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Concentric Oval Intensity Features (COIF)

Daniel Puckowski

Abstract

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.

Example COIFv6 Result

General Comparison

Description SIFT COIF
Instances Equal 55 55
SIFT Better 11 -
COIF Better - 8
Accuracy (%) 98.9589 98.5205
More Accurate (%) +0.4384 -

Detailed Accuracy Distribution

COIFv6

Accuracy Range Count
100% 60
99-95% 6
94-90% 4
89-85% 0
84-80% 3

SIFT

Accuracy Range Count
100% 65
99-95% 4
94-90% 1
89-85% 2
84-80% 0
79-75% 1

Image Stitching Dataset Performance

Dataset COIFv6 Success COIFv6 Failure SIFT Success SIFT Failure COIFv6 vs. SIFT
SPW Dataset (2020) 88.373% 11.627% 95.455% 4.545% -7.082%
Dataset for Stitching with Multiple Registrations (2018) 65.286% 35.714% 50.000% 50.000% +15.286%
VPG Dataset (2020) 90.910% 9.090% 44.455% 54.545% +46.455%

Impact of Environmental Factors on Measurement Accuracy

Effect Accuracy Range
Light Variation +/- 10%
Perspective Transformation 25%
Scale Change +/- 20%
Guassian Blur +3 pixel radius

Performance Metrics and Distribution Statistics for Image Matching Operations

Average Matching Time Median Matching Time Image Pair Count Pixels Processed Count
4,283 milliseconds 1,985 milliseconds 45 25,543,680

Performance Metrics and Distribution Statistics for COIFv6 Upright (Minimal Image Rotation)

Average Matching Time Median Matching Time Image Pair Count Pixels Processed Count
3,778 milliseconds 1,577 milliseconds 45 25,543,680

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%

Detailed Analysis of Iteration Counts by Bin Merge

Bin Merge Count Iteration Count Percent Occurrence
1 1 66 51.96%
2 1 16 12.59%
3 1 11 8.66%
4 1 11 8.66%
4 2 1 0.78%
4 6 1 0.78%
5 1 7 5.51%
5 2 2 1.57%
5 4 1 0.78%
5 5 1 0.78%
5 7 2 1.57%
5 8 1 0.78%
5 9 7 5.51%

Given the test image pair set, 51.96% of all image pairs yielded passing feature matches with the default COIFv6 parameters. Given the test image pair set, 87.38% of all image pairs yielded passing feature matches within the first 5 iterations.