Finding axes of skewed symmetry
Computer Vision, Graphics, and Image Processing
An introduction to contemporary statistics (2nd ed.)
An introduction to contemporary statistics (2nd ed.)
Emulating the human interpretation of line-drawings as three-dimensional objects
International Journal of Computer Vision
An optimization-based approach to the interpretation of single line drawings as 3D wire frames
International Journal of Computer Vision
Creating solid models from single 2D sketches
SMA '95 Proceedings of the third ACM symposium on Solid modeling and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Estimating depth from line drawing
Proceedings of the seventh ACM symposium on Solid modeling and applications
Support Vector Mixture for Classification and Regression Problems
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Recognizing Geometric Patterns for Beautification of Reconstructed Solid Models
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Interpreting a 3D object from a rough 2D line drawing
VIS '90 Proceedings of the 1st conference on Visualization '90
Regularity selection for effective 3D object reconstruction from a single line drawing
Pattern Recognition Letters
An interactive sketching method for 3D object modeling
Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts
Technical Section: An optimisation-based reconstruction engine for 3D modelling by sketching
Computers and Graphics
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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In automatic reconstruction of 3D objects from single line drawings, existing systems are all single-track, containing one general solution for all drawings. This paper proposes a method in which an input drawing is first classified based on dominant features which exist in the drawing, including symmetry, orthogonality and parallelism. The reconstruction is then performed by experts to deal with each class specifically. Drawing classification is done using the technique of support vector machine classification. A specific set of features are selected to form an optimal regularity set for each class, and used in the formulation of the objective function for effective reconstruction. Experimental results show that the proposed system can improve the reconstruction accuracy and efficiency than that of a single-track general 3D reconstruction system.