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Building keypoint mappings on multispectral images by a cascade of classifiers with a resurrection mechanism.

Li Y, Jing J, Jin H, Qiao W - Sensors (Basel) (2015)

Bottom Line: Initial keypoint mappings are built with the descriptors associated with keypoints; then, at each step, only a small number of keypoint mappings of a high confidence are classified to be incorrect.Due to the drawback of a classification rule, some correct keypoint mappings may be misclassified as incorrect at a step.Experimental results show that the proposed cascade structure combined with the resurrection mechanism can effectively build more reliable keypoint mappings on multispectral images than existing methods.

View Article: PubMed Central - PubMed

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

ABSTRACT
Inspired by the boosting technique for detecting objects, this paper proposes a cascade structure with a resurrection mechanism to establish keypoint mappings on multispectral images. The cascade structure is composed of four steps by utilizing best bin first (BBF), color and intensity distribution of segment (CIDS), global information and the RANSAC process to remove outlier keypoint matchings. Initial keypoint mappings are built with the descriptors associated with keypoints; then, at each step, only a small number of keypoint mappings of a high confidence are classified to be incorrect. The unclassified keypoint mappings will be passed on to subsequent steps for determining whether they are correct. Due to the drawback of a classification rule, some correct keypoint mappings may be misclassified as incorrect at a step. Observing this, we design a resurrection mechanism, so that they will be reconsidered and evaluated by the rules utilized in subsequent steps. Experimental results show that the proposed cascade structure combined with the resurrection mechanism can effectively build more reliable keypoint mappings on multispectral images than existing methods.

No MeSH data available.


Related in: MedlinePlus

Matching result on a remote sensing image pair taken during the 2008 Sichuan earthquake (on a scale of 1:10,000) from the dataset ‘EOIR’. (a) The proposed method; (b) SIFT + RANSAC. The clouds appearing in the IR image do not generate incorrect matches for the proposed method, since they have been removed step by step in the cascade structure. SIFT + RANSAC barely generates a keypoint mapping due to the lack of texture in the cloud area (hence, fewer keypoints). The repeating structure of image content causes mismatches for SIFT + RANSAC.
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f7-sensors-15-11769: Matching result on a remote sensing image pair taken during the 2008 Sichuan earthquake (on a scale of 1:10,000) from the dataset ‘EOIR’. (a) The proposed method; (b) SIFT + RANSAC. The clouds appearing in the IR image do not generate incorrect matches for the proposed method, since they have been removed step by step in the cascade structure. SIFT + RANSAC barely generates a keypoint mapping due to the lack of texture in the cloud area (hence, fewer keypoints). The repeating structure of image content causes mismatches for SIFT + RANSAC.

Mentions: Figure 7 gives another visual result of keypoint mappings on an image pair taken during the 2008 Sichuan earthquake from the dataset ‘EOIR’, with all five mappings built with SIFT + RANSAC incorrect in Figure 7b. In the proposed method, more than one mapping reference keypoint is assigned to a test keypoint in the first step, which helps preserve more keypoint mappings. Furthermore, clouds appear in the IR image, which cause occlusion and mismatches. The proposed method utilizes the descriptors, as well as complementary information, providing six correct keypoint matches in this image pair in Figure 7a.


Building keypoint mappings on multispectral images by a cascade of classifiers with a resurrection mechanism.

Li Y, Jing J, Jin H, Qiao W - Sensors (Basel) (2015)

Matching result on a remote sensing image pair taken during the 2008 Sichuan earthquake (on a scale of 1:10,000) from the dataset ‘EOIR’. (a) The proposed method; (b) SIFT + RANSAC. The clouds appearing in the IR image do not generate incorrect matches for the proposed method, since they have been removed step by step in the cascade structure. SIFT + RANSAC barely generates a keypoint mapping due to the lack of texture in the cloud area (hence, fewer keypoints). The repeating structure of image content causes mismatches for SIFT + RANSAC.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481929&req=5

f7-sensors-15-11769: Matching result on a remote sensing image pair taken during the 2008 Sichuan earthquake (on a scale of 1:10,000) from the dataset ‘EOIR’. (a) The proposed method; (b) SIFT + RANSAC. The clouds appearing in the IR image do not generate incorrect matches for the proposed method, since they have been removed step by step in the cascade structure. SIFT + RANSAC barely generates a keypoint mapping due to the lack of texture in the cloud area (hence, fewer keypoints). The repeating structure of image content causes mismatches for SIFT + RANSAC.
Mentions: Figure 7 gives another visual result of keypoint mappings on an image pair taken during the 2008 Sichuan earthquake from the dataset ‘EOIR’, with all five mappings built with SIFT + RANSAC incorrect in Figure 7b. In the proposed method, more than one mapping reference keypoint is assigned to a test keypoint in the first step, which helps preserve more keypoint mappings. Furthermore, clouds appear in the IR image, which cause occlusion and mismatches. The proposed method utilizes the descriptors, as well as complementary information, providing six correct keypoint matches in this image pair in Figure 7a.

Bottom Line: Initial keypoint mappings are built with the descriptors associated with keypoints; then, at each step, only a small number of keypoint mappings of a high confidence are classified to be incorrect.Due to the drawback of a classification rule, some correct keypoint mappings may be misclassified as incorrect at a step.Experimental results show that the proposed cascade structure combined with the resurrection mechanism can effectively build more reliable keypoint mappings on multispectral images than existing methods.

View Article: PubMed Central - PubMed

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

ABSTRACT
Inspired by the boosting technique for detecting objects, this paper proposes a cascade structure with a resurrection mechanism to establish keypoint mappings on multispectral images. The cascade structure is composed of four steps by utilizing best bin first (BBF), color and intensity distribution of segment (CIDS), global information and the RANSAC process to remove outlier keypoint matchings. Initial keypoint mappings are built with the descriptors associated with keypoints; then, at each step, only a small number of keypoint mappings of a high confidence are classified to be incorrect. The unclassified keypoint mappings will be passed on to subsequent steps for determining whether they are correct. Due to the drawback of a classification rule, some correct keypoint mappings may be misclassified as incorrect at a step. Observing this, we design a resurrection mechanism, so that they will be reconsidered and evaluated by the rules utilized in subsequent steps. Experimental results show that the proposed cascade structure combined with the resurrection mechanism can effectively build more reliable keypoint mappings on multispectral images than existing methods.

No MeSH data available.


Related in: MedlinePlus