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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 center-of-mass (COM); (d) the main orientation computed by the histogram of intensities (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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f4-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 center-of-mass (COM); (d) the main orientation computed by the histogram of intensities (HOI); (e) the proposed method that utilizes the main orientation computed by PIIFD. The test (IR) image is rotated by 20°.

Mentions: Figure 4 shows the keypoint matches on an image pair from dataset EOIR built with EOH, EOH equipped with SIFT main orientation, with COM (center-of-mass) main orientation [24], with HOI (histogram of intensity) main orientation [24] and the proposed method. The infrared image is rotated by 20°. EOH provides five keypoint matches in Figure 4a, and three are visually correct. SIFT main orientation gives seven keypoint matches in Figure 4b, and four matches are visually correct. The COM and HOI main orientations do not give many correct matches, as shown in Figure 4c,d, while the proposed method gives 11 keypoint matches in Figure 4e, and nine matches are visually correct. Visually, the SIFT main orientation and the proposed method give almost the same correct rate of matches, except that the proposed method gives more matches. The reason might be that although this pair of images was taken with a visible camera and an infrared camera, they are very close to single-spectrum images, i.e., brighter (darker) areas in the visible image are also brighter (darker) in the infrared image. However, the relationship between image intensities is not linear, which makes COM and HOI not perform very well.


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 center-of-mass (COM); (d) the main orientation computed by the histogram of intensities (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

f4-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 center-of-mass (COM); (d) the main orientation computed by the histogram of intensities (HOI); (e) the proposed method that utilizes the main orientation computed by PIIFD. The test (IR) image is rotated by 20°.
Mentions: Figure 4 shows the keypoint matches on an image pair from dataset EOIR built with EOH, EOH equipped with SIFT main orientation, with COM (center-of-mass) main orientation [24], with HOI (histogram of intensity) main orientation [24] and the proposed method. The infrared image is rotated by 20°. EOH provides five keypoint matches in Figure 4a, and three are visually correct. SIFT main orientation gives seven keypoint matches in Figure 4b, and four matches are visually correct. The COM and HOI main orientations do not give many correct matches, as shown in Figure 4c,d, while the proposed method gives 11 keypoint matches in Figure 4e, and nine matches are visually correct. Visually, the SIFT main orientation and the proposed method give almost the same correct rate of matches, except that the proposed method gives more matches. The reason might be that although this pair of images was taken with a visible camera and an infrared camera, they are very close to single-spectrum images, i.e., brighter (darker) areas in the visible image are also brighter (darker) in the infrared image. However, the relationship between image intensities is not linear, which makes COM and HOI not perform very well.

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