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A Linked List-Based Algorithm for Blob Detection on Embedded Vision-Based Sensors.

Ricardo Acevedo-Avila1, Miguel Gonzalez-Mendoza2, Andres Garcia-Garcia3

  • 1Department of Postgraduate Studies, Tecnológico de Monterrey, Campus Estado de México, Atizapán de Zaragoza, Estado de México 52926, Mexico. ricardo.acevedo@itesm.mx.

Sensors (Basel, Switzerland)
|May 31, 2016
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Summary
This summary is machine-generated.

This study introduces a novel algorithm for real-time blob detection optimized for embedded systems. The method achieves efficient object detection with minimal memory, suitable for resource-constrained platforms.

Keywords:
embedded computer visionfield programmable gate array (FPGA)object detection

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Area of Science:

  • Computer Vision
  • Embedded Systems Engineering
  • Algorithm Design

Background:

  • Blob detection is crucial for vision applications but often requires significant computational resources.
  • Existing algorithms are typically designed for general-purpose computers, limiting their use in embedded platforms.
  • There is a need for efficient blob detection methods that minimize memory consumption for real-time embedded applications.

Purpose of the Study:

  • To design a novel algorithm for real-time blob detection.
  • To minimize system memory consumption for embedded platforms.
  • To demonstrate the algorithm's applicability in resource-constrained environments.

Main Methods:

  • Developed a one-scan image processing algorithm for blob detection.
  • Utilized a linked-list data structure tree for labeling blobs based on shape and node information.
  • Implemented a blob detection co-processor on a low-powered field-programmable gate array (FPGA) for a smart video surveillance system.

Main Results:

  • The algorithm successfully detects objects in a single image scan.
  • A co-processor was built on FPGA hardware, demonstrating real-time capabilities.
  • Tested with character recognition tasks, showing a balance between accuracy and memory usage.

Conclusions:

  • The proposed algorithm is valid for real-time implementation on resource-constrained computing platforms.
  • The approach offers a practical solution for embedded vision systems requiring efficient blob detection.
  • The developed method shows potential for applications like smart video surveillance and character recognition.