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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

Comparison of keypoint matches for different methods. The horizontal axis represents the degree of rotation, and the vertical axis represents the percent of correct matches. Correct matches are defined to be of a distance falling in (0 10]. (a) The result on dataset EOIR; (b) the result on dataset VS-long-wave infrared (LWIR). On both EOIR and VS-LWIR, the performance of every method decreases when the rotation degree increases. The proposed method and the variant EOH with main orientation decrease significantly slower than the original EOH, SIFT main orientation, COM and HOI on both datasets, varying the effectiveness of the main orientation.
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f6-sensors-15-15595: Comparison of keypoint matches for different methods. The horizontal axis represents the degree of rotation, and the vertical axis represents the percent of correct matches. Correct matches are defined to be of a distance falling in (0 10]. (a) The result on dataset EOIR; (b) the result on dataset VS-long-wave infrared (LWIR). On both EOIR and VS-LWIR, the performance of every method decreases when the rotation degree increases. The proposed method and the variant EOH with main orientation decrease significantly slower than the original EOH, SIFT main orientation, COM and HOI on both datasets, varying the effectiveness of the main orientation.

Mentions: The performance decreases with the increase of rotation degree for all methods. For example, on dataset VS-LWIR, when the test image is rotated by 10°, the proposed method has 48.13% of matches falling in [0, 10], but this number decreases to 43.08%, 33.13% and 24.02% when the test image is rotated by 20°, 30° and 45°. For EOH, the percent of keypoint matches falling in [0, 10] decreases more than the proposed method, from 41.83% to 5.93%, 1.20% and 0.44%. The performance decrease for EOH is due to the lack of main orientation, while the decrease for the proposed method originates from the inaccuracy of computing main orientation. Figure 6 shows the performance of different methods under rotation. Keypoint matches of distance d ≤ 10 are defined to be correct. From Figure 6, it can be seen that the percent of correct matches for all methods decreases with the increase of rotation degree. On both EOIR and VS-LWIR, the proposed method and the variant implementation of EOH decrease slower than the original EOH without the main orientation, SIFT main orientation, COM main orientation and HOI main orientation.


Assigning Main Orientation to an EOH Descriptor on Multispectral Images.

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

Comparison of keypoint matches for different methods. The horizontal axis represents the degree of rotation, and the vertical axis represents the percent of correct matches. Correct matches are defined to be of a distance falling in (0 10]. (a) The result on dataset EOIR; (b) the result on dataset VS-long-wave infrared (LWIR). On both EOIR and VS-LWIR, the performance of every method decreases when the rotation degree increases. The proposed method and the variant EOH with main orientation decrease significantly slower than the original EOH, SIFT main orientation, COM and HOI on both datasets, varying the effectiveness of the main orientation.
© Copyright Policy
Related In: Results  -  Collection

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

f6-sensors-15-15595: Comparison of keypoint matches for different methods. The horizontal axis represents the degree of rotation, and the vertical axis represents the percent of correct matches. Correct matches are defined to be of a distance falling in (0 10]. (a) The result on dataset EOIR; (b) the result on dataset VS-long-wave infrared (LWIR). On both EOIR and VS-LWIR, the performance of every method decreases when the rotation degree increases. The proposed method and the variant EOH with main orientation decrease significantly slower than the original EOH, SIFT main orientation, COM and HOI on both datasets, varying the effectiveness of the main orientation.
Mentions: The performance decreases with the increase of rotation degree for all methods. For example, on dataset VS-LWIR, when the test image is rotated by 10°, the proposed method has 48.13% of matches falling in [0, 10], but this number decreases to 43.08%, 33.13% and 24.02% when the test image is rotated by 20°, 30° and 45°. For EOH, the percent of keypoint matches falling in [0, 10] decreases more than the proposed method, from 41.83% to 5.93%, 1.20% and 0.44%. The performance decrease for EOH is due to the lack of main orientation, while the decrease for the proposed method originates from the inaccuracy of computing main orientation. Figure 6 shows the performance of different methods under rotation. Keypoint matches of distance d ≤ 10 are defined to be correct. From Figure 6, it can be seen that the percent of correct matches for all methods decreases with the increase of rotation degree. On both EOIR and VS-LWIR, the proposed method and the variant implementation of EOH decrease slower than the original EOH without the main orientation, SIFT main orientation, COM main orientation and HOI main orientation.

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