Implementation Of IHS Fusion Technique And Comparative Analysis With PCA Fusion Technique For Cotton Contaminants Detection
Contaminants in cotton have serious effect on the quality of cotton fiber. The removal of cotton contaminants by manual system requires a lot of manpower and time consuming process therefore, automatic cotton contamination detection is used in cotton and other textile industries. Machine vision provides efficient and accurate detection of contaminants based on digital image processing algorithms. Various techniques in this field developed and implemented based on Co-occurrence Matrix Contrast Information, on the Basis of Wavelet, Neighborhood Gradient Based on YCbCr Color Space, using Intensity And Hue Properties, Based on RGB Space Model, using YDbDr Color Space, using X-Ray microtomographic image analysis, Cotton Using Near Infrared Optimal Wavelength Imaging, PCA for Detecting contaminants in Cotton and comparison with various color spaces. Intensity Hue Saturation (IHS) fusion technique has not been implemented for detecting different types of contaminants. Based on PCA and IHS fusion technique, we developed new methods for detecting contaminants in cotton efficiently and effectively. In this research we concluded that the technique based on IHS fusion applied on HSV color space is gives best result in terms of all contaminants detection, less number of false targets and the visual clarity of contaminants.
Keywords—Cotton contaminants; detection; PCA Fusion; IHS Fusion; YCbCr; YDbDR; IHS; HSV; Comparison