Daniel Puckowski
In this paper, I present a novel quasi-rotation invariant interest point descriptor, coined COIF (Concentric Oval Intensity Features). The descriptor is straightforward to implement and feature matching is time efficient. COIF may be used to detect rotated images and may be used for image stitching in panorama applications. COIF demonstrates the feasibility of using luminance histograms for feature matching.
| Description | SIFT | COIF |
|---|---|---|
| Instances Equal | 55 | 55 |
| SIFT Better | 11 | - |
| COIF Better | - | 8 |
| Accuracy (%) | 98.9589 | 98.5205 |
| More Accurate (%) | +0.4384 | - |
| Accuracy Range | Count |
|---|---|
| 100% | 60 |
| 99-95% | 6 |
| 94-90% | 4 |
| 89-85% | 0 |
| 84-80% | 3 |
| Accuracy Range | Count |
|---|---|
| 100% | 65 |
| 99-95% | 4 |
| 94-90% | 1 |
| 89-85% | 2 |
| 84-80% | 0 |
| 79-75% | 1 |
| Effect | Accuracy Range |
|---|---|
| Light Variation | +/- 10% |
| Perspective Transformation | 25% |
| Scale Change | +/- 50% |
| Average Matching Time | Median Matching Time | Image Pair Count | Pixels Processed Count |
|---|---|---|---|
| 7,589 milliseconds | 3,162 milliseconds | 55 | 30,612,480 |
Matching times include time to identify corners, time to generate descriptors, and time for feature matching.
| Bin Merge Count | Number of Times Used | Percent Occurrence |
|---|---|---|
| 1 | 38 | 69.09% |
| 2 | 3 | 5.45% |
| 3 | 4 | 7.27% |
| 4 | 5 | 9.09% |
| 5 | 5 | 9.09% |