Step‐by‐step pipeline processing approach for line segment detection. Issue 6 (24th April 2017)

• Record Type: Journal Article
• Authors: Shao, Chunyan; Ding, Qinghai; Luo, Haibo; Chang, Zheng; Zhang, Chi; Zheng, Tianjiang
• Is Part Of: IET image processing. Volume 11:Issue 6(2017)
• Page Start: 416
• Page End: 424
• Publication Date: 2017-04-24
• Subjects: image segmentation -- affine transforms -- edge detection -- eigenvalues and eigenfunctions; line segment detection -- step‐by‐step pipeline processing approach -- resistant to affine transformation and monotonic intensity change descriptor -- RATMIC descriptor -- Canny detector -- Harris corner detector -- regions of interest; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0493
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : This study proposes a line segment detection that can efficiently and effectively handle non‐linear uniform intensity changes. The presented sketching algorithm applies the resistant to affine transformation and monotonic intensity change (RATMIC) descriptor to conduct binary translation in the image pre‐processing step, which can remove the unwanted smoothing of the Canny detector in most line detections. The Harris corner detector is applied to catch regions of line segments for the purpose of simulating the composition of sketching and achieving a sense of unity within the picture. Furthermore, the RATMIC descriptor is employed to obtain binary images of the regions of interest (ROIs). Finally, small eigenvalue analysis is implemented to detect straight lines in the ROIs. The experiments conducted on various images with image rotation, scaling, and translation validate the effectiveness of the proposed method. The experimental results also demonstrate that about 30% in the overall coverage of major lines and 20% in the coverage per major line are increased compared with the state‐of‐the‐art line detectors. Moreover, the performance of the proposed method produces a combined advantage of ∼17% in the coverage of line segments over the line segment detector with noisy images.