Compressing 2-D Shapes Using Concavity Trees

Concavity trees have been known for quite some time as structural descriptors of 2-D shape; however, they haven’t been explored further until recently. This paper shows how 2-D shapes can be concisely, but reversibly, represented during concavity tree extraction. The representation can be exact, or approximate to a pre-set degree. This is equivalent to a lossless, or lossy compression of the image containing the shape. This paper details the proposed technique and reports nearlossless compression ratios that are 150% better than the JBIG standard on a test set of binary silhouette images.

• Type: Conference
• Year: 2005
• Where: ICIAR
• Authors: Ossama El Badawy, Mohamed S. Kamel
• Keywords: 2-d Shapes, Concavity Tree, Concavity Tree Extraction, ICIAR 2005