Authors:
Saliha Aouat
and
Slimane Larabi
Affiliation:
University of Sciences and Technology – Houari Boumediene, Algeria
Keyword(s):
Textual Descriptors, Noise, Similarity Measures, Indexing, Recognition, Quasi-invariants, Parts Areas.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Shape Representation and Matching
Abstract:
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 silhouette.
In this paper, we compute parts areas and geometric quasi-invariants to find the best model for the given query; they are efficient similarity measures to perform the recognition process.