2026-06-10T16:01:34.104Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:2:7659 2020-05-10T13:14:21Z 2

Revisiting spectral clustering for near-convex decomposition of 2D shape 2020 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/7659 https://www.sciencedirect.com/science/article/abs/pii/S0031320320301746 Z. Li J. Hu M. Stojmenović Z. Liu W. Liu We present a novel 2D shape decomposition algorithm via a recursive partitioning process. Starting with the contour points of a shape, we repeatedly separate the points into two parts by spectral clustering, until the stopping condition is met. Motivated by the fact that the points in a convex part are mutually visible, we regard the visibility matrix of points as the affinity matrix of spectral clustering to obtain a near-convex decomposition. Additionally, we present an efficient stopping rule to avoid over-segmentation on the shape branches. The stopping criterion is based on a novel shape signature called visible protrusion strength which can be used to measure the segmentability of a sub-shape. Finally, we demonstrate the efficiency of our algorithm on a variety of publicly available shapes, and provide qualitative and quantitative comparisons with state-of-art approaches. article 105 1 16 0031-3203 PATTERN RECOGNITION