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A computer vision system for rapid search inspired by surface-based attention mechanisms from human perception
Johannes Mohr1, Jong-Han Park1, Klaus Obermayer1
1Department for Electrical Engineering and Computer Science, Technische Universität Berlin, Germany; MAR 5-6, Marchstr. 23, D-10587 Berlin, Germany.
This study introduces a surface-based attention mechanism for computer vision, enhancing visual search efficiency. By grouping depth cues into surfaces, the system achieves real-time performance with minimal computational cost.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Cognitive Science
Background:
- Human visual search relies on selective attention to relevant scene regions.
- Biological vision suggests object surfaces are fundamental units for attentional processes.
Purpose of the Study:
- To demonstrate how surface-based attention mechanisms can accelerate computer vision systems for visual search.
- To improve the efficiency of visual search algorithms.
Main Methods:
- Utilizing fast perceptual grouping of depth cues to represent the visual world as surfaces.
- Implementing a short-term memory for surface representation, updated dynamically.
- Employing a top-down guided attention mechanism to select surfaces for recognition module inspection.
Main Results:
- The proposed attention framework operates with low computational overhead (approx. 11 ms).
- The system achieves real-time performance in visual search tasks.
- A substantial increase in search efficiency was observed.
Conclusions:
- Surface-based attention is an effective mechanism for speeding up computer vision search.
- This approach offers a computationally efficient method for real-time visual search.
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