A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Dense Matching of Multiple Wide-baseline Views
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combined Depth and Outlier Estimation in Multi-View Stereo
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
| Hi-index | 0.00 |
This paper presents a novel local image descriptor for dense wide-baseline matching purposes, coined SULD (Speeded-Up Local Descriptor). SULD approximates or even outperforms than previously proposed schemes such as SURF and DAISY, and can be computed and compared much faster. This is achieved by summing up the Haar wavelet responses rather than the gradient, by computing convolutions recursively and by using low dimensions descriptor. The proposed approach was tested with ground truth laser scanned depth maps as well as on image pairs of different resolutions and the results show that good reconstruction is achieved even with only two small images.