Motion-based video segmentation using continuation method and robust cost functions

Cheng-Hong Yang; Janusz Konrad

doi:10.1117/12.298389

9 January 1998 Motion-based video segmentation using continuation method and robust cost functions

Cheng-Hong Yang, Janusz Konrad

Proceedings Volume 3309, Visual Communications and Image Processing '98; (1998) https://doi.org/10.1117/12.298389
Event: Photonics West '98 Electronic Imaging, 1998, San Jose, CA, United States

Abstract

We propose a new approach to spatial segmentation of video sequences that is based on motion attributes. The approach, similarly to some previous efforts, uses Markov random field models and maximum a posteriori probability estimation.Our approach is novel in three ways. First, we propose a general formation for the joint motion estimation. Secondly, instead of the usual quadratic models we propose a robust estimation criterion that eliminates the impact of outliers on the estimates. Thirdly, since solving the segmentation problem directly in the space of discrete labels is difficult, we opt for a continuation method over a Gaussian pyramid. Thus, the estimation process starts as a motion estimation and then slowly converges towards a motion-based segmentation by 'hardening' the smoothness constraint. The final result is a quasi-segmentation, i.e., the estimated vector field is continuous but almost peicewise constant, and must undergo subsequent quantization. We show experimental results on two natural image sequences; the resolution quasi-segmentations clearly extract moving objects. The method may serve as an initial stage for joint motion estimation and segmentation, or may produce final segmentations if suitable post- processing is applied.

Citation Download Citation

Cheng-Hong Yang and Janusz Konrad "Motion-based video segmentation using continuation method and robust cost functions", Proc. SPIE 3309, Visual Communications and Image Processing '98, (9 January 1998); https://doi.org/10.1117/12.298389

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