Concurrent Lines Perpendicular Distance Functions for Contour Points Analysis

In this article, we have developed a novel Concurrent Lines Perpendicular Distance Functions (CLPDF) base shape descriptor. The shortest distance from object contour points to the family of multi-line passes through a fixed point have provided the insight into the functioning of object recognition, matching, registration and analysis of two-dimensional (2-D) binary shape silhouettes. In this method, we have computed the perpendicular distance from each point of an object contour to the family of concurrent lines passing through a fixed point. The fixed point is the centre of gravity of a shape. Geometrically (translation and rotation) invariant object feature for shape-preserving classification in pattern recognition and image processing are characterized by the perpendicular distance functions. To make CLPDF scale invariant, we introduce a normalization function for CLPDF base shape descriptor. In the matching stage, we compute and analysis the euclidean distance between eigenvalues of two shapes corresponding to the concurrent lines perpendicular distance functions. The CLPDF methodology in pattern recognition and classification gives an excellent discriminative power, which is demonstrated by excellent retrieval performance that has been experimented on several shape benchmarks data sets, including Kimia silhouettes, MPEG7 CE-Shape-1 data set