Textile Image Classification | Categorizing huge amount of textile images efficiently

R¿fat A¿l¿yan

ISBN 10: 3838357329 / ISBN 13: 9783838357324

Published by LAP LAMBERT Academic Publishing, 2010

About this Item

Textile Image Classification | Categorizing huge amount of textile images efficiently | R¿fat A¿l¿yan | Taschenbuch | 116 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838357324 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 101193615

Bibliographic Details

• Title: Textile Image Classification | Categorizing huge amount of textile images efficiently
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2010
• Language: English
• ISBN 10: 3838357329
• ISBN 13: 9783838357324
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 7D millimeters 220 millimeters width by 150 millimeters height by 7 millimeters depth
• Weight: 191 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Today, according to the increasing development of the information technologies the number of the images in textile industry is rising very fast. It seems that the textile images cannot be processed with manually. It is required that textile images are categorized as suitable classes to profit from these images. In this book, it is explained how huge textile images are classified into striped, plaided and speckled image classes. Striped images are also categorized into the classes of horizontal, vertical, 45 and 135 degree lines. Line and circle features have been used for the classification methods. To detect line and circle features of the images, it is applied to line detection operator and Hough transform method respectively. With the line feature, striped and plaided images are classified, but speckled images are found with circle feature. For the classification of the textile images, some classification methods are used as Manual Thresholding, Nearest Neighbor, k-Nearest Neighbor, Minimum Distance. The system has been evaulated in detail, and the experimental results show that the developed system categorized the textile images successfully.