# Corner Detection in Manifold-valued Images and in Vector Fields

### Aleksei Shestov, Mikhail Kumskov

#### Abstract

This paper is devoted to the problem of corner detection in manifold-valued images and in vector fields on manifolds. Our solution is a generalization of the Harris corner detector (C. Harris, 1988). As in the grayscale case, our algorithm is based on an estimation of a self-similarity of a point neighborhood. We define the self-similarity for the general cases and obtain approximations of it by an action of a bilinear form. This form can be viewed as a generalization of the structure tensor (M. Kass, 1987). The generalized structure tensor is then used as usual in the corner detection procedure. Finally, we describe future experiments: the algorithm will be tested on a task of chemical compounds classification.

Download#### Paper Citation

#### in Harvard Style

Shestov A. and Kumskov M. (2020). **Corner Detection in Manifold-valued Images and in Vector Fields**.In *Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,* ISBN 978-989-758-402-2, pages 405-411. DOI: 10.5220/0009102304050411

#### in Bibtex Style

@conference{visapp20,

author={Aleksei Shestov and Mikhail Kumskov},

title={Corner Detection in Manifold-valued Images and in Vector Fields},

booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},

year={2020},

pages={405-411},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0009102304050411},

isbn={978-989-758-402-2},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,

TI - Corner Detection in Manifold-valued Images and in Vector Fields

SN - 978-989-758-402-2

AU - Shestov A.

AU - Kumskov M.

PY - 2020

SP - 405

EP - 411

DO - 10.5220/0009102304050411