An Active Contour Model with Improved Shape Priors using Fourier Descriptors

Snakes or active contours are widely used for image segmentation. There are many different implementationsof snakes. No matter which implementation is being employed, the segmentation results suffer greatly inpresence of occlusions, noise, concavities or abnormal modification of shape. If some prior knowledge aboutthe shape of the object is available, then its addition to an existing model can greatly improve the segmentationresults. In this work inclusion of such shape constraints for explicit active contours is presented. Theseshape priors are introduced through the use of Fourier based descriptors which makes them invariant to thetranslation, scaling and rotation factors and enables the deformable model to converge towards the prior shapeeven in the presence of occlusion and context noise. These shape constraints have been computed in descriptorspace so no reconstruction is required. Experimental results clearly indicate that the inclusion of these shapepriors greatly improved the segmentation results in comparison with the original snake model.