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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

When the main orientation is assigned to a keypoint, the filters shown in Figure 1 ought to be rotated with respect to the main orientation. Black dots represent the integer pixel grid, and red dots are the fractional pixel locations, whosewhole values are used by the rotated filters.
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f2-sensors-15-15595: When the main orientation is assigned to a keypoint, the filters shown in Figure 1 ought to be rotated with respect to the main orientation. Black dots represent the integer pixel grid, and red dots are the fractional pixel locations, whosewhole values are used by the rotated filters.

Mentions: When main orientation is assigned to a keypoint, we need to compute the maximum response of the five filters. The five filters are rotated by the amount of main orientation, and the rotated pixels for computing the filter response lie in a fractional grid, as shown in Figure 2. To obtain pixel values at the fractional grid, a bilinear interpolation is employed.


Assigning Main Orientation to an EOH Descriptor on Multispectral Images.

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

When the main orientation is assigned to a keypoint, the filters shown in Figure 1 ought to be rotated with respect to the main orientation. Black dots represent the integer pixel grid, and red dots are the fractional pixel locations, whosewhole values are used by the rotated filters.
© Copyright Policy
Related In: Results  -  Collection

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

f2-sensors-15-15595: When the main orientation is assigned to a keypoint, the filters shown in Figure 1 ought to be rotated with respect to the main orientation. Black dots represent the integer pixel grid, and red dots are the fractional pixel locations, whosewhole values are used by the rotated filters.
Mentions: When main orientation is assigned to a keypoint, we need to compute the maximum response of the five filters. The five filters are rotated by the amount of main orientation, and the rotated pixels for computing the filter response lie in a fractional grid, as shown in Figure 2. To obtain pixel values at the fractional grid, a bilinear interpolation is employed.

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