Shape analysis using Pytiyev morphologic paradigm and its use in machine vision

Alexander A. Stepanov; Sergey Yu. Zheltov; Yuri V. Vizilter

doi:10.1117/12.189147

6 October 1994 Shape analysis using Pytiyev morphologic paradigm and its use in machine vision

Alexander A. Stepanov, Sergey Yu. Zheltov, Yuri V. Vizilter

Proceedings Volume 2350, Videometrics III; (1994) https://doi.org/10.1117/12.189147
Event: Photonics for Industrial Applications, 1994, Boston, MA, United States

Abstract

Correlation techniques are widely used to match corresponding areas in stereo image pairs. This technique provides pixel correspondence that is required for the generation of 3D data. However correlation based approaches can provide false results and inaccurate matching due to noise and geometric and radiometric distortions in the stereo images. This paper is devoted to not well known outside Russia the Pytiyev morphological approach. The main idea of this approach is based on terms of the set topology theory and the projection on subspace created by the conditions of image changeability including radiometric distortions. The shape of image acquires quantitative mathematical description based on topological ideas. The specific correlational measure is built. This method can be effectively used in image matching and comparison tasks.

Citation Download Citation

Alexander A. Stepanov, Sergey Yu. Zheltov, and Yuri V. Vizilter "Shape analysis using Pytiyev morphologic paradigm and its use in machine vision", Proc. SPIE 2350, Videometrics III, (6 October 1994); https://doi.org/10.1117/12.189147

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available