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An Efficient Hardware-Oriented Single-Pass Approach for Connected Component Analysis.

Fanny Spagnolo1, Stefania Perri2, Pasquale Corsonello3

  • 1Department of Informatics, Modeling, Electronics and System Engineering, University of Calabria, 87036 Rende, Italy.

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Summary
This summary is machine-generated.

This study introduces a novel hardware accelerator for Connected Component Analysis (CCA), enabling efficient single-pass feature extraction in real-time image processing. The design achieves high performance with minimal hardware resources, outperforming existing solutions.

Keywords:
FPGAsconnected component analysisembedded systemsfeatures extraction

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Area of Science:

  • Computer Vision
  • Hardware Acceleration
  • Image Analysis

Background:

  • Connected Component Analysis (CCA) is crucial for image analysis and pattern recognition.
  • CCA is computationally intensive, necessitating efficient hardware solutions.
  • Existing accelerators often struggle with real-time processing and resource constraints.

Purpose of the Study:

  • To develop a novel hardware accelerator for CCA.
  • To enable single-pass feature extraction from image frames.
  • To achieve high performance with a favorable resource-performance trade-off.

Main Methods:

  • A new single-pass CCA algorithm was developed.
  • A heterogeneous hardware system was implemented on a Xilinx Zynq-7000 FPGA SoC.
  • The design was validated using 640x480 resolution images.

Main Results:

  • The accelerator processed images in a single pass without suspending data flow.
  • Minimal hardware resources were utilized: 760 LUTs and 787 FFs.
  • Achieved a frame rate of ~325 fps and a throughput of 95.37 Mp/s.

Conclusions:

  • The proposed CCA hardware accelerator offers a superior performance-resources trade-off.
  • It enables efficient, real-time feature extraction for image analysis applications.
  • The design is suitable for resource-constrained embedded systems.