Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images
Abstract
:1. Introduction
2. Related Work
3. Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters for Infrared and Visible Images
3.1. Corner Detection Based on Distinct Wavelength Phase Congruency
3.2. Corners Detection Combining Distinct Wavelength Phase Congruency of Original Images and Gaussian Smoothing Images
3.3. Feature Descriptor Based on Log-Gabor Filters and the Corresponding Keypoint Detection Using the RANSAC Algorithm
3.4. Similarity Computation of the Points Sets from the Visible and Infrared Images by BiDimRegressional Regression Modeling
- Vectors A,B represent the coordinates of the visible image, which are extracted from the independent image possessing the relationships with the corresponding infrared image from the same scene. A and B are known as the first and the second dimension, respectively.
- Vectors X,Y represent the coordinates of the infrared image, which are extracted from the independent image possessing the relationships with the corresponding visible image from the same scene. X and Y are known as the first and the second dimension, respectively.
- is the squared regression coefficient.
- F represents F statistics for the overall regression model including appendant degrees of freedom(df1,df2).
- p value is the accordant significance level.
4. Experiments
4.1. Experiment Data
4.2. Evaluation Measures
- Repeatable rate (RPR): as shown in Figure 8, the red points show repeatable keypoints and the green points show non-matching keypoints. In two images (the infrared image in Figure 8a and the visible image in Figure 8b), the high percentage of repeatable keypoints in both images is called repeatability. Thus, the repeatable rate (RPR) is defined as follows:
- Recall rate (RR): this is shown as Figure 9, he red keypoints are the true matched corresponding keypoints and the yellow ones are non-matched keypoints. A better keypoint detection algorithm should detect more corresponding keypoints accurately over the repeatable keypoints set. Thus, the recall rate (RR) can be defined as:
- Accuracy rate (AR): as shown in Figure 10, the detected corresponding keypoints should be accurate; the red lines show accurately detected corresponding keypoints and the green lines represent those which were not accurate. More red lines denote better performance of the image matching. Thus, the accuracy rate (AR) is defined as:
- Quantity rate (QR): Figure 11a demonstrates that a larger number of keypoints will generate more corresponding keypoints favorable to image matching. Figure 11b, in contrast, shows that a smaller number of keypoints can cause failure in image matching. Thus, the number of detected keypoints should be sufficiently large; for example, a reasonable number of keypoints should be detected, even on small objects. Nevertheless, the optimal number of features depends on the application. Thus, we define the QR as:
- Efficiency (EF): The detection of features in an image should be considered as time-critical application. Thus, we defined the as:
4.3. Experiment Results Comparison and Discussion
- Edge-oriented histogram descriptor (EHD): this algorithm first detects the contour of the image and, then, the edge histogram descriptor is obtained by using the MPEG-7 standard [69].
- Phase congruency edge-oriented histogram descriptor (PCEHD): This algorithm uses phase congruency to detect corners and edges in the image. In the corners, the EHD algorithm is utilized to obtain the feature descriptors.
- Log-Gabor feature descriptor (LGHD): this algorithm uses a fast algorithm to detect corners and log-Gabor filters to generate feature descriptors.
- Phase congruency log-Gabor image matching (PCLGM): This algorithm uses phase congruency to detect corners and edges in the image. In these corners, log-Gabor histograms are utilized to obtain the feature descriptors in overlapping subregions.
- Modified phase congruency log-Gabor image matching(MPCLGM): This algorithm modified corners detection based on phase congruency by combining the original images and Gaussian smoothed images. In these corners, log-Gabor feature descriptors are utilized to obtain the feature descriptors.
- Distinct modified phase congruency log-Gabor image matching(DMPCLGM): Distinct wavelengths are employed in the DMPCLGM for the visible and infrared images.
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Descriptor | RPR | RR | AR | QR | EF |
---|---|---|---|---|---|
EHD | 0.02 | 0.04 | 0.14 | 0.005 | 0.28 |
PCEHD | 0.02 | 0.02 | 0.01 | 0.005 | 1.05 |
LGHD | 0.02 | 0.02 | 0.01 | 0.005 | 1.55 |
PCLGM | 0.19 | 0.014 | 0.59 | 0.27 | 40.3 |
MPCLGM | 0.198 | 0.013 | 0.62 | 0.31 | 65.54 |
DMPCLGM | 0.27 | 0.01 | 0.64 | 0.46 | 82 |
Descriptor | RPR | RR | AR | QR | EF |
---|---|---|---|---|---|
EHD | 0.025 | 0.054 | 0.1 | 0.0018 | 0.4 |
PCEHD | 0.034 | 0.037 | 0.123 | 0.0025 | 1.87 |
LGHD | 0.04 | 0.08 | 0.25 | 0.003 | 11.56 |
PCLGM | 0.24 | 0.003 | 0.21 | 0.43 | 67.68 |
MPCLGM | 0.24 | 0.013 | 0.26 | 0.43 | 71.62 |
DMPCLGM | 0.24 | 0.0075 | 0.32 | 0.43 | 78.01 |
Descriptor | RPR | RR | AR | QR | EF |
---|---|---|---|---|---|
EHD | 0.087 | 0.3 | 0.91 | 0.11 | 8.65 |
PCEHD | 0.087 | 0.3 | 0.9 | 0.11 | 11.41 |
LGHD | 0.087 | 0.4 | 0.93 | 0.11 | 30.3 |
PCLGM | 0.2976 | 0.0282 | 0.8991 | 0.2279 | 30.51 |
MPCLGM | 0.2976 | 0.0278 | 0.8919 | 0.22341 | 30.40 |
DMPCLGM | - | - | - | - | - |
Descriptor | RPR | RR | AR | QR | EF |
---|---|---|---|---|---|
EHD | 0.22 | 0.27 | 0.99 | 0.06 | 4.48 |
PCEHD | 0.22 | 0.27 | 0.99 | 0.06 | 5.73 |
LGHD | 0.22 | 0.28 | 1 | 0.06 | 15.16 |
PCLGM | 0.22639 | 0.0528 | 0.7234 | 0.3134 | 69.05 |
MPCLGM | 0.22639 | 0.0604 | 0.7261 | 0.4170 | 96.1237 |
DMPCLGM | - | - | - | - | - |
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Liu, X.; Li, J.-B.; Pan, J.-S. Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images. Sensors 2019, 19, 4244. https://doi.org/10.3390/s19194244
Liu X, Li J-B, Pan J-S. Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images. Sensors. 2019; 19(19):4244. https://doi.org/10.3390/s19194244
Chicago/Turabian StyleLiu, Xiaomin, Jun-Bao Li, and Jeng-Shyang Pan. 2019. "Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images" Sensors 19, no. 19: 4244. https://doi.org/10.3390/s19194244