Steerable PCA for Rotation-Invariant Image Recognition | Zendy

Cédric Vonesch | Zendy; Frédéric Stauber | Zendy; Michaël Unser | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Steerable PCA for Rotation-Invariant Image Recognition

Author(s) -

Cédric Vonesch,

Frédéric Stauber,

Michaël Unser

Publication year - 2015

Publication title -

siam journal on imaging sciences

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.944

H-Index - 71

ISSN - 1936-4954

DOI - 10.1137/15m1014930

Subject(s) - principal component analysis , template , artificial intelligence , invariant (physics) , orientation (vector space) , rotation (mathematics) , pattern recognition (psychology) , computer science , domain (mathematical analysis) , particle filter , computer vision , mathematics , geometry , mathematical analysis , mathematical physics , programming language , kalman filter

In this paper, we propose a continuous-domain version of principal-component analysis, with the constraint that the underlying family of templates appears at arbitrary orientations. We show that the corresponding principal components are steerable. Our method can be used for designing steerable filters so that they best approximate a given collection of reference templates. We apply this framework to the detection and classification of micrometer-sized particles that are used in a microfluidic diagnostics system. This is done in two steps. First, we decompose the particles into a small number of templates and compute their steerable principal components. Then we use these principal components to automatically estimate the orientation and the class of each particle.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research