Engineering drawing processing and vectorization system
Computer Vision, Graphics, and Image Processing
A simple algorithm for computing the smallest enclosing circle
Information Processing Letters
Local sine and cosine bases of Coifman and Meyer and the construction of smooth wavelets
Wavelets: a tutorial in theory and applications
Perfecting vectorized mechanical drawings
Computer Vision and Image Understanding
Recognition of 2D Object Contours Using the Wavelet Transform Zero-Crossing Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape characterization with the wavelet transform
Signal Processing
Orthonormal ridgelets and linear singularities
SIAM Journal on Mathematical Analysis
Invariant 2D object recognition using the wavelet modulus maxima
Pattern Recognition Letters
Rotation-invariant pattern matching using wavelet decomposition
Pattern Recognition Letters
Technical Symbols Recognition Using the Two-Dimensional Radon Transform
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Radon Transform for Lineal Symbol Representation
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
On the Combination of Ridgelets Descriptors for Symbol Recognition
Graphics Recognition. Recent Advances and New Opportunities
Palmprint Linear Feature Extraction and Identification Based on Ridgelet Transforms and Rough Sets
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
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Ridgelets transform, related to the wavelets and the Radon transform, offers a sound mathematical framework to organize linear information at different scales of resolution. We use this multiresolution property to define a representation scheme at several levels of decomposition to describe images whose relevant information is concentrated around linear singularities. We have also defined a classification procedure that combines information from all levels and is invariant to usual affine transformations. We have applied this scheme to the description and classification of linear graphic symbols, achieving recognition rates over 97% with the GREC'03 symbol database, even with degraded and distorted images.