Polygonal approximation of digital planar curve using novel significant measure
This is an incremental improvement for image processing tasks like boundary detection, offering better preservation of sharp turns in digital curves.
The paper tackles the problem of polygonal approximation of digital planar curves by introducing an iterative smoothing technique that uses a novel significant measure to preserve high-curvature points, and it competes well with state-of-the-art methods.
This paper presents an iterative smoothing technique for polygonal approximation of digital image boundary. The technique starts with finest initial segmentation points of a curve. The contribution of initially segmented points towards preserving the original shape of the image boundary is determined by computing the significant measure of every initial segmentation points which is sensitive to sharp turns, which may be missed easily when conventional significant measures are used for detecting dominant points. The proposed method differentiates between the situations when a point on the curve between two points on a curve projects directly upon the line segment or beyond this line segment. It not only identifies these situations, but also computes its significant contribution for these situations differently. This situation-specific treatment allows preservation of points with high curvature even as revised set of dominant points are derived. The experimental results show that the proposed technique competes well with the state of the art techniques.