* V. Garcia and E. Debreuve and F. Nielsen and M. Barlaud.
k-nearest neighbor search: fast GPU-based implementations and application to high-dimensional feature matching.
In Proceedings of the IEEE International Conference on Image Processing (ICIP), Hong Kong, China, September 2010
* V. Garcia and E. Debreuve and M. Barlaud.
Fast k nearest neighbor search using GPU.
In Proceedings of the CVPR Workshop on Computer Vision on GPU, Anchorage, Alaska, USA, June 2008.
* Vincent Garcia
Ph.D. Thesis: Suivi d'objets d'intérêt dans une séquence d'images : des points saillants aux mesures statistiques
Université de Nice - Sophia Antipolis, Sophia Antipolis, France, December 2008
REQUIREMENTS
- The computer must have a CUDA-enabled graphic card (c.f. NVIDIA website)
- CUDA has to be installed (CUDA drivers and CUDA toolkit)
- A C compiler has to be installed
- For using the CUDA code in Matlab, please refer to the CUDA Matlab plug-in webpage for requirements.
COMPILATION & EXECUTION
The provided code can be used for C and Matlab applications.
We provide bellow the C and Matlab procedures to compile and execute our code.
The user must have a basic knowledge of compiling and executing standard examples before trying to compile and execute our code.
We will consider here that we want to compile the file "knn_cuda_with_indexes.cu".
For C
1. Set the global variable MATLAB_CODE to 0 in file knn_cuda_with_indexes.cu
2. Compile the CUDA file with the command line:
nvcc -o knn_cuda_with_indexes.exe knn_cuda_with_indexes.cu -lcuda -D_CRT_SECURE_NO_DEPRECATE
3. Execute the knn_cuda_with_indexes.exe with the command line:
./knn_cuda_with_indexes.exe
For MATLAB
1. Set the global variable MATLAB_CODE to 1 in file knn_cuda_with_indexes.cu
2. Compile the CUDA file with the Matlab command line:
nvmex -f nvmexopts.bat knn_cuda_with_indexes.cu -I'C:\CUDA\include' -L'C:\CUDA\lib' -lcufft -lcudart -lcuda -D_CRT_SECURE_NO_DEPRECATE
3. Execute the run_matlab.m script
ORGANISATION OF DATA
In CUDA, it is usual to use the notion of array.
For our kNN search program, the following array
A = | 1 3 5 |
| 2 4 6 |
corresponds to the a set of 3 points of dimension 2:
p1 = (1, 2)
p2 = (3, 4)
p3 = (5, 6)
The array A is actually stored in memory as a linear vector:
A = (1, 3, 5, 2, 4, 6)
The organisation of data is different in Matlab and in CUDA. For Matlab, the previous linear vector
corresponds to an array of 3 lines and 2 columns:
| 1 2 |
A = | 3 4 |
| 5 6 |