Object localization/segmentation using generic shape priors

Generally object segmentation is an ill-posed problem. Approaches that use only plain image information will often fail. To overcome these limitations, prior knowledge (like information of the object contour) can be added to the segmentation process. In this paper, we present a novel generic shape model. We use the expertise from the field of object class recognition, namely a boundary-fragment-model (BFM) as prior knowledge for our level set segmentation approach. Commonly, shape models need synthetically generated or pre-segmented training sets that are usually trained on one specific object or a small group of objects. With our new approach we are able to train shape models for whole categories, which makes the segmentation method much more flexible. Additionally we overcome the difficulty of the correct initialization and reduce the segmentation effort. Experimental results demonstrate the excellent performance of our method on different types of objects (categories)