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IEEE Transactions on Geoscience and Remote Sensing, 2020
In this article, a new method is proposed for feature matching of remote sensing images using sample consensus based on sparse coding (SCSC) to improve the image registration technique.
Pouriya Etezadifar, H. Farsi
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In this article, a new method is proposed for feature matching of remote sensing images using sample consensus based on sparse coding (SCSC) to improve the image registration technique.
Pouriya Etezadifar, H. Farsi
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ACM Journal of Experimental Algorithmics, 2012
Directed graphs are commonly drawn by the Sugiyama algorithm where first vertices are placed on distinct hierarchical levels, and second vertices on the same level are permuted to reduce the overall number of crossings. Separating these two phases simplifies the algorithms but diminishes the quality of the result.
Christian Bachmaier +2 more
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Directed graphs are commonly drawn by the Sugiyama algorithm where first vertices are placed on distinct hierarchical levels, and second vertices on the same level are permuted to reduce the overall number of crossings. Separating these two phases simplifies the algorithms but diminishes the quality of the result.
Christian Bachmaier +2 more
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Proceedings of the ACM International Conference on Image and Video Retrieval, 2010
The best known Scale-Invariant Feature Transform (SIFT) shows its superior performance in a variety of image processing tasks due to its distinctiveness, invariance to scale, rotation and local geometric distortion. Despite its remarkable performance, SIFT is not invariant to mirror images and grayscale-inverted images.This paper proposes an improved ...
Rui Ma, Jian Chen, Zhong Su
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The best known Scale-Invariant Feature Transform (SIFT) shows its superior performance in a variety of image processing tasks due to its distinctiveness, invariance to scale, rotation and local geometric distortion. Despite its remarkable performance, SIFT is not invariant to mirror images and grayscale-inverted images.This paper proposes an improved ...
Rui Ma, Jian Chen, Zhong Su
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Proceedings of the 4th International Conference on Internet Multimedia Computing and Service, 2012
Recently, great advance has been made in large-scale content-based image search. Most state-of-the-art approaches are based on the Bag-of-Visual-Words model with local features, such as SIFT. Visual matching between images is obtained by vector quantization of local features.
Wengang Zhou +4 more
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Recently, great advance has been made in large-scale content-based image search. Most state-of-the-art approaches are based on the Bag-of-Visual-Words model with local features, such as SIFT. Visual matching between images is obtained by vector quantization of local features.
Wengang Zhou +4 more
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VF-SIFT: Very Fast SIFT Feature Matching
2010Feature-based image matching is one of the most fundamental issues in computer vision tasks. As the number of features increases, the matching process rapidly becomes a bottleneck. This paper presents a novel method to speed up SIFT feature matching. The main idea is to extend SIFT feature by a few pairwise independent angles, which are invariant to ...
Faraj Alhwarin +2 more
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Remote Sensing Image Registration Based on Modified SIFT and Feature Slope Grouping
IEEE Geoscience and Remote Sensing Letters, 2019In feature-based remote sensing image registration, the scale-invariant feature transform (SIFT) algorithm has been one of the most popular solutions. However, it is still a challenge to possess an appropriate amount of correct matches while eliminating ...
Herng-Hua Chang, Guan-Long Wu, M. Chiang
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019
Synthetic aperture radar (SAR) image registration is a challenging task in remote sensing because of the presence of significant intensity as well as geometric differences between the images. Moreover, the influence of multiplicative speckle noise in SAR
S. Paul, U. C. Pati
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Synthetic aperture radar (SAR) image registration is a challenging task in remote sensing because of the presence of significant intensity as well as geometric differences between the images. Moreover, the influence of multiplicative speckle noise in SAR
S. Paul, U. C. Pati
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A spatial-spectral SIFT for hyperspectral image matching and classification
Pattern Recognition Letters, 2019The scale-invariant feature transform (SIFT) is known as one of the most robust local invariant feature and is widely applied to image matching and classification. However, There is few studies on SIFT for hyperspectral image (HSI).
Yanshan Li, Qingteng Li, Yan Liu, W. Xie
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2D object recognition: a comparative analysis of SIFT, SURF and ORB feature descriptors
Multimedia tools and applications, 2021Monika Bansal +2 more
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Proceedings of the 18th ACM international conference on Multimedia, 2010
Invariant feature descriptors such as SIFT and GLOH have been demonstrated to be very robust for image matching and object recognition. However, such descriptors are typically of high dimensionality, e.g. 128-dimension in the case of SIFT. This limits the performance of feature matching techniques in terms of speed and scalability.
Gangqiang Zhao +3 more
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Invariant feature descriptors such as SIFT and GLOH have been demonstrated to be very robust for image matching and object recognition. However, such descriptors are typically of high dimensionality, e.g. 128-dimension in the case of SIFT. This limits the performance of feature matching techniques in terms of speed and scalability.
Gangqiang Zhao +3 more
openaire +1 more source