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
Bayesian methods for adaptive models
Bayesian methods for adaptive models
An optimization-based approach to the interpretation of single line drawings as 3D wire frames
International Journal of Computer Vision
The nature of statistical learning theory
The nature of statistical learning theory
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Teddy: a sketching interface for 3D freeform design
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
On domain knowledge and feature selection using a support vector machine
Pattern Recognition Letters
A Graph-Based Method for Face Identification from a Single 2D Line Drawing
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
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
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Identifying Faces in a 2D Line Drawing Representing a Manifold Object
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection and Dualities in Maximum Entropy Discrimination
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Bayesian trigonometric support vector classifier
Neural Computation
Interpreting a 3D object from a rough 2D line drawing
VIS '90 Proceedings of the 1st conference on Visualization '90
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Technical Section: An optimisation-based reconstruction engine for 3D modelling by sketching
Computers and Graphics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks
An interactive sketching method for 3D object modeling
Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts
A method for reconstructing sketched polyhedral shapes with rounds and fillets
SG'10 Proceedings of the 10th international conference on Smart graphics
A hybrid intelligent system for 3D reconstruction from a single line drawing
International Journal of Computer Applications in Technology
Investigations of the compliance function in 3D reconstruction from 2D line drawings
International Journal of Computer Applications in Technology
3D reconstruction from drawings with straight and curved edges
SIGGRAPH Asia 2013 Technical Briefs
| Hi-index | 0.11 |
Different regularities have been used in the reconstruction of a 3D object from a single-view line drawing. These regularities are not all equally informative in the reconstruction process: certain regularities may correspond mainly to noise, not information; some may overlap with each other or are not too relevant to the reconstruction. This paper studies these regularities comprehensively, so as to select the most effective set that can give robust and reliable 3D reconstruction. The selection is made through a method called automatic relevance determination (ARD), which employs the Bayesian framework and support vector regression estimation. The proposed method is able to identify the worst regularities according to their ARD parameters and eliminate them. The effectiveness of this pruning is evaluated by model validation. The regularity set so obtained is effective for general 3D reconstruction. The experimental results show that the regularity set selected can reduce the reconstruction complexity and produce satisfactory reconstruction performance.