tailieunhanh - Defect detection in fabric images using two dimensional discrete wavelet transformation technique
In this paper, a method is proposed for recognizing defects in fabric image textures based on two dimensional discrete wavelet transformation techniques. The proposed approach applied to real fabric textures. The proposed algorithm shows good result to detect all types of defects occurred in fabric images. High detection rate and low computational complexity are advantages of this proposed approach. | ISSN:2249-5789 T D Venkateswaran et al , International Journal of Computer Science & Communication Networks,Vol 4(1),33-40 DEFECT DETECTION IN FABRIC IMAGES USING TWO DIMENSIONAL DISCRETE WAVELET TRANSFORMATION TECHNIQUE Research Scholar, Department of Computer Science, Madurai Kamaraj University, Madurai, India. thadanvenkateswaran@ Senior Professor and Head, Department of Computer Science, Madurai Kamaraj University, Madurai, India. gurusamyarumugam@ Abstract Defect recognition is one of the problems in image processing and many different methods based on texture analysis have been proposed. In this paper, a method is proposed for recognizing defects in fabric image textures based on two dimensional discrete wavelet transformation techniques. The proposed approach applied to real fabric textures. The proposed algorithm shows good result to detect all types of defects occurred in fabric images. High detection rate and low computational complexity are advantages of this proposed approach. Keywords: Defect Detection, Image Processing, Discrete wavelet transformation technique. 1. Introduction Today thanks to advances in machine visions and hardware, monitoring and classification process of industrial products can be performed automatically using intelligent software and high speed hardware. Visual quality inspection system play an important role in many industrial and commercial applications such as tiles, metal, agricultural products, fabric, ceramic, paper and etc. Any hole, damage, abnormalities and slot in products surfaces are called defect. Ghazini et al. proposed a defect detection approach of tiles using combination of two dimensional wavelet transform and statistical features. Henry et al. used ellipsoidal region features and min-max technique on patterned fabric for detecting defects. Ch. Lin et al., described a texture defect detection system based on image deflection compensation. Tolba used a .
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