Spatial detection of tv channel logos as outliers from the content

Ahmet Ekin; Ralph Braspenning

doi:10.1117/12.642739

19 January 2006 Spatial detection of tv channel logos as outliers from the content

Ahmet Ekin, Ralph Braspenning

Proceedings Volume 6077, Visual Communications and Image Processing 2006; 60770X (2006) https://doi.org/10.1117/12.642739
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

This paper proposes a purely image-based TV channel logo detection algorithm that can detect logos independently from their motion and transparency features. The proposed algorithm can robustly detect any type of logos, such as transparent and animated, without requiring any temporal constraints whereas known methods have to wait for the occurrence of large motion in the scene and assume stationary logos. The algorithm models logo pixels as outliers from the actual scene content that is represented by multiple 3-D histograms in the YC_BC_R space. We use four scene histograms corresponding to each of the four corners because the content characteristics change from one image corner to another. A further novelty of the proposed algorithm is that we define image corners and the areas where we compute the scene histograms by a cinematic technique called Golden Section Rule that is used by professionals. The robustness of the proposed algorithm is demonstrated over a dataset of representative TV content.

Citation Download Citation

Ahmet Ekin and Ralph Braspenning "Spatial detection of tv channel logos as outliers from the content", Proc. SPIE 6077, Visual Communications and Image Processing 2006, 60770X (19 January 2006); https://doi.org/10.1117/12.642739

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