Robust Subpixel Centerline Extraction of Dense Laser Stripes Using PAE-Constrained Anisotropic Gaussian Modeling

Subpixel centerline extraction of dense laser stripes is critical for high-precision 3D measurement systems, yet traditional methods suffer from significant localization errors under noise, blur, and closely spaced stripe conditions—challenges that severely limit measurement accuracy in industrial metrology. This paper presents a novel extraction method based on the PAE-Constrained Anisotropic Gaussian framework (PAE-AGK) to achieve robust subpixel accuracy. Unlike conventional approaches that assume intensity continuity, the partial area effect (PAE) treats pixel intensity as area-weighted averages across edges, enabling precise estimation of subpixel edge position, orientation, curvature, and contrast under adverse conditions. These PAE-derived geometric features serve as physically meaningful priors to constrain anisotropic Gaussian kernel construction, eliminating iterative parameter optimization while ensuring geometric-photometric consistency. The method operates in three stages: Stage I—PAE-based local edge model and stripe geometry estimation; Stage II—anisotropic Gaussian kernel construction with parameters fully determined from Stage I; Stage III—coarse localization via scale-normalized second-order response and analytic subpixel refinement along the normal direction. Comprehensive experimental validation demonstrates superior performance over traditional methods, achieving root mean square error (RMSE) of 0.0131–0.0289 pixels under noise-free conditions and maintaining 0.0135–0.0992 pixels under noisy conditions—substantially outperforming classical algorithms including Steger, Gray-Gravity and Geometric-Center methods. The method successfully extracts centerlines in dense scenarios with inter-stripe distance as low as 12 pixels. Qualitative evaluation on diverse real-world materials validates practical applicability for precision measurement and 3D reconstruction applications.