Shape detection from line drawings with local neighborhood structure

An object detection method from line drawings is presented. The method adopts the local neighborhood structure as the elementary descriptor, which is formed by grouping several nearest neighbor lines/curves around one reference. With this representation, both the appearance and the geometric structure of the line drawing are well described. The detection algorithm is a hypothesis-test scheme. The top k most similar local structures in the drawing are firstly obtained for each local structure of the model, and the transformation parameters are estimated for each of the k candidates, such as object center, scale and rotation factors. By treating each estimation result as a point in the parameter space, a dense region around the ground truth is then formed provided that there exist a model in the drawing. The mean shift method is used to detect the dense regions, and the significant modes are accepted as the occurrence of object instances.