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Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision

Shoaib Ehsan1, Adrian F Clark2, Naveed ur Rehman3

  • 1School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK. sehsan@essex.ac.uk.

Sensors (Basel, Switzerland)
|July 18, 2015
PubMed
Summary

This study introduces novel hardware algorithms for efficient integral image computation and storage in embedded vision systems. These methods enable faster processing and significantly reduce memory requirements for real-time applications.

Keywords:
embedded vision systemsintegral imagememory-efficient designparallel architecture

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Area of Science:

  • Computer Vision
  • Embedded Systems Engineering
  • Image Processing

Background:

  • Integral images are crucial for fast feature detection in algorithms like Speeded-Up Robust Features (SURF).
  • Resource-constrained embedded vision systems face challenges in integral image computation and storage due to hardware limitations.
  • Standard integral image calculation involves numerous additions, posing performance issues for large image data.

Purpose of the Study:

  • To develop efficient hardware algorithms for parallel integral image computation.
  • To propose a design strategy for a parallel computation unit reducing internal memory.
  • To present algorithms for decreasing integral image storage requirements in embedded systems.

Main Methods:

  • Developed two hardware algorithms based on decomposing recursive integral image equations for row-parallel computation.
  • Proposed an efficient design for a parallel integral image computation unit.
  • Introduced two algorithms specifically to address integral image storage reduction.

Main Results:

  • Achieved row-parallel computation of integral images without significant increase in operations.
  • Reduced internal memory requirements for the parallel computation unit by nearly 35% for HD video.
  • Decreased memory requirements for integral image storage by at least 44.44%.

Conclusions:

  • The proposed hardware algorithms and design strategies effectively address computational and storage challenges of integral images in embedded vision.
  • The developed architectures offer significant performance and memory efficiency improvements for real-time embedded vision applications.
  • A case study demonstrated the practical utility of these architectures in embedded vision systems.