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.