Stripe Centerline Extraction Using MATLAB Programming

Stripe centerline extraction is a crucial task in image processing, widely applied in optical measurement, structured light analysis, and related fields. Implementing this functionality in MATLAB requires integrating multiple image processing techniques. The following breakdown explains the implementation approach: Image Preprocessing: First, denoise the stripe image using Gaussian filtering or median filtering to eliminate noise, ensuring accuracy in subsequent processing steps. In MATLAB, this can be implemented using functions like imgaussfilt() or medfilt2() with appropriate kernel sizes. Edge Detection: Commonly employ operators such as Canny, Sobel, or Prewitt to detect stripe edges. This step helps establish the preliminary contour of the stripes. MATLAB's edge() function provides direct implementation with different method parameters (e.g., 'canny', 'sobel'). Binarization Processing: Convert the stripe image to binary using threshold segmentation methods like Otsu's algorithm, making it easier to distinguish between stripe and non-stripe regions. The graythresh() and imbinarize() functions in MATLAB automate optimal threshold selection and conversion. Thinning Algorithm: Extract the stripe centerline using skeletonization or morphological thinning techniques such as the Zhang-Suen algorithm. This critical step ensures edges are refined to single-pixel width. MATLAB's bwmorph() function with 'thin' or 'skel' options implements these algorithms efficiently. Post-processing Optimization: Apply smoothing or breakpoint connection processing to the extracted centerline, removing unnecessary burrs or disconnected points to enhance readability and applicability. Functions like bwareaopen() can remove small objects, while morphological operations can connect gaps. The advantage of this method lies in leveraging MATLAB's powerful matrix operations and Image Processing Toolbox, enabling efficient stripe centerline extraction suitable for both research and industrial inspection scenarios. The implementation typically involves chaining these operations with parameter tuning for specific image characteristics.