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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 matched keypoints built with descriptors. (a) The original EOH without main orientation; (b) the main orientation computed by SIFT, ranging from [0, 2π]; (c) the main orientation computed by COM; (d) the main orientation computed by HOI; (e) the proposed method that utilizes the main orientation computed by PIIFD. The test (IR) image is rotated by 20°.
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f5-sensors-15-15595: The matched keypoints built with descriptors. (a) The original EOH without main orientation; (b) the main orientation computed by SIFT, ranging from [0, 2π]; (c) the main orientation computed by COM; (d) the main orientation computed by HOI; (e) the proposed method that utilizes the main orientation computed by PIIFD. The test (IR) image is rotated by 20°.

Mentions: Figure 5 illustrates the keypoint matches on an image pair from dataset VS-LWIR built with EOH, EOH equipped with SIFT main orientation, COM main orientation, HOI main orientation and the proposed method. The performance of EOH and EOH equipped with SIFT main orientation in Figure 5a,b is inferior to that in Figure 4a,b. The performance of COM and HOI is not good either, as shown in Figure 5c,d. This image pair is taken with a visible camera and an LWIR camera. The multimodality between them causes the inaccuracy of SIFT main orientation, COM and HOI and, hence, the mismatches in Figure 5b–d. The proposed method, for the induction of main orientation to keypoints, performs still well on this image pair.


Assigning Main Orientation to an EOH Descriptor on Multispectral Images.

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

The matched keypoints built with descriptors. (a) The original EOH without main orientation; (b) the main orientation computed by SIFT, ranging from [0, 2π]; (c) the main orientation computed by COM; (d) the main orientation computed by HOI; (e) the proposed method that utilizes the main orientation computed by PIIFD. The test (IR) image is rotated by 20°.
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-15-15595: The matched keypoints built with descriptors. (a) The original EOH without main orientation; (b) the main orientation computed by SIFT, ranging from [0, 2π]; (c) the main orientation computed by COM; (d) the main orientation computed by HOI; (e) the proposed method that utilizes the main orientation computed by PIIFD. The test (IR) image is rotated by 20°.
Mentions: Figure 5 illustrates the keypoint matches on an image pair from dataset VS-LWIR built with EOH, EOH equipped with SIFT main orientation, COM main orientation, HOI main orientation and the proposed method. The performance of EOH and EOH equipped with SIFT main orientation in Figure 5a,b is inferior to that in Figure 4a,b. The performance of COM and HOI is not good either, as shown in Figure 5c,d. This image pair is taken with a visible camera and an LWIR camera. The multimodality between them causes the inaccuracy of SIFT main orientation, COM and HOI and, hence, the mismatches in Figure 5b–d. The proposed method, for the induction of main orientation to keypoints, performs still well on this image pair.

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