Fast recognition of foreign fibers in cotton lint using machine vision
This paper presents an approach for fast segmentation of foreign fiber images and precise recognition of foreign fiber objects using machine vision. Live images were acquired in real time using a line scan CCD camera. After an image was acquired it was transferred to a host computer immediately for image processing and object classification. The captured image was firstly segmented according to the mean and standard deviation of R, G and B values of each pixel in the image. Then noises were removed using the area threshold method. Afterwards, color features, shape features and texture features of each foreign fiber object were extracted. Finally, a one-against-one directed acyclic graph multi-class support vector machine (OAO-DAG MSVM) was constructed and used to perform the classification. The results indicate that the image processing algorithm is fast and precise; the OAO-DAG MSVM gets a mean accuracy of 92.34％ and a mean classification time of 12 ms, which can satisfy the accuracy and speed requirement of online classification of foreign fibers.
Foreign fiber Image segmentation Object classification Machine vision
Wenzhu Yang Sukui Lu Sile Wang Daoliang Li
College of Mathematics and Computer Science, Hebei University, Baoding 071002, China College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, Ch
国际会议
南昌
英文
877-882
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）