Abstract
Image-based line segment extraction plays an important role in a wide range of applications. Traditional line segment extraction algorithms focus on the accuracy and efficiency, without considering the integrity. Serious line segmentation fracture problems caused by image quality will result in poor subsequent applications. To solve this problem, a multi-constrained line segment extraction method, based on multi-scale image space, is presented. Firstly, using Gaussian down-sampling with a classical line segment detection method, a multi-scale image space is constructed to extract line segments in each image scale and all line segments are projected onto the original image. Then, a new line segment optimization and purification strategy is proposed with the horizontal and vertical distances and angle geometric constraint relationships between line segments to merge fracture line segments and delete redundant line segments. Finally, line segments with adjacent positions are optimized using the grayscale constraint relationship, based on normalized cross-correlation similarity criterion for realizing the second optimization of fracture line segments. Compared with mainstream line segment detector and edge drawing lines methods, experimental results (i.e., indoor, outdoor, and aerial images) indicate the validity and superiority of our proposed methods which can extract longer and more complete line segments.
Highlights
The line feature is an important part of an image’s geometric information and plays a crucial role in photogrammetry and remote sensing [1], three-dimensional (3D) urban modeling [2,3], computer vision and robot navigation positioning [4,5], and so on
The line feature has the following advantages as a more advanced feature than the point feature: (1) It has rich structural information for expressing edge information of 3D objects, such as structured buildings; (2) the extraction of the line feature is less affected by image noise, occlusion, geometric distortion, and grayscale distortion; (3) it has higher matching accuracy; (4) it is not necessary to completely determine the position of its two endpoints; and (5) in case of missing texture or uniformity, the line feature is easier to extract than the point feature
To alleviate3 othf 2is5 common problem, this paper proposes a multi-constrained line segment extraction optimization acolgmomritohnmpbraosbeldemo,ntmhisulptia-spcearlepirmopaogseespaacme,unltaim-coenlystMraSinLeindelsi,niencsleugdminegntMeSxLtrSaDctiaonndoMptSimEDizLaitnioens,ewsphaicche,rendaumceelsythMeSiLniflnueesn, cinecolfugdrinaygsMcaSleLdSiDstoarntdionMoSnEDfeLatinueres e(MxtSraicstiaonnaubsbirnegvtihateioidneoafoMf fuultziz-Sycpalreo)c,ewsshinicgh. reduces the influence of grayscale distortion on feature extraFcitgiounreu1sisnhgotwhse tihdeeaovoefrfaullzzmyepthrodceoslosignyg.presented in this paper, which is divided into three main moduFliegsu.rTeh1esfihroswt ms othdeuloevceornalslismtseothf ocodnoslotrguyctpinregstehneteldineinsethgims epnatpeexrt,rwachtiiocnh misoddievli.dAedminutloti-tshcraele immaaingempoydruamlesid
Summary
The line feature is an important part of an image’s geometric information and plays a crucial role in photogrammetry and remote sensing [1], three-dimensional (3D) urban modeling [2,3], computer vision and robot navigation positioning [4,5], and so on. A line feature is the kind of rich structural information which can intuitively express edge contours of the structured scene. This has achieved good practical application results in scientific and engineering decision-making applications. Elqursh et al [13] proposed a bundle-adjustment method combined with line segments to achieve relative orientation of stereo-images and the estimated position and pose information of the camera. It is valuable to extract good line features in many fields
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