Image segmentation using factor graphs

Factor graphs were first studied in the context of error correction decoding and have since been shown to be a useful tool in a wide variety of applications. In this paper, we provide a brief introduction to factor graphs with an emphasis on their broad applicability, and then describe a new algorithm for segmenting binary images that have been blurred and corrupted by additive white Gaussian noise. Though the algorithm is developed for this particular class of images, generalizations are immediate. Simulation results detail the performance of the algorithm for images in three separate blurring conditions. The results suggest the potential for this approach, providing additional evidence of the usefulness of the factor graph framework.