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Assigning Main Orientation to an EOH Descriptor on Multispectral Images.

Li Y, Shi X, Wei L, Zou J, Chen F - Sensors (Basel) (2015)

Bottom Line: EOH has a better matching ability than SIFT (scale-invariant feature transform) on multispectral images, but does not assign a main orientation to keypoints.Then, EOH is computed for every keypoint with respect to its main orientation.In addition, an implementation variant is proposed for fast computation of the EOH descriptor.

View Article: PubMed Central - PubMed

Affiliation: Beijing University of Posts and Teles., School of Electronic Engineering, Rd. Xitucheng 10#, Beijing 100876, China. yli@bupt.edu.cn.

ABSTRACT
This paper proposes an approach to compute an EOH (edge-oriented histogram) descriptor with main orientation. EOH has a better matching ability than SIFT (scale-invariant feature transform) on multispectral images, but does not assign a main orientation to keypoints. Alternatively, it tends to assign the same main orientation to every keypoint, e.g., zero degrees. This limits EOH to matching keypoints between images of translation misalignment only. Observing this limitation, we propose assigning to keypoints the main orientation that is computed with PIIFD (partial intensity invariant feature descriptor). In the proposed method, SIFT keypoints are detected from images as the extrema of difference of Gaussians, and every keypoint is assigned to the main orientation computed with PIIFD. Then, EOH is computed for every keypoint with respect to its main orientation. In addition, an implementation variant is proposed for fast computation of the EOH descriptor. Experimental results show that the proposed approach performs more robustly than the original EOH on image pairs that have a rotation misalignment.

No MeSH data available.


Related in: MedlinePlus

The matching performance under rotation. The test image is rotated by 10°, 20°, and 30° from top to bottom line. The left column is the result of EOH without main orientation. The right column is the result of EOH equipped with the partial intensity invariant feature descriptor (PIIFD) main orientation.
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f3-sensors-15-15595: The matching performance under rotation. The test image is rotated by 10°, 20°, and 30° from top to bottom line. The left column is the result of EOH without main orientation. The right column is the result of EOH equipped with the partial intensity invariant feature descriptor (PIIFD) main orientation.

Mentions: This section gives visual matching results. Figure 3 gives the keypoint matchings built with the original EOH without the main orientation and the proposed method. The visible image serves as the reference image, and the infrared image is used as the test image. The test image is rotated by 10°, 20° and 30°. Figure 3a,c,e show the matching result of EOH between the reference and the rotated test image by 10°, 20° and 30°, respectively. Due to the lack of main orientation, the keypoint matches built with the EOH contain very few or no correct matches. As a comparison, the proposed method provides sufficiently many correct matches in Figure 3b,d,f.


Assigning Main Orientation to an EOH Descriptor on Multispectral Images.

Li Y, Shi X, Wei L, Zou J, Chen F - Sensors (Basel) (2015)

The matching performance under rotation. The test image is rotated by 10°, 20°, and 30° from top to bottom line. The left column is the result of EOH without main orientation. The right column is the result of EOH equipped with the partial intensity invariant feature descriptor (PIIFD) main orientation.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541846&req=5

f3-sensors-15-15595: The matching performance under rotation. The test image is rotated by 10°, 20°, and 30° from top to bottom line. The left column is the result of EOH without main orientation. The right column is the result of EOH equipped with the partial intensity invariant feature descriptor (PIIFD) main orientation.
Mentions: This section gives visual matching results. Figure 3 gives the keypoint matchings built with the original EOH without the main orientation and the proposed method. The visible image serves as the reference image, and the infrared image is used as the test image. The test image is rotated by 10°, 20° and 30°. Figure 3a,c,e show the matching result of EOH between the reference and the rotated test image by 10°, 20° and 30°, respectively. Due to the lack of main orientation, the keypoint matches built with the EOH contain very few or no correct matches. As a comparison, the proposed method provides sufficiently many correct matches in Figure 3b,d,f.

Bottom Line: EOH has a better matching ability than SIFT (scale-invariant feature transform) on multispectral images, but does not assign a main orientation to keypoints.Then, EOH is computed for every keypoint with respect to its main orientation.In addition, an implementation variant is proposed for fast computation of the EOH descriptor.

View Article: PubMed Central - PubMed

Affiliation: Beijing University of Posts and Teles., School of Electronic Engineering, Rd. Xitucheng 10#, Beijing 100876, China. yli@bupt.edu.cn.

ABSTRACT
This paper proposes an approach to compute an EOH (edge-oriented histogram) descriptor with main orientation. EOH has a better matching ability than SIFT (scale-invariant feature transform) on multispectral images, but does not assign a main orientation to keypoints. Alternatively, it tends to assign the same main orientation to every keypoint, e.g., zero degrees. This limits EOH to matching keypoints between images of translation misalignment only. Observing this limitation, we propose assigning to keypoints the main orientation that is computed with PIIFD (partial intensity invariant feature descriptor). In the proposed method, SIFT keypoints are detected from images as the extrema of difference of Gaussians, and every keypoint is assigned to the main orientation computed with PIIFD. Then, EOH is computed for every keypoint with respect to its main orientation. In addition, an implementation variant is proposed for fast computation of the EOH descriptor. Experimental results show that the proposed approach performs more robustly than the original EOH on image pairs that have a rotation misalignment.

No MeSH data available.


Related in: MedlinePlus