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VisualEyes: A Modular Software System for Oculomotor Experimentation

VisualEyes: A Modular Software System for Oculomotor Experimentation

Published on: March 25, 2011

An object-based visual attention model for robotic applications.

Yuanlong Yu¹, George K I Mann, Raymond G Gosine

¹Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada. y.yu@mun.ca

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 5, 2010

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This study introduces an object-based visual attention model that uses low-dimensional features for fast attentional selection. The model enables robots to detect specific stationary and moving objects by integrating top-down and bottom-up processing.

Area of Science:

Computer Vision
Robotics
Cognitive Science

Background:

Visual attention is crucial for efficient information processing.
Existing models often lack a robust mechanism for object-specific selection.
Integrated competition hypothesis provides a framework for understanding attentional selection.

Purpose of the Study:

To propose an object-based visual attention model extending the integrated competition hypothesis.
To enable fast attentional selection of objects using low-dimensional features.
To apply the model for object detection tasks in robotics.

Main Methods:

A seven-module attention model incorporating long-term memory (LTM), preattentive processing, top-down biasing, bottom-up competition, saliency map generation, and perceptual completion.

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VisualEyes: A Modular Software System for Oculomotor Experimentation

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Published on: March 25, 2011

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Published on: August 1, 2018

A dual-coding object representation using local (intensity, color, orientation) and global (contour) features.

Two phases: a learning phase for training object representations and an attending phase for selection.

Main Results:

The model successfully segments the visual field into proto-objects and evaluates their saliency.
It mediates between bottom-up and top-down processing to generate saliency maps.
Demonstrated effectiveness in detecting task-specific stationary and moving objects in robotic applications.

Conclusions:

The proposed object-based visual attention model facilitates efficient and fast attentional selection.
The dual-coding representation enhances object recognition capabilities.
The model's successful application in robotics validates its practical utility.