Measuring linearity of open planar curve segments
Image and Vision Computing
A parts-based multi-scale method for symbol recognition
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
Multi-modal descriptors for multi-class hand pose recognition in human computer interaction systems
Proceedings of the 15th ACM on International conference on multimodal interaction
Spectra of shape contexts: An application to symbol recognition
Pattern Recognition
A new geometric descriptor for symbols with affine deformations
Pattern Recognition Letters
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In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations.