classification and clustering techniques textile
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classification and clustering techniques textile




A review of recent advances in surface defect detection using texture analysis techniques
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The authors also investigated various values of K in terms of classification accuracy.  [84] also applied KNN to classify filter responses and wavelet  Recently, Pernkopf [109] classified steel surfaces based on data likelihood computed from coupled hidden Markov random fields. 

Model-based methods for textile fault detection
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 The final classification is an extremely elongated elliptical cluster (black) with a Poisson noise background (white). Vol. 10, 339–346 (1999) 345 Page 8. G. Celeux and G. Govaert, Gaussian parsimonious clustering models, Pattern Recognition 28 (1995), 781–793. 

Feature Extraction And Classification Of Textile Images: Towards a Design Information System for the Textile Industry.
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 The proposed methodology to perform the analysis and classification of fabric models is based on the  It is possible to classify the design by using the theory of groups of  The Mean-shift is a non-parametric clustering technique recently used for colour image segmentation [6]. It is 

A soft computing approach to classification of trash in ginned cotton
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 feature vector representing the trash types are identified, the neural networks and the clustering algorithm can be used to classify the different  Table 4. Classification - ANFIS  TA114 Classified Total Actual Bark1 Bark2 Leaf Pepper 2 Bark1  

Application of corpus-based techniques to Amharic texts
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 the result using 10-fold cross validation as test mode for classification is also shown in column 6. The accuracy of the classification algorithm is  Most adjectives and adverbs are incorrectly classified as nouns.  Clustering seems to be a difficult task as the above results show. 

Colour Classification Method for Recycled Melange Fabrics
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sensor very small (about 20 mm2) this tool is not able to properly classify melange fabrics  Some results of the classification performed by the pickers are provided in Table 1. Image acquisition  of a database of 1440 images each one representing a fabric to be classified in terms 

Mining the body features to develop sizing systems to improve business logistics and marketing using fuzzy clustering data mining
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 This study is aimed to classify the techniques of data mining into below 6 types as shown Table 2 [7, 27]. Table 2 The technique type of data mining Type Description  Based on these rules, the body types classification of Taiwanese adult females may be classified. 

Application of the image analysis technique for textile identification
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 Properties of Spliced Yarns by Regression and Neural Network Techniques', Textile Research Journal  Huang Chang-Chiun, Chen I-Chun, 'Neural-Fuzzy Classification for Fabric Defects  Color and Pattern Analysis of Printed Fabric by an Unsupervised Clustering Method', Textile 

Employing a three-stage data mining procedure to develop sizing system
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 there is a significant differences in the waist girth, body girth being predictors used to classify the target variable.  of division. Table 2 shows the classification rules for and waist girth.  control dimensions and size interval and second, clusters could be classified to the sub-clusters 

The Use of K-Means and Kohonen Self Organizing Maps to Classify Cotton Bales
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 The model is used to classify 2421 cotton bales whose HVI data containing 13 cotton attributes, was obtained  This is an indication that the vector is correctly classified.  The bale classification model can be summarized as follows; (i) Use SOM data visualization technique to get 

Implementing A Data Mining Solution To Customer Segmentation For Decayable Products–A Case Study For A Textile Firm
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 a heterogeneous population into a number of more homogenous clusters to filter, classify, and extract  There are numerous machine learning techniques available for classification model.  Applying some index functions, it is possible to obtain an optimum clustering, but some 

Design and Development of an Algorithm for Image Clustering In Textile Image Retrieval Using Color Descriptors
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 that similar data objects belong to the same group and dissimilar data objects to different clusters [1,9  person identification in movie clips and festive home videos, recognition in biometric system, natural scene classification for robot  4. IMAGE CLUSTERING OF TEXTILE IMAGES 

A Novel Evaluation Method of Visual Impression of Black Fabrics
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 The classification sensitivity of a data  classify black fabrics objectively by unifying the sensory test and the image features for the texture characteristics  (2001) 7. Asano Muraki C., Asano A., Mori M. and Fujimoto T. Applications of Image Processing to Classifications of 

Use of SOM to Study Cotton Growing and Spinning
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 The collected data was used to classify the cotton bales using a model consisting of Kohonen  2.3 k-means Clustering Technique (for bale classification) Clustering algorithms attempt to organize unlabeled feature vectors  This is an indication that the vector is correctly classified. 

Artificial Neural Network Prosperities in Textile Applications
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M Maleki ,cdn.intechweb.org  All nonwoven samples were classified into five grades according to visual qualities (such as  The classification rate amounted to 80% correct classifications, the rest differed from the  a hybrid model (integration of genetic algorithm and neural network) to classify garment defects. 

A NEW APPROACH TO THE UNSUPERVISED DETECTION AND CLASSIFICATION OF THE SPLICED YARN JOINT
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 This database consists of two different kinds of classified images, that is, 280 standard  Moreover, the proposed method could be used to detect and classify splice joints  7. Stanislaw Lewandowski, Tomasz Stanczyck, 'Identification and classification of spliced wool combed yarn 


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