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Anisotropic-Scale Junction Detection and Matching for Indoor Images

发布日期:2018-03-29 17:00:54 阅读次数:[426]次 作者:

核心提示:来源出版物:IEEE TRANSACTIONS ON IMAGE PROCESSING

 

作者:Xue, N (Xue, Nan); Xia, GS (Xia, Gui-Song) ; Bai, X (Bai, Xiang); Zhang, LP (Zhang, Liangpei) ; Shen, WM (Shen, Weiming)

卷: 27  期: 1  页: 78-91 DOI: 10.1109/TIP.2017.2754945 出版年: JAN 2018
摘要: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 anisotropic-scale 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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