State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
School of Electronics Information and Communications, Huazhong University of Science and Technology, Wuhan, China.
IEEE Trans Image Process. 2018;27(1):78-91. doi: 10.1109/TIP.2017.2754945.
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 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 (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.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 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 (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.
结在刻画图像局部几何结构方面起着重要作用，而结的检测是一项长期以来但具有挑战性的任务。现有的结检测器通常侧重于识别结分支的位置和方向，而忽略它们的尺度，然而，这些尺度包含了丰富的图像几何形状。本文提出了一种新的结检测和特征描述方法，该方法特别利用了结的局部各向异性几何形状，并依靠模型来估计其尺度。输出的结具有各向异性尺度，即每个结分支都有一个尺度参数与之相关联，因此被命名为 （ASJs）。然后，我们将新检测到的 ASJs 应用于匹配室内图像，在室内图像中，视点和检测到的局部视觉特征（例如关键点）通常会发生剧烈变化，并且通常不够充足，缺乏独特的能力。我们提出利用我们的结的各向异性几何形状来提高室内图像的匹配精度。在一组室内图像上的匹配结果表明，我们的方法在室内图像匹配方面达到了最新的性能。
