Color Clustering Analysis of Yarn-dyed Fabric in HSL Color Space

2009 ◽  
Author(s):  
Ruru Pan ◽  
Weidong Gao ◽  
Jihong Liu
Keyword(s):  
Clustering Analysis ◽  
Color Space ◽  
Color Clustering ◽  
Dyed Fabric

Automatic Defect Detection of Yarn-Dyed Fabrics Based on Energy Fusion and Local Binary Patterns

2012 ◽  
Vol 472-475 ◽  
pp. 3039-3042 ◽  
Author(s):  
Wen Yu Li ◽  
Long Di Cheng ◽  
Wen Liang Xue
Keyword(s):  
Defect Detection ◽  
Color Space ◽  
Local Binary Patterns ◽  
High Detection Rate ◽  
Energy Feature ◽  
Dyed Fabrics ◽  
Dyed Fabric ◽  
True Color ◽  
Real Time Application ◽  
Log Gabor

For the purpose of realizing fast and effective detection of defects of yarn-dyed fabric, and in consideration of the inherent characteristics of texture, i.e., color and structure, an approach for automatic defect detection is proposed in this paper. The image of yarn-dyed fabric to be enhanced is first converted from RGB true color space to L*a*b* color space. Then Log-gabor filters filter chromatic and brightness channels, and energy feature images are acquired after energy is fused between chromatic and brightness. Finally defects of yarn-dyed fabrics can be detected on the energy feature images using local binary patterns. The proposed method can detect colored and structural flaws. Experimental results for the defect detection from six kinds of yarn-dyed fabrics indicate that a high detection rate is achieved for the proposed method. It is fast enough to be possible for real-time application.

scholarly journals Color and fastness properties of mordanted Bridelia ferruginea B dyed cellulosic fabric

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Jamiu Mosebolatan Jabar ◽  
Ademola Israel Ogunmokun ◽  
Tella Adewale Akanni Taleat
Keyword(s):  
Calcium Chloride ◽  
Metal Ion ◽  
Color Space ◽  
Fastness Properties ◽  
Color Strength ◽  
Bridelia Ferruginea ◽  
Yellow Color ◽  
Dyed Fabrics ◽  
Cellulosic Fabric ◽  
Dyed Fabric

AbstractBridelia ferruginea B dye was extracted from the bark of the tree using aqueous extraction method. Extracted dye was used to dye cellulosic (cotton) fabric in presence of 5% calcium chloride (CaCl2) or 5% alum (KAl(SO4)2·12H2O) of weight of fabric (o.w.f) as mordant. Fabric dyed without mordant was lighter in hue than metal ion mordanted dyed fabrics. The fabrics dyed in presence of calcium chloride as mordant are of deeper hue than those dyed with alum as mordant. Hence, their dye-uptake and color strength (K/S) are in the same order. K/S value of fabric dyed with alum is 43.71% higher than that without mordant and fabric dyed with calcium chloride has K/S value 51.09% higher than dyed with alum as mordant. CIEL*a*b* coordinate indicator and color space quadrant showed that those dyed fabrics without mordant and with alum as mordant are closer to yellow than red color. Those cellulosic fabrics dyed with calcium chloride as mordant are closer to red than yellow color as confirmed in colour space quadrant. Pre-mordanted dyed fabrics are of deeper color than post-mordanted dyed fabrics than meta-mordanted dyed fabrics than unmordant dyed fabric. Fastness properties of B. ferruginea B dyed cellulosic fabrics ranged from good (3) to excellent (5).

ROBUST IMAGE RETRIEVAL APPROACH BASED ON CLUSTERING ANALYSIS OF RE-QUANTIZED COLOR IMAGE

2007 ◽  
Vol 05 (01) ◽  
pp. 213-225 ◽  
Author(s):  
YA-LI JI ◽  
XIAO-PING CHENG ◽  
NAI-QIN FENG
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Color Space ◽  
Feature Representation ◽  
Hsv Color Space ◽  
Invariant Moments ◽  
Robust Image ◽  
Color Clustering ◽  
Object Features ◽  
Large Image Databases

In this paper, we propose a robust approach about color image retrieval. It can realize fast matching in CBIR (Content-Based Image Retrieval) when we search in large image databases. Indexes root in object features of Z image which is the result of re-quantization in HSV color space, matching with a non-geometrical distance is based on objects, so time consumption pixel by pixel can be avoided. Because Z image is made up of many color clustering regions and invariant moments are used for feature representation, our approach is robust to translation, scale and rotation.

