Graph matching algorithm in position and orientation estimation tasks

Barinov, A. E.; Zakharov, A. A.

doi:10.1109/crmico.2014.6959454

Cited by 3 publications

(2 citation statements)

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“…The clustering is used in pattern recognition [14], object tracking [15] or reconstruction of the three-dimensional scenes [16,17]. The main problem is considered to be the question of the graph connectivity, the similarity of vertices and edges.…”

Section: Related Literaturementioning

confidence: 99%

Clustering using a random walk on graph for head pose estimation

Barinov

Zakharov

2015

2015 International Conference on Mechanical Engineering, Automation and Control Systems (MEACS)

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In this paper, the problem of head pose estimation is described. The solution consists of several stages. The clustering is a critical step. The clustering of feature points of the image is consuming and important step that needs to simplify and speed up. For this task, it is proposed to use the properties of a random walk on the graph. The random walk can lead to a measure of cluster cohesion. This approach is closely related to spectral graph theory. The paper presents formulas, steps of the algorithm and an example of calculations. Experiments and comparisons are made with the closest analogue, the method of normalized cut

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Section: Related Literaturementioning

confidence: 99%

Clustering using a random walk on graph for head pose estimation

Barinov

Zakharov

2015

2015 International Conference on Mechanical Engineering, Automation and Control Systems (MEACS)

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“…Spectral clustering is used to group regions with similar characteristics. Spectral graph theory is used in pattern recognition [11], reconstruction of three-dimensional scenes [12], tracking the movement of objects [13].…”

Section: Detectionmentioning

confidence: 99%

Automatic building detection from satellite images using spectral graph theory

Zakharov

Tuzhilkin

Zhiznyakov

2015

2015 International Conference on Mechanical Engineering, Automation and Control Systems (MEACS)

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Approach of detection of buildings on satellite images is presented in this paper. Actuality of work is shown. Possible areas of the use of the research results are presented. The detection is based on joint use of height, color, shape. The height of the object is determined based on energy minimization within the region. The image is segmented previously. The area of vegetation and the shadows are detected based on color invariants. Regions of the image are grouped based on the properties using spectral clustering. The shape and size of objects are analyzed additionally. The results of the algorithm are given.

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