Novel Coplanar Line-Points Invariants for Robust Line Matching Across Views
Robust line matching across wide-baseline views is a challenging task in computer vision. Most of the existing methods highly depend on the positional relationships between lines and the associated textures. These cues are sensitive to various image trans
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Abstract. Robust line matching across wide-baseline views is a challenging task in computer vision. Most of the existing methods highly depend on the positional relationships between lines and the associated textures. These cues are sensitive to various image transformations especially perspective deformations, and likely to fail in the scenarios where few texture present. In this paper, we construct a new coplanar linepoints invariant upon a newly developed projective invariant, named characteristic number, and propose a line matching algorithm using the invariant. The construction of this invariant uses intersections of coplanar lines instead of endpoints, rendering more robust matching across views. Additionally, a series of line-points invariant values generate the similarity metric for matching that is less affected by mismatched interest points than traditional approaches. Accurate homography recovered from the invariant allows all lines, even those without interest points around them, a chance to be matched. Extensive comparisons with the state-of-the-art validate the matching accuracy and robustness of the proposed method to projective transformations. The method also performs well for image pairs with few textures and similar textures. Keywords: Line matching number
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Projective invariant
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Characteristic
Introduction
Feature matching is such a fundamental task in computer vision that it has found wide applications in photogrammetry, image mosaicking, and object tracking etc. [2,7]. Points and lines are prone to be mismatched due to illumination and viewpoint changes. In the last two decades, point matching methods have been well studied [11,14], while lines are not so popular as points due to the higher geometric complexity. Lines usually incorporate more semantic and structural information than points, and thus it is quite important to match lines in the scenarios where lines are abundant. The scenarios include 3D modeling and robot navigation in manmade scenes [6,13]. Most of existing line matching methods use texture information near lines as descriptors. Wang et al. [17] proposed a SIFT-like descriptor, the mean-standard c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part VIII, LNCS 9912, pp. 599–611, 2016. DOI: 10.1007/978-3-319-46484-8 36
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deviation line descriptor (MSLD). In many images, the textures in the vicinity of line segments are not rich enough to assemble an effective descriptor. These textures are also quite similar, possibly generating a less distinctive descriptor. Moreover, MSLD is sensitive to scale changes that is quite common in feature matching. Zhang et al. [18] utilize both local appearance and geometric attributes of lines in order to construct the line band descriptor (LBD). This method requires a global rotation angle between images, which is not always accurate. Texture based methods are typically sensitive to various image transformations, and may fail on images of low texture images and similar textures. Some met
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