Relaxation network for a feature-driven visual attention system

Ruggero Milanese; Jean-Marc Bost; Thierry Pun

doi:10.1117/12.130859

16 December 1992 Relaxation network for a feature-driven visual attention system

Ruggero Milanese, Jean-Marc Bost, Thierry Pun

Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992) https://doi.org/10.1117/12.130859
Event: San Diego '92, 1992, San Diego, CA, United States

Abstract

In this paper an attention module is described, which can be used by an active vision system to generate gaze changes. This module is based on a bottom-up, feature-driven analysis of the image. The results are regions of the input image which contain strange features, i.e., locations of the most `interesting' and `important' information. The method proposed for detecting such regions is based on the decomposition of the input image into a set of independent retinotopic feature maps. Each map represents the value of a certain attribute computed on a set of low-level primitives such as contours and regions. Relevant objects can be detected if the corresponding primitives have a feature value strongly different from the neighboring ones. Local comparisons of feature values are used to compute such measures of `difference' for each feature map and give rise to a corresponding set of conspicuity maps. In order to obtain a single measure of interest for each location and to make the process robust to noise, a relaxation algorithm is run on the set of conspicuity maps. A dozen iterations are sufficient to detect a binary mask identifying the attention regions. Results on real scenes are presented.

Citation Download Citation

Ruggero Milanese, Jean-Marc Bost, and Thierry Pun "Relaxation network for a feature-driven visual attention system", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130859

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