A Markov Random Field Model for Extracting Near-Circular Shapes

We propose a binary Markov Random Field (MRF) model that assigns high probability to regions in the image domain consisting of an unknown number of circles of a given radius. We construct the model by discretizing the ‘gas of circles’ phase field model in a principled way, thereby creating an ‘equivalent’MRF. The behaviour of the resultingMRF model is analyzed, and the performance of the new model is demonstrated on various synthetic images as well as on the problem of tree crown detection in aerial images.

• Type: Conference
• Year: 2009
• Where: ICIP
• Authors: Tamas Blaskovics, Zoltan Kato, and Ian Jermyn
• Keywords: ICIP 2009, Image Processing, Markov Random Field, Segmentation, Shape Prior