Image Recognition of Dyed Fibers and Component Analysis of Cigarette Paper Based on Hue, Saturation, and Value (HSV) Threshold Segmentation

Abstract

Traditional fiber component analysis combining Herzberg and Graff “C” staining methods achieves high accuracy but relies on manual fiber length measurement using an ImageJ software, making it cumbersome and subjective. This study developed a MATLAB-based image preprocessing approach utilizing HSV color space transformation and color threshold segmentation to achieve precise extraction of different fibers from stained microscopic images. Experiments employed four two-component and two three-component mixed slurry samples to compare accuracy and efficiency against the ImageJ method. Optimal color rendering was attained with saturation and lightness gain factors of 1.5 and 1.1 after Herzberg staining and 2.0 and 1.1 after Graff “C” staining. The new method matched ImageJ's accuracy while significantly improving processing efficiency. Applied to commercial cigarette paper, it accurately identified fiber components, consistent with raw material data. Integrating staining techniques with image recognition maintains analytical precision while substantially boosting detection speed. This approach provides an efficient high-throughput solution for cigarette paper fiber analysis with clear industrial application potential.