MTV-2D Algorithm for Stereoscopic Molecular Tagging Velocimetry

The MTV-2D Algorithm is specifically designed for 2D stereoscopic MTV, leveraging laser grid lines with a signal-to-noise ratio greater than 2 for optimal performance. This algorithm uses a three-staged cross-correlation optimization process to determine the correspondence between the source and target images.

Algorithm Overview

Key Features:

1. High Signal-to-Noise Ratio: Designed for laser grid lines with a signal-to-noise ratio > 2.
2. Hough Line Transformation: Identifies grid patterns in the source image.
3. Three-Stage Cross-Correlation: Ensures robust matching between source and target images.

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Example Image

The algorithm is design for the specific geometry of image generated by molecular tagging velocimetry using grid optical components. Below is an example of a source image with grid lines detected:

• Description: Example image from MTV