An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Fast texture synthesis using tree-structured vector quantization
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Solid texture synthesis from 2D exemplars
ACM SIGGRAPH 2007 papers
ACM SIGGRAPH 2008 papers
What Is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
PatchMatch: a randomized correspondence algorithm for structural image editing
ACM SIGGRAPH 2009 papers
The generalized patchmatch correspondence algorithm
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Fast Exact Nearest Patch Matching for Patch-Based Image Editing and Processing
IEEE Transactions on Visualization and Computer Graphics
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Special Section on CAD/Graphics 2013: Image compositing using dominant patch transformations
Computers and Graphics
Hi-index | 0.00 |
Matching patches of a source image with patches of itself or a target image is a first step for many operations. Finding the optimum nearest-neighbors of each patch using a global search of the image is expensive. Optimality is often sacrificed for speed as a result. We present the Mixed-Resolution Patch-Matching (MRPM) algorithm that uses a pyramid representation to perform fast global search. We compare mixed-resolution patches at coarser pyramid levels to alleviate the effects of smoothing. We store more matches at coarser resolutions to ensure wider search ranges and better accuracy at finer levels. Our method achieves near optimality in terms of average error compared to exhaustive search. Our approach is simple compared to complex trees or hash tables used by others. This enables fast parallel implementations on the GPU, yielding upto 70× speedup compared to other iterative approaches. Our approach is best suited when multiple, global matches are needed.