Stripe Centerline Extraction Based on MATLAB Programming

Introduction: In fields such as optical measurement, industrial inspection, and medical imaging, the analysis of stripe images often relies on accurate centerline extraction. MATLAB, as a powerful numerical computing tool, offers comprehensive image processing functions to meet this requirement through programmable implementations.

The stripe centerline extraction process typically involves these key steps: First, noise reduction is applied to the original image using Gaussian filtering or median filtering to eliminate high-frequency noise interference. Next, edge detection algorithms (such as Canny or Sobel operators) are employed to preliminarily locate stripe boundaries, often resulting in dual-edge structures. The core procedure is skeletonization, where thinning algorithms (e.g., morphological skeleton extraction) converge dual edges into single-pixel-wide centerlines. For discontinuous stripes, morphological closing operations or Hough transform-based line segment connection can repair breaks. In MATLAB code, this can be implemented using functions like `imgaussfilt()` for filtering, `edge()` with 'Canny' option for edge detection, and `bwmorph()` with 'skel' argument for skeletonization.

Two critical issues require attention in practical applications: First, non-uniform lighting may cause local contrast reduction, making background correction advisable prior to processing. Second, intersecting stripes form topological nodes, necessitating connected component analysis to distinguish different branches. MATLAB's Image Processing Toolbox functions such as `bwmorph` and `skeleton` (via `bwmorph(BW,'skel',Inf)`) significantly streamline development. For example, `regionprops()` can analyze connectivity after skeletonization to handle complex stripe intersections.

The method's advantages include compatibility with various stripe types like laser interference fringes and Moiré patterns. By adjusting morphological operation parameters, it adapts to stripes of different widths and curvatures. For sub-pixel precision scenarios, gray-scale centroid methods or Gaussian fitting can be integrated to enhance positioning accuracy, implementable in MATLAB using polynomial fitting functions or custom weighted centroid calculations on local intensity profiles.