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Binocular stereo matching algorithm based on MST cost aggregation.

Jian Zhang1, Yan Zhang2, Cong Wang1

  • 1School of information and Communication Engineering, Nanjing Institute of Technology, Nanjing 211167, China.

Mathematical Biosciences and Engineering : MBE
|July 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an improved binocular stereo matching algorithm using Minimum Spanning Tree (MST) cost aggregation. The novel approach enhances both accuracy and speed in computer vision applications.

Keywords:
binocular visioncost aggregationminimum spanning treestereo matching

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Achieving high precision and speed simultaneously in binocular stereo matching is a significant challenge.
  • Existing algorithms often face trade-offs between accuracy and computational efficiency.
  • The demand for faster and more accurate stereo vision systems is growing across various industries.

Purpose of the Study:

  • To propose an improved binocular stereo matching algorithm that overcomes the limitations of current methods.
  • To enhance both the accuracy and matching speed of stereo vision systems.
  • To leverage parallel computing for efficient stereo matching.

Main Methods:

  • Developed an improved binocular stereo matching algorithm utilizing Minimum Spanning Tree (MST) cost aggregation.
  • Optimized parallel algorithm performance by reducing tree height.
  • Introduced an improved Root to Leaf (L2R) cost aggregation algorithm.
  • Integrated stereo matching with parallel computing for synchronous algorithm-level parallelization.

Main Results:

  • The improved algorithm demonstrates high accuracy in binocular stereo vision tasks.
  • Experimental results confirm a significant increase in matching speed.
  • The approach effectively balances precision and speed, addressing a key challenge in the field.

Conclusions:

  • The proposed MST-based cost aggregation algorithm offers a superior solution for binocular stereo matching.
  • Combining stereo matching with parallel computing enhances system performance.
  • This method provides a viable pathway for real-time, high-accuracy stereo vision applications.