Computer Optics
RUS  ENG    JOURNALS   PEOPLE   ORGANISATIONS   CONFERENCES   SEMINARS   VIDEO LIBRARY   PACKAGE AMSBIB  
General information
Latest issue
Archive

Search papers
Search references

RSS
Latest issue
Current issues
Archive issues
What is RSS



Computer Optics:
Year:
Volume:
Issue:
Page:
Find






Personal entry:
Login:
Password:
Save password
Enter
Forgotten password?
Register


Computer Optics, 2018, Volume 42, Issue 2, Pages 283–290
DOI: https://doi.org/10.18287/2412-6179-2018-42-2-283-290
(Mi co505)
 

This article is cited in 14 scientific papers (total in 14 papers)

IMAGE PROCESSING, PATTERN RECOGNITION

Object detection in images with a structural descriptor based on graphs

A. A. Zakharova, A. E. Barinova, A. L. Zhiznyakova, V. S. Titovb

a Murom Institute (branch), Vladimir State University named after Alexander and Nikolay Stoletovs, Murom, Russia
b Southwest State University, Kursk, Russia
References:
Abstract: We discuss the development of a structural descriptor for object detection in images. The descriptor is based on a graph, whose vertices are the centers of mass of segment features. The embedding of the graph in a vector space is implemented using a Young–Householder decomposition and based on differential geometry. Compound curves are used to describe the relationship between the points. The image graph is described by a matrix of curvature parameters. The distance matrix for the graphs of the candidate object and the reference object is calculated using the Hausdorff metric. A multidimensional scaling method is used to represent the results. Images of test objects and images of human faces are used to study the developed approach. A comparison of the developed descriptor with the Viola-Jones method is performed when detecting a human head in the image. The advantage of the developed approach is the image rotational invariance in the plane while searching for objects. The descriptor can detect objects rotated in space by angles of up to 50 degrees. Using the mass centers of segments of features as the graph vertices makes the approach more robust to changes in image acquisition angles in comparison with the approach that uses image features as the graph vertices.
Keywords: image analysis, objects detection, structural descriptor, graph embedding, computer vision.
Funding agency Grant number
Ministry of Education and Science of the Russian Federation 2.1950.2017/ПЧ
Russian Foundation for Basic Research 16-37-00235 мол_а
This work was partly funded by the RF Ministry of Education and Science under a state contract (project 2.1950.2017/ПЧ) and the Russian Foundation for Basic Research (RFBR grant No. 16-37-00235).
Received: 21.11.2017
Accepted: 29.01.2018
Document Type: Article
Language: Russian
Citation: A. A. Zakharov, A. E. Barinov, A. L. Zhiznyakov, V. S. Titov, “Object detection in images with a structural descriptor based on graphs”, Computer Optics, 42:2 (2018), 283–290
Citation in format AMSBIB
\Bibitem{ZakBarZhi18}
\by A.~A.~Zakharov, A.~E.~Barinov, A.~L.~Zhiznyakov, V.~S.~Titov
\paper Object detection in images with a structural descriptor based on graphs
\jour Computer Optics
\yr 2018
\vol 42
\issue 2
\pages 283--290
\mathnet{http://mi.mathnet.ru/co505}
\crossref{https://doi.org/10.18287/2412-6179-2018-42-2-283-290}
Linking options:
  • https://www.mathnet.ru/eng/co505
  • https://www.mathnet.ru/eng/co/v42/i2/p283
  • This publication is cited in the following 14 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Computer Optics
     
      Contact us:
     Terms of Use  Registration to the website  Logotypes © Steklov Mathematical Institute RAS, 2024