Accurate Similarity Measures for Silhouettes Recognition

In this paper, we propose a new method to recognize silhouettes of objects. Models of silhouettes are stored in the database using their textual descriptors. Textual Descriptors are written following the part-based method published in (Larabi et al, 2003). The main issue with the textual description is its sensitiveness to noise, in order to overcome this issue, we have applied (Aouat and Larabi, 2010) a convolution to initial outline shape with a Gaussian filter at different scales. The approach was very interesting for shape matching and indexing (Aouat and Larabi, 2009), but unfortunately it is not appropriate to the recognition process because there is no use of similarity measures in order to select the best model for a query