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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
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.
Received: 21.11.2017 Accepted: 29.01.2018
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
Linking options:
https://www.mathnet.ru/eng/co505 https://www.mathnet.ru/eng/co/v42/i2/p283
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Abstract page: | 264 | Full-text PDF : | 91 | References: | 26 |
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