
Peter Girman ◽  
Jan Kříž ◽  
Jozef Friedmanský ◽  
FrantišEk Saudek
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Imaging ◽  
Digital Image Analysis ◽  
Three Dimensional ◽  
Two Dimensions ◽  
New Method ◽  
Important Method ◽  
Three Dimensional Objects ◽  
Significant Difference

Digital image analysis (DIA) is a new method in assessment of islet amount, which is expected to provide reliable and consistent results. We compared this method with conventional counting of small numbers of rat islets. Islets were isolated from 8 pancreases and counted in 24 samples in duplicate, first routinely by sizing according to estimated diameters under a calibrated reticule and then by processing of islets pictures taken by camera. As presumed, no significant difference was found in absolute numbers of islets per sample between DIA and conventional assessment. Volumes of islets per sample measured by DIA were on average more than 10% higher than amounts evaluated conventionally, which was statistically significant. DIA has been shown to be an important method to remove operator bias and provide consistent results. Evaluation of only two dimensions of three-dimensional objects still represents a certain limitation of this technique. With lowering of computer prices the system could become easily available for islet laboratories.

scholarly journals AN AUTOMATED FRAMEWORK FOR CORONARY ANALYSIS FROM CORONARY CINE ANGIOGRAMS USING MACHINE LEARNING AND IMAGE ANALYSIS TECHNIQUES

2021 ◽  
Vol 9 (1) ◽  
pp. 1406-1412
Author(s):  
K. Santhi, A. Rama Mohan Reddy
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Image Analysis ◽  
Visual Assessment ◽  
Disease Diagnosis ◽  
Healthy Life ◽  
Common Cause ◽  
Analysis Techniques ◽  
Image Modality ◽  
Image Analysis Techniques

Cardiovascular disease (CVD) is one of the critical diseases and the most common cause of morbidity and mortality worldwide. Therefore, early detection and prediction of such a disease is extremely essential for a healthy life. Cardiac imaging plays an important role in the diagnosis of cardiovascular disease but its role has been limited to visual assessment of heart structure and its function. However, with the advanced techniques and tools of big data and machine learning, it become easier to clinician to diagnose the CVD. Stenosis with in the Coronary Arteries (CA) are often determined by using the Coronary Cine Angiogram (CCA). It comes under the invasive image modality. CCA is the effective method to detect and predict the stenosis. In this paper a coronary analysis automation method is proposed in disease diagnosis. The proposed method includes pre-processing, segmentation, identifying vessel path and statistical analysis.

Improving the Quality Assessment band in Landsat cloud images for the application of cloud removal  

2021 ◽  
Author(s):  
Boli Yang ◽  
Yan Feng ◽  
Ruyin Cao
Keyword(s):  
Quality Assessment ◽  
Land Surface ◽  
Missing Values ◽  
Similarity Index ◽  
Visual Assessment ◽  
New Method ◽  
Landsat Data ◽  
Cloud Detection ◽  
Cloud Removal ◽  
Ultimate Objective

<p>Cloud contamination is a serious obstacle for the application of Landsat data. Thick clouds can completely block land surface information and lead to missing values. The reconstruction of missing values in a Landsat cloud image requires the cloud and cloud shadow mask. In this study, we raised the issue that the quality of the quality assessment (QA) band in current Landsat products cannot meet the requirement of thick-cloud removal. To address this issue, we developed a new method (called Auto-PCP) to preprocess the original QA band, with the ultimate objective to improve the performance of cloud removal on Landsat cloud images. We tested the new method at four test sites and compared cloud-removed images generated by using three different QA bands, including the original QA band, the modified QA band by a dilation of two pixels around cloud and cloud shadow edges, and the QA band processed by Auto-PCP (“QA_Auto-PCP”). Experimental results, from both actual and simulated Landsat cloud images, show that QA_Auto-PCP achieved the best visual assessment for the cloud-removed images, and had the smallest RMSE values and the largest Structure SIMilarity index (SSIM) values. The improvement for the performance of cloud removal by QA_Auto-PCP is because the new method substantially decreases omission errors of clouds and shadows in the original QA band, but meanwhile does not increase commission errors. Moreover, Auto-PCP is easy to implement and uses the same data as cloud removal without additional image collections. We expect that Auto-PCP can further popularize cloud removal and advance the application of Landsat data.     </p><p><strong> </strong></p><p><strong>Keywords: </strong>Cloud detection, Cloud shadows, Cloud simulation, Cloud removal, MODTRAN</p>

