Polygonal approximation of digital planar curve using novel significant measure

TL;DR

This paper introduces a novel iterative smoothing method for polygonal approximation of digital curves, emphasizing a new significant measure that better preserves sharp features and curvature.

Contribution

The paper proposes a new significant measure and a situation-specific approach for polygonal approximation that improves shape preservation over existing methods.

Findings

The method effectively preserves sharp turns and high curvature points.

It competes well with state-of-the-art techniques in shape approximation.

Experimental results demonstrate improved shape fidelity.

Abstract

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.