Shadow separation of pavement images based on morphological component analysis
type: Item
instance of: scholarly article
title: Shadow separation of pavement images based on morphological component analysis
zbMATH Open document ID: 1459.94015
DOI: 10.1155/2021/8828635
author: Changxia Ma
author: Heng Zhang
author: Bing Keong Li
published in: Journal of Control Science and Engineering
publication date: 2021-02-25T00:00:00Z
review text: Summary: The shadow of pavement images will affect the accuracy of road crack recognition and increase the rate of error detection. A shadow separation algorithm based on morphological component analysis (MCA) is proposed herein to solve the shadow problem of road imaging. The main assumption of MCA is that the image geometric structure and texture structure components are sparse within a class under a specific base or overcomplete dictionary, while the base or overcomplete dictionaries of each sparse representation of morphological components are incoherent. Thereafter, the corresponding image signal is transformed according to the dictionary to obtain the sparse representation coefficients of each part of the information, and the coefficients are shrunk by soft thresholding to obtain new coefficients. Experimental results show the effectiveness of the shadow separation method proposed in this paper.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 68U10
Mathematics Subject Classification ID: 68T10
zbMATH DE Number: 7315718
zbMATH Keywords: road crack recognition
zbMATH Keywords: shadow separation algorithm
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2021/8828635
OpenAlex ID: W3123701328
cites work: Image decomposition via the combination of sparse representations and a variational approach
description: scientific article; zbMATH DE number 7315718