Segmentation of motion textures using mixed-state Markov random fields

T. Crivelli; B. Cernuschi-Frías; P. Bouthemy; J. F. Yao

doi:10.1117/12.674648

25 August 2006 Segmentation of motion textures using mixed-state Markov random fields

T. Crivelli, B. Cernuschi-Frías, P. Bouthemy, J. F. Yao

Proceedings Volume 6315, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX; 63150J (2006) https://doi.org/10.1117/12.674648
Event: SPIE Optics + Photonics, 2006, San Diego, California, United States

Abstract

The aim of this work is to model the apparent motion in image sequences depicting natural dynamic scenes (rivers, sea-waves, smoke, fire, grass etc) where some sort of stationarity and homogeneity of motion is present. We adopt the mixed-state Markov Random Fields models recently introduced to represent so-called motion textures. The approach consists in describing the distribution of some motion measurements which exhibit a mixed nature: a discrete component related to absence of motion and a continuous part for measurements different from zero. We propose several extensions on the spatial schemes. In this context, Gibbs distributions are analyzed, and a deep study of the associated partition functions is addressed. Our approach is valid for general Gibbs distributions. Some particular cases of interest for motion texture modeling are analyzed. This is crucial for problems of segmentation, detection and classification. Then, we propose an original approach for image motion segmentation based on these models, where normalization factors are properly handled. Results for motion textures on real natural sequences demonstrate the accuracy and efficiency of our method.

Citation Download Citation

T. Crivelli, B. Cernuschi-Frías, P. Bouthemy, and J. F. Yao "Segmentation of motion textures using mixed-state Markov random fields", Proc. SPIE 6315, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX, 63150J (25 August 2006); https://doi.org/10.1117/12.674648

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