Complex Color Space Segmentation to Classify Objects in Urban Environments

Juan-Jose Cardenas-Cornejo, Mario-Alberto Ibarra-Manzano, Daniel-Alberto Razo-Medina and Dora-Luz Almanza-Ojeda ()
Additional contact information
Juan-Jose Cardenas-Cornejo: Electronics Engineering Department, DICIS, University of Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico
Mario-Alberto Ibarra-Manzano: Electronics Engineering Department, DICIS, University of Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico
Daniel-Alberto Razo-Medina: Electronics Engineering Department, DICIS, University of Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico
Dora-Luz Almanza-Ojeda: Electronics Engineering Department, DICIS, University of Guanajuato, Carr. Salamanca-Valle de Santiago KM. 3.5 + 1.8 Km., Salamanca 36885, Mexico

Mathematics, 2022, vol. 10, issue 20, 1-18

Abstract: Color image segmentation divides the image into areas that represent different objects and focus points. One of the biggest problems in color image segmentation is the lack of homogeneity in the color of real urban images, which generates areas of over-segmentation when traditional color segmentation techniques are used. This article describes an approach to detecting and classifying objects in urban environments based on a new chromatic segmentation to locate focus points. Based on components a and b on the CIELab space, we define a chromatic map on the complex space to determine the highest threshold values by comparing neighboring blocks and thus divide various areas of the image automatically. Even though thresholds can result in broad segmentation areas, they suffice to locate centroids of patches on the color image that are then classified using a convolutional neural network (CNN). Thus, this broadly segmented image helps to crop only outlying areas instead of classifying the entire image. The CNN is trained to use six classes based on the patches drawn from the database of reference images from urban environments. Experimental results show a high score for classification accuracy that confirms the contribution of this segmentation approach.

Keywords: image segmentation; complex numbers; CNN classifier; outdoor environments (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/20/3752/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/20/3752/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:20:p:3752-:d:940112

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().