Segmentation of Crop Disease Images with an Improved K-means Clustering Algorithm

2018 ◽  
Vol 34 (2) ◽  
pp. 277-289 ◽  
Author(s):  
Zhibin Wang ◽  
Kaiyi Wang ◽  
Shouhui Pan ◽  
Yanyun Han
Keyword(s):  
Clustering Algorithm ◽  
Color Space ◽  
Entropy Method ◽  
Adaptive Segmentation ◽  
Segmentation Method ◽  
Threshold Method ◽  
Initial Cluster ◽  
Crop Disease ◽  
Color Clustering ◽  
Spot Image

Abstract. Disease spot segmentation from crop leaf images is a key prerequisite for disease early warning and diagnosis. To improve the accuracy and stability of disease spot segmentation, an adaptive segmentation method for crop disease images based on K-means clustering is proposed. The approach is based on three stages. First, the excess green feature and the a* component of the CIE (L*a*b*) color space were combined to adaptively learn the initial cluster centers. Second, iterative color clustering of two clusters was conducted using the squared Euclidian distance as the similarity distance. Finally, the distance of a* components between two clusters as the clustering criterion function was used to correct the clustering results. To verify the effectiveness of the proposed method, segmentation experiments were performed on images of three kinds of cucumber diseases and one kind of soybean disease. The results of the experiments were compared with the results obtained using a fixed threshold method, the Otsu method, the traditional K-means clustering method, and the Renyi entropy method, which showed that our adaptive segmentation method was accurate and robust for segmentation of crop disease images. Keywords: Adaptive, CIE L*a*b*, Disease spot, Image segmentation, K-means clustering.

Application of Clustering Analysis and Discriminant Analysis on Quality Grade Evaluation of Jadeite-Jades’ Green Color

2013 ◽  
Vol 591 ◽  
pp. 343-347
Author(s):  
Huan Wang ◽  
Ying Guo ◽  
Hong Mei Du
Keyword(s):  
Discriminant Analysis ◽  
Clustering Analysis ◽  
Color Space ◽  
Color Measurement ◽  
Fisher Discriminant Analysis ◽  
Function Model ◽  
Linear Discriminant ◽  
Green Color ◽  
Quality Grading ◽  
Dark Green

Based on CIE1976 L*a*b* uniform color space, 381 jadeite-jades’ green color were measured by Color i5 spectrophotometer. By applying clustering analysis and discriminant analysis, a classifying and grading function model of jadeite-jades’ green color is tried to be established according to L*, a* and b* variable factors. The results showed that jadeite-jades’ green color were divided well into six categories by K-Means clustering analysis, and ANOVA showed L*, a* and b* values among various categories were significantly different (all p <0.01). The green color of Category 5 with moderate brightness and high chroma outperformed the others, followed by Category 3. The green color of Category 2 with higher brightness and chroma was appraised to be better than the deep green color of Category 3. Dark green of Category 4 and faint green of Category 6 were in the minimum level of the quality. A linear discriminant function model of the six categories of jadeite-jades’ green color was established by Fisher discriminant analysis, which had excellent performance of discriminant with the model checking accuracy of 95.80%.It is concluded that the method of color measurement used by Color i5 spectrophotometer combined with clustering analysis and discriminant analysis offers an efficient, reasonable and feasible approach to the fast classifying and quality grading evaluation of jadeite-jades’ green color.

IDEAL COLOR SPACE COMPONENT FOR RECONSTRUCTION OF CONTAMINATION PLUMES

2013 ◽  
Vol 20 (3) ◽  
pp. 125-159 ◽  
Author(s):  
Sina Kashuk ◽  
Sophia R. Mercurio ◽  
Magued Iskander
Keyword(s):  
Color Space

2249-PUB: Clustering Analysis as a Tool to Define Endotypes in Type 1 Diabetes (T1D)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2249-PUB
Author(s):  
ALEJANDRO F. SILLER ◽  
XIANGJUN GU ◽  
MUSTAFA TOSUR ◽  
MARCELA ASTUDILLO ◽  
ASHOK BALASUBRAMANYAM ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Clustering Analysis

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.

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