Name
Abstract | ||
|---|---|---|
In this paper we introduce a family of filter kernels - the Gray-Code Kernels (GCK) and demonstrate their use in image analysis. Filtering an image with a sequence of Gray-Code Kernels is highly efficient and requires only 2 operations per pixel for each filter kernel, independent of the size or dimension of the kernel. We show that the family of kernels is large and includes the Walsh-Hadamard kernels amongst others. The GCK can also be used to approximate arbitrary kernels since a sequence of GCK can form a complete representation. The efficiency of computation using a sequence of GCK filters can be exploited for various real-time applications, such as, pattern detection, feature extraction, texture analysis, and more. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ICPR.2004.394 | ICPR (1) |
Keywords | Field | DocType |
gray-code kernels,approximate arbitrary kernel,feature extraction,complete representation,walsh-hadamard kernel,gck filter,filter kernel,pattern detection,image analysis,texture analysis,gray codes,gray code | Kernel (linear algebra),Computer vision,Pattern recognition,Computer science,Filter (signal processing),Gray code,Feature extraction,Pixel,Artificial intelligence,Kernel adaptive filter,Pattern detection,Computation | Conference |
ISSN | ISBN | Citations |
1051-4651 | 0-7695-2128-2 | 3 |
PageRank | References | Authors |
0.58 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gil Ben-Artzi | 1 | 45 | 3.55 |
| Hagit Zabrodsky Hel-Or | 2 | 371 | 28.19 |
| Yacov Hel-Or | 3 | 461 | 40.74 |