Prediction-Correction Line Segment Detection

Abstract

Edge-drawing methods have gained increasing popularity in line segment detection due to their notable efficiency. However, existing algorithms commonly impose a pre-determined threshold on the gradient magnitude of the input image to control false positives, which could lead to the detection of line segments with insufficient completeness. To address this fundamental problem, we propose a novel method called the prediction-correction line segment detector (PCLSD). The PCLSD initiates with a prediction stage utilizing a Canny-based approach to generate line segment predictions. In the subsequent correction stage, each predicted line segment undergoes refinement. Specifically, a directional routing method is employed to extend and refit the line segment, improving the accuracy of its orientation, position, and completeness. The corrected line segment is then validated to ensure confidence. Experimental results demonstrate the superior performance of the proposed PCLSD compared to current state-of-the-art methods.