Vanishing point detection and line classification with BPSO

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• 作者：Lei Han ; Chenrong Huang ; Shengnan Zheng ; Zhen Zhang…
• 关键词：Vanishing point estimation ; Visual measurement ; BPSO ; Variance coefficient
• 刊名：Signal, Image and Video Processing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：11
• 期：1
• 页码：17-24
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing; Image Processing and Computer Vision; Computer Imaging, Vision, Pattern Recognition and Graphics; Multimedia Information Systems;
• 出版者：Springer London
• ISSN：1863-1711
• 卷排序：11

文摘

Estimating image vanishing points has many applications in the computer vision field, such as robotic navigation, visual measurement, camera calibration, 3D reconstruction and augmented reality, which requires a balance between accuracy and rate. In this paper, we present an algorithm to accurately and efficiently detect vanishing points and classify lines through the clustering method and binary particle swarm optimization (BPSO). First, lines are clustered according to their slope angles based on an iterative BPSO process, since parallel lines, in a medium-to-long range scene, present similar slopes. The solutions are continuously evaluated using multiple factors, such as the number and length of the line segments and their distance to the related vanishing points. The coefficient of variation is applied to weigh these factors. As a result, all possible non-orthogonal vanishing points in the image are directly detected, irrespective of the camera calibration parameters to avoid mapping segments on the Gaussian sphere. Compared with other algorithms on the York Urban Database, the proposed algorithm exhibits significant performance improvements.