Panda, S. and Nanda, P.K. (2009) Constrained Compound Markov Random Field Model with Graduated Penalty Function for Color Image Segmentation. IEEE International Conference on Control, Robotics and Cybernetics (ICCRC-2011), Delhi, 19-20 March 2009, VI-126-VI-132.

has been cited by the following article:

• TITLE: Color and Texture Segmentation Using an Unified MRF Model

  AUTHORS: Sucheta Panda, Pradipta Kumar Nanda

  KEYWORDS: Color Image, Color Model, Image Segmentation, Simulated Annealing, MRF Model

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.6, June 30, 2022

  ABSTRACT: The color image segmentation problem has two main issues to be solved. The proper choice of a color model and the choice of an appropriate image model are the key issues in color image segmentation. In this work, Ohta (I1, I2, I3) is taken as the color model and different variants of Markov Random Field (MRF) models are proposed. In this regard, a Compound Markov Random Field (COMRF) model is porposed to take care of inter-color-plane and intra-color-plane interactions as well. In continuation to this model, a Constrained Compound Markov Random Field Model (CCOMRF) has been proposed to model the color images. The color image segmentation problem has been formulated in an unsupervised framework. The performance of the above proposed models has been compared with the standard MRF model and some of the state-of-the-art methods, and found to exhibit improved performance.