| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 533360 | Pattern Recognition | 2012 | 9 Pages |
In this paper we describe a framework to learn a model of shape variability in a set of patterns. The framework is based on the Active Appearance Model (AAM) and permits to combine shape deformations with appearance variability. We have used two modifications of the Blurred Shape Model (BSM) descriptor as basic shape and appearance features to learn the model. These modifications permit to overcome the rigidity of the original BSM, adapting it to the deformations of the shape to be represented. We have applied this framework to representation and classification of handwritten digits and symbols. We show that results of the proposed methodology outperform the original BSM approach.
► We propose a methodology for modeling shape appearance variations. ► We have proposed two different deformable shape descriptors tolerant to deformations. ► We use these deformable shape descriptors to build the appearance model. ► We integrate the appearance models in two classification schemes. ► The validity of the method to deal with deformations is shown for shape recognition.
