Extracting focused object from low depth-of-field image sequences

Jungwoo Park; Changick Kim

doi:10.1117/12.642319

19 January 2006 Extracting focused object from low depth-of-field image sequences

Jungwoo Park, Changick Kim

Proceedings Volume 6077, Visual Communications and Image Processing 2006; 60771O (2006) https://doi.org/10.1117/12.642319
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

The paper proposes a novel unsupervised video object segmentation algorithm for image sequences with low depth-offield (DOF), which is a popular photographic technique enabling to represent the intention of photographer by giving a clear focus only on an object-of-interest (OOI). The proposed algorithm largely consists of two modules. The first module automatically extracts OOIs from the first frame by separating sharply focused OOIs from other out-of-focused foreground or background objects. The second module tracks OOIs for the rest of the video sequence, aimed at running the system in real-time, or at least, semi-real-time. The experimental results indicate that the proposed algorithm provides an effective tool, which can be a basis of applications, such as video analysis for virtual reality, immersive video system, photo-realistic video scene generation and video indexing systems.

Citation Download Citation

Jungwoo Park and Changick Kim "Extracting focused object from low depth-of-field image sequences", Proc. SPIE 6077, Visual Communications and Image Processing 2006, 60771O (19 January 2006); https://doi.org/10.1117/12.642319

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