Identification of wool and cashmere extremely similar fibers is always an important topic in the textile industry. In order to solve this problem much better, a novel fiber identification method based on the extraction and analysis of the morphological features was proposed in this paper. Firstly, the original fiber images were captured by the self-developed system including the optical microscope and digital camera. The influence of the acquisition process may lead to the low contrast and impurities, so the original fiber images needed to be processed by the image enhancement and de-noise to obtain the available fiber images with a better quality. Then the hessian matrix of processed images was put into the Frangi filter to detect the edge of the fiber scales, and the binary images of filter output images were processed to obtain the signal-pixel scale skeleton. The connected region labeling algorithm can be adopted for the scale skeleton images to mark and extract every scale from the whole fiber according to the different color information. Next, the three morphological features including scale height, fiber diameter and their ratio can be calculated by the self-defined vertical line rotation analysis method, and the mean value of five different scales was calculated as the final features to describe one fiber. In the experiment, 500 fiber cashmere and 500 wool fiber images were collected for the whole research, and a Bayesian classification model for identifying wool and cashmere fibers was established based on the statistical assumptions of three morphological characteristics. The results show that the identification accuracy of the method proposed in this paper could reached the 94.2%. It also proves that this novel method can be used for the identification of cashmere and wool extremely similar animal fibers.
Keywords: Bayesian classification model; Fiber identification; Image processing; Morphological feature analysis; Scale extraction.
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