COREG: a corner based registration technique for multimodal images

Lv, Guohua; Teng, Shyh Wei; Lu, Guojun

doi:10.1007/s11042-017-4907-3

Cited by 9 publications

(4 citation statements)

References 39 publications

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“…It is common that there exist substantial intensity variations between corresponding parts of multi-modal images [7,23,37]. As stated in Sections 1 and 2.2, a SIFT-like descriptor is represented by a 128-dimensional vector and the value at each dimension is a GM value accumulated at a specific bin of an orientation histogram.…”

Section: Normalizing Gm-based Descriptors (Strategies 1 and 2)mentioning

confidence: 99%

“…Local image features [1] are of vital importance in the field of image processing and have been widely studied in various applications such as object recognition [2], image retrieval [3] and image registration [4][5][6][7][8][9][10][11]. A local image feature [12,13] such as a keypoint or corner is encoded into a local descriptor by representing image information within a local region such as color, gradient and shape [14].…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors

Dong¹,

Han²,

Lv³

et al. 2019

Symmetry

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The utilization of gradient information is a key issue in building Scale Invariant Feature Transform (SIFT)-like descriptors. In the literature, two types of gradient information, i.e., Gradient Magnitude (GM) and Gradient Occurrence (GO), are used for building descriptors. However, both of these two types of gradient information have limitations in building and matching local image descriptors. In our prior work, a strategy of combining these two types of gradient information was proposed to intersect the keypoint matches which are obtained by using gradient magnitude and gradient occurrence individually. Different from this combination strategy, this paper explores novel strategies of weighting these two types of gradient information to build new descriptors with high discriminative power. These proposed weighting strategies are extensively evaluated against gradient magnitude and gradient occurrence as well as the combination strategy on a few image registration datasets. From the perspective of building new descriptors, experimental results will show that each of the proposed strategies achieve higher matching accuracy as compared to both GM-based and GO-based descriptors. In terms of recall results, one of the proposed strategies outperforms both GM-based and GO-based descriptors.

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Section: Normalizing Gm-based Descriptors (Strategies 1 and 2)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors

Dong¹,

Han²,

Lv³

et al. 2019

Symmetry

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“…This paper focuses on feature-based registration of multispectral satellite images. The central difficulty lies in the significant and nonlinear radiometric (intensity) differences between images [9], [10], [15]- [18]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Robust Registration of Multispectral Satellite Images Based on Structural and Geometrical Similarity

Chi

Awrangjeb

et al. 2022

IEEE Geosci. Remote Sensing Lett.

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Accurate registration of multispectral satellite images is a challenging task due to the significant and nonlinear radiometric differences between these data. To address this problem, this paper explores the strategy of geometrical similarity between triplets of feature points, and it is combined with the structural similarity between images in a feature-based image registration framework. The underlying principle is that the structural and geometrical similarities generally preserve across the images being registered. In this feature-based image registration framework, a set of control points are firstly detected. Then, the geometric similarity between triplets of control points is defined, followed by a ranking operation of these triplets of control points. The highly-ranked triplets are used to estimate a spatial transformation between images. Finally, initial matches obtained by a benchmark registration technique are refined by the estimated transformation. The experimental results demonstrate the great effectiveness of the proposed technique for registering multispectral satellite images.

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“…SIFT generally achieves great effectiveness in dealing with mono-modal images [14]- [18]. However, it is far more demanding to handle multi-modal images as the intensity variations between corresponding parts are usually complex and nonlinear [2], [4]- [6], [12], [26], [27], [29]. Symmetric SIFT (S-SIFT) [22], as a multi-modal variant of SIFT, was proposed by addressing the gradient reversal problem that commonly exists in multi-modal image registration.…”

Section: Introductionmentioning

confidence: 99%

Self-Similarity and Symmetry With SIFT for Multi-Modal Image Registration

2019

IEEE Access

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This paper presents a novel feature-based multi-modal image registration technique called selfsimilarity and symmetry with a scale invariant feature transform (3S-SIFT). The proposed technique has the following two main components. First, a ubiquitous problem existing in registering multi-modal images, gradient reversal, is well studied and addressed. Second, the proposed technique takes into account selfsimilarity information between keypoint triangles, which is conducive to enhancing the registration accuracy. Moreover, a simplified version of 3S-SIFT called 4S-SIFT is proposed as a pruning technique for feature matching. The proposed techniques are generally applicable to the registration of multi-modal images with changes in scale, rotation, and translation. The experiments have been conducted on a set of benchmark datasets in the domain of image registration, demonstrating that the proposed techniques achieve the stateof-the-art performance in both matching accuracy and recall. INDEX TERMS Multi-modal image registration, local descriptors, self-similarity, symmetric SIFT.

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COREG: a corner based registration technique for multimodal images

Cited by 9 publications

References 39 publications

Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors

Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors

Robust Registration of Multispectral Satellite Images Based on Structural and Geometrical Similarity

Self-Similarity and Symmetry With SIFT for Multi-Modal Image Registration

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