Extraction of line segments in cluttered images via multiscale edges

Current methods for line segment extraction often fail in challenging scenarios that abound in real-life images, e.g., those containing corrupted lines, of various widths, with multiple crossings, and immersed in clutter. We propose a method that tackles these issues by combining multiscale edges while taking line segment connectivity into account. In particular, we use two scales originating what we call contextual and local edges, obtained with filters of, respectively, large and small footprints. Contextual edges are robust to noise and our method uses them validate local edges, i.e., to only select the local edges that correspond to the same intensity transition (dark-to-bright or vice-versa). Line segment connectivity is enforced by joining the valid local edges whose distance does not exceed a threshold. To enable dealing with situations where the edges divide regions of non-uniform intensity distributions, e.g., textures, the contextual edges are decided by using a two-sample statistical test. We present experiments that illustrate how our method is efficient in extracting complete segments in several situations where current methods fail.