Before revision:  In part, this is because we lack an ideal observer model of segmentation to guide constrained experiments. On the other hand, despite recent progress in machine learning, modern algorithms still fall short of human segmentation performance. Our goal here is two-fold (i) propose a model to probe human visual segmentation mechanisms and (ii) develop an efficient algorithm for image segmentation. To this aim , we propose a novel probabilistic generative model of visual segmentation that for the first time combines 1 ) knowledge about the sensitivity of neurons in the visual cortex to statistical regularities in natural images;
After revision:  In part, this is because we lack segmentation models consistent with experimental and theoretical knowledge in visual neuroscience. Biological sensory systems have been shown to approximate probabilistic inference to interpret their inputs. This requires a generative model that captures both the statistics of the sensory inputs and expectations about the causes of those inputs. Following this hypothesis , we propose a novel probabilistic generative model of visual segmentation that for the first time combines 1 ) knowledge about the sensitivity of neurons in the visual cortex to statistical regularities in natural images;