Contrast-Guided Line Segment Detection

Due to the effects of quantization error and image noise, detecting 'meaningful' line segments from an image with high continuity is a challenging task. To pursue this goal, a novel line segment detector, called the contrast-guided line segment detector (CGLSD), is proposed in this paper. Our basic idea is to integrate a low-level image attribute, i.e., edge contrast , into the line segment detection process for improving line continuity. After applying an edge detector to the input image, the edge contrast is exploited to guide the growth of a line-support region for each line segment individually. This is achieved by evaluating edge pixels as well as those non-edge pixels that are nearby the edges. As a result, some of the non-edge pixels are re-considered as 'edge' pixels and included for establishing the support region. Reversely, certain edge pixels might be treated as 'non-edge' pixels instead and excluded from the region. Since each support region is supposed to yield only one line segment, each formed support region needs to have a refinement by removing those edge pixels that do not belong to it. Lastly, the support region is required to pass through a validation check that might lead to a complete discard of the line segment due to its low confidence. Extensive experiments are conducted and compared with multiple state-of-the-arts on two datasets, including the one from us with manually-annotated ground truth. The results have shown that the proposed CGLSD can deliver superior performance in nearly all test cases.