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2007
Springer

Multi-class Binary Object Categorization Using Blurred Shape Models

9 years 8 months ago
Multi-class Binary Object Categorization Using Blurred Shape Models
The main difficulty in the binary object classification field lays in dealing with a high variability of symbol appearance. Rotation, partial occlusions, elastic deformations, or intra-class and inter-class variabilities are just a few problems. In this paper, we introduce a novel object description for this type of symbols. The shape of the object is aligned based on principal components to make the recognition invariant to rotation and reflection. We propose the Blurred Shape Model (BSM) to describe the binary objects. This descriptor encodes the probability of appearance of the pixels that outline the object’s shape. Besides, we present the use of this descriptor in a system to improve the BSM performance and deal with binary objects multi-classification problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split object classes. Then, the different binary problems learned by the Adaboost are embedded in the Error Correcting Output ...
Sergio Escalera, Alicia Fornés, Oriol Pujol
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where CIARP
Authors Sergio Escalera, Alicia Fornés, Oriol Pujol, Josep Lladós, Petia Radeva
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