Evaluation of Video Image Analysis (VIA) technology to predict meat yield of sheep carcasses online under abattoir conditions

2007 ◽  
Vol 2007 ◽  
pp. 108-108
Author(s):  
E. Rius-Vilarrasa ◽  
L. Bunger ◽  
K. Matthews ◽  
C. Maltin ◽  
A Hinz ◽  
...  
Keyword(s):  
Image Analysis ◽  
Visual Assessment ◽  
Carcass Composition ◽  
Eating Quality ◽  
Beef Industry ◽  
Meat Yield ◽  
A Value ◽  
Fat Score ◽  
The Uk ◽  
The Eu

Accurate estimates of carcass composition and eating quality are critical to the introduction and the success of a value-based marketing system (VBMS) and to help address increased consumer demands for leaner meat with higher quality. Currently in the UK, carcass composition is assessed by a subjective carcass classification system based on the EUROP conformation system, and a visual assessment of fat cover using a numeric fat score (“MLC Scoring”) (Anderson, 2003). Objective, image analysis based systems to classify carcasses into current classification categories have been studied (Allen and Finnery, 2000) and are in use in the beef industry in the EU. However, the introduction of automatic technologies such as VIA may also have considerable potential for prediction of lean meat yield of the carcass. There is growing interest in the possibility of developing payment criteria which are based on carcass meat yield. Therefore, the present research project investigated the potential of VIA technology to predict meat yield in terms of saleable meat yield (SMY), saleable primal meat yield (SPMY) and the carcass components leg, chump, loin and shoulder in lamb.

scholarly journals Recognition the Parameters of Slub-yarn Based on Image Analysis

2011 ◽  
Vol 6 (1) ◽  
pp. 155892501100600 ◽  
Author(s):  
Ruru Pan ◽  
Weidong Gao ◽  
Jihong Liu ◽  
Hongbo Wang
Keyword(s):  
Image Analysis ◽  
Fourier Transform ◽  
High Resolution ◽  
New Method ◽  
Slub Yarn

In this study, a new method for recognizing the parameters of slub-yarn based on image analysis has been proposed. The slub yarn was wrapped on the surface of the black board by YG381 Yarn Evenness Tester. A high resolution scanner was used to acquire the yarn image. Gray stretching and thresholding were carried out to preprocess the image of slub yarn. By separating the slubs from base yarn with different widths, the slub length, slub distance and slub amplitude can be obtained. With the lists of slub length and slub distance, the periodicity rule of slub yarn can be determined. The period of the slubs then will be identified by 1D-Fourier transform. The experiment indicated that the method can identify the parameters of slub yarn with satisfactory results.

Inter-laboratory comparison of Ki-67 proliferating index detected by visual assessment and automated digital image analysis

2019 ◽  
Vol 38 (2) ◽  
pp. 73-79
Author(s):  
Snježana Tomić ◽  
Ivana Mrklić ◽  
Jasminka Jakić Razumović ◽  
Nives Jonjić ◽  
Božena Šarčević ◽  
...  
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Visual Assessment ◽  
Ki 67

Collaborative Study of the Spectrophotometric Determination of Phosphatase Activity in Milk Utilizing a New Alkaline Phosphatase Assay System

1972 ◽  
Vol 55 (3) ◽  
pp. 498-503
Author(s):  
D H Kleyn ◽  
C L Huang
Keyword(s):  
Systematic Error ◽  
Collaborative Study ◽  
Correlation Coefficients ◽  
New Method ◽  
Statistical Comparison ◽  
Alkaline Phosphatase Assay ◽  
Phosphatase Assay ◽  
Method Analysis ◽  
Quantitative Procedure

Abstract A quantitative procedure (modified new method) has been studied that employs phenolphthalein monophosphate as the substrate and dialysis of released phenolphthalein followed by subseqvient measurement of the dialysate in a spectrophotometer at 550 nm. Nine collaborators evaluated 6 unknown samples of milk containing various levels of rawmilk, in triplicate, by the modified new method and the Scharer modified spectrophotometric method. Analysis of variance revealed that the random error of the modified new method was almost twice that of the Scharer technique, while the systematic error of the modified new method was only about ¼ that of the latter method. Two-sample charts indicated that the systematic error of the modified new method was less than that of the Scharer method; this was verified by a statistical comparison which showed that the total analytical error was much lower for the modified new method. A linear relationship was found between the 2 methods by 5 of the collaborators; the correlation coefficients ranged from 0.993 to 0.999. Based on these results, the method has been adopted as official first action for the analysis of milk.

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