# Binocular Visual Measurement Method Based on Feature Matching

## TL;DR

This paper introduces a new binocular camera method that improves 3D measurement accuracy for objects with challenging surface textures.

## Contribution

The novel approach uses an improved feature matching algorithm with particle swarm optimization and region growing for better accuracy and robustness.

## Key findings

- The proposed method achieves an average relative error of 0.75% and an average measurement time of 0.82 s on the Middlebury dataset.
- The error matching rate is 2.02%, and the PSNR reaches 34 dB, showing improved performance.
- The algorithm is robust against brightness variations and noise for objects with sparse or weak textures.

## Abstract

To address the issues of low measurement accuracy and unstable results when using binocular cameras to detect objects with sparse surface textures, weak surface textures, occluded surfaces, low-contrast surfaces, and surfaces with intense lighting variations, a three-dimensional measurement method based on an improved feature matching algorithm is proposed. Initially, features are extracted from the left and right images obtained by the binocular camera. The extracted feature points serve as seed points, and a one-dimensional search space is established accurately based on the disparity continuity and epipolar constraints. The optimal search range and seed point quantity are obtained using the particle swarm optimization algorithm. The zero-mean normalized cross-correlation coefficient is employed as a similarity measure function for region growing. Subsequently, the left and right images are matched based on the grayscale information of the feature regions, and seed point matching is performed within each matching region. Finally, the obtained matching pairs are used to calculate the three-dimensional information of the target object using the triangulation formula. The proposed algorithm significantly enhances matching accuracy while reducing algorithm complexity. Experimental results on the Middlebury dataset show an average relative error of 0.75% and an average measurement time of 0.82 s. The error matching rate of the proposed image matching algorithm is 2.02%, and the PSNR is 34 dB. The algorithm improves the measurement accuracy for objects with sparse or weak textures, demonstrating robustness against brightness variations and noise interference.