Hierarchical line matching based on Line–Junction–Line structure descriptor and local homography estimation

Li, Kai; Yao, Jian; Lu, Xin; Li, Li; Zhang, Zhichao

doi:10.1016/j.neucom.2015.07.137

Cited by 75 publications

(53 citation statements)

References 35 publications

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“…For known fundamental matrix, the local homography between a pair of junctions can be estimated to produce more accurate results and refining epipolar geometry meanwhile [36]. These results are very related to recent approach for the hierarchical line segment matching approach LJL [15]. In this work, detected line segments are used to generate junctions with virtual intersections in the first stage.…”

Section: Junction Matchingmentioning

confidence: 95%

“…There has been many approaches such as Canny edge detector [37] and line segment detector (LSD) [16,17] to extract line segments. LSD, which can produce more complete line segments than canny edge without any parameter tuning procedure, has been applied in many tasks such as line-segments matching [15] and 3D reconstruction [38]. Compared with key-points, line-segments can produce more complete result that contain the primary sketch for the scene.…”

Section: Indoor Image Matching With Geometric Structurementioning

confidence: 99%

“…LPI has ability to handle the relation between linesegments and matched key-points with viewpoint changes. LJL [15] method matches image pairs in multiple stages. In the first, detected line-segments are intersected with appropriate threshold to produce junctions and matching these intersections in the same way with key-points matching.…”

Section: Indoor Image Matching With Geometric Structurementioning

confidence: 99%

“…The line segment matching problem has been studied in recent years since it can represent more structural information than key-points. Many algorithms match line segments by using either photometric descriptors with individual line segments [19] or the initial geometric relation [14,15] to assist line segment matching. The approaches using pre-estimated epipolar geometry usually perform better than those of using photometric descriptors [19], but the epipolar geometry estimation still needs key-point correspondences in many Figure 1: A pair of indoor images.…”

Section: Introductionmentioning

confidence: 99%

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Anisotropic-Scale Junction Detection and Matching for Indoor Images

Xue

Xia

Bai

et al. 2018

IEEE Trans. on Image Process.

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Junctions play an important role in characterizing local geometrical structures of images, and the detection of which is a longstanding but challenging task. Existing junction detectors usually focus on identifying the location and orientations of junction branches while ignoring their scales which however contain rich geometries of images. This paper presents a novel approach for junction detection and characterization, which especially exploits the locally anisotropic geometries of a junction and estimates its scales by relying on an a-contrario model. The output junctions are with anisotropic scales, saying that a scale parameter is associated with each branch of a junction, and are thus named as anisotropicscale junctions (ASJs). We then apply the new detected ASJs for matching indoor images, where there are dramatic changes of viewpoints and the detected local visual features, e.g. key-points, are usually insufficient and lack distinctive ability. We propose to use the anisotropic geometries of our junctions to improve the matching precision of indoor images. The matching results on sets of indoor images demonstrate that our approach achieves the state-of-the-art performance on indoor image matching.

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Section: Junction Matchingmentioning

confidence: 95%

Section: Indoor Image Matching With Geometric Structurementioning

confidence: 99%

Section: Indoor Image Matching With Geometric Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Anisotropic-Scale Junction Detection and Matching for Indoor Images

Xue

Xia

Bai

et al. 2018

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

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“…In contrast, Meltzer and Soatto worked on point-like local affine invariant matching [20], which has a good matching effect for large viewing angles, but which is not good at dealing with non-coplanar and complex 3D-structured scenes. Moreover, the two adjacent line segment methods proposed by Li et al construct ray-point-ray [21] and line-juncture-line [22] structure descriptors. In this method, structured line segments in the first image are used to match with other structured line segments in the second image, so the robustness is relatively strong, and good matching results are achieved.…”

Section: Introductionmentioning

confidence: 99%

Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images

et al. 2018

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Line matching is the foundation of three-dimensional (3D) outline reconstruction for city buildings in aerial photogrammetry. Many existing studies have good line matching effects when dealing with aerial images with short baselines and small viewing angles. However, when faced with wide-baseline and large viewing-angle images, the matching effect drops sharply or even fails altogether. This paper deals with an efficient and simple method to achieve better line matching performance by a pair of wide-baseline aerial images, which make use of viewpoint-in variance to conduct line matching in rectified image spaces. Firstly, the perspective transformation relationship between the image plane and the geoid plane can be established from a Positioning and Orientation System (POS). Then, according to perspective projection matrices, two original images are separately rectified to conformal images, whose perspective deformation of large viewing-angle can be eliminated. Finally, the rectified images are used to conduct line matching, and the matched line segments obtained are back-projected to the original images. Four pairs of urban oblique aerial images are used to demonstrate the validity and efficiency of this method. Compared with line matching on original images, the number and the correctness of the matched line segments are greatly improved. Moreover, there is no loss of time efficiency. The proposed method can also be applied to general UAV (Unmanned Aerial Vehicle) aerial photogrammetry and introduced into matching for other geometric features, such as points, circles, curves, etc.

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