Contour Completion of Partly Occluded Objects Based on Figural Goodness

Object extraction has a crucial role in various visual semantic scenarios. In this paper, we propose a system for object extraction which can deal with an object partly occluded by other objects. In order to estimate the hidden part of the occluded object, the system combines different types of contour completion methods such as curve completion and symmetry completion. The system is composed of two modules: contour discrimination and contour completion. The contour discrimination module separates the contour of the occluded object from the contours of the occluding objects. To the contour of the occluded object, the contour completion module applies different types of contour completion algorithms to generate various completion patterns. Based on the perceptual model of figural goodness, the system computes the figural goodness scores of the generated patterns, where the simplicity and symmetricity are evaluated. Finally, the system outputs the pattern having the highest score as a result of the object extraction. From the experimental results, we have confirmed that the figural goodness model works effectively for extracting partly occluded objects.