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Data-Driven Multiresolution Camera Using the Foveal Adaptive Pyramid.

Martin González1, Antonio Sánchez-Pedraza2, Rebeca Marfil3

  • 1Dpto. Tecnología Electrónica, University of Málaga, Campus de Teatinos, 29071 Málaga, Spain. martin@uma.es.

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|November 30, 2016
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Summary
This summary is machine-generated.

This study introduces a hardware-software system for foveal vision, enabling spatially-variant resolution for image processing. It efficiently maps sensor data for foveal attention mechanisms, achieving high performance in object segmentation and attention tasks.

Keywords:
AP SoCfoveal imageshardware/software co-designhierarchical segmentationirregular pyramidsvisual attention

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Area of Science:

  • Computer Vision
  • Image Processing
  • Robotics

Background:

  • Traditional image processing often requires uniform high resolution, which is inefficient for tasks needing focus on specific regions.
  • Foveal vision systems offer a solution by using spatially-variant resolution, concentrating high detail in a small 'fovea' region while maintaining a wide field of view.

Purpose of the Study:

  • To propose a hardware system for mapping uniformly-sampled sensors to space-variant ones.
  • To integrate this mapping with a software-based foveal attention mechanism for enhanced image analysis.
  • To design an embedded architecture for an all-programmable system-on-chip (AP SoC).

Main Methods:

  • Development of a hardware system for space-variant resolution mapping.
  • Implementation of a software-based foveal attention mechanism processing foveal image streams.
  • Integration of both components into an embedded AP SoC architecture.

Main Results:

  • Demonstrated flexibility in data exchange between mapping and attention modules.
  • Achieved high performance rates for the spatial mapping procedure.
  • Experimental results show segmentation and attention models comparable to computationally intensive algorithms.

Conclusions:

  • The proposed integrated hardware/software architecture effectively implements foveal vision with attention.
  • The system offers efficient data processing for tasks requiring selective high-resolution focus.
  • This approach provides a computationally efficient alternative for advanced image analysis applications.