Translator Disclaimer
1 April 2008 Novel semisupervised high-dimensional correspondences learning method
Author Affiliations +
Correspondence is one of the big challenges in machine learning and image processing. To match two high-dimensional data sets with a certain number of aligned training examples, a novel semisupervised method is proposed. It is mainly based on two manifold learning approaches: maximum variance unfolding (MVU) and locally linear embedding (LLE). We have modified MVU to a semi-supervised version to solve the correspondence problem. Additionally, the nonuniform warps and folds caused by employing LLE alone and the computational burden of MVU disappear when they are combined. The proposed algorithm outperforms traditional methods in accuracy and efficiency. Three examples are performed to demonstrate the potential of this method.
©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)
Chenping Hou, Yi Wu, Dongyun Yi, and Yuanyuan Jiao "Novel semisupervised high-dimensional correspondences learning method," Optical Engineering 47(4), 047201 (1 April 2008).
Published: 1 April 2008


Path Planner With Vision Capability
Proceedings of SPIE (March 01 1990)
Visual servo control system of space robot
Proceedings of SPIE (October 10 2000)
Task Panel Sensing with a Movable Camera
Proceedings of SPIE (March 01 1990)
A Vision System For Robotic Inspection And Manipulation
Proceedings of SPIE (March 29 1988)

Back to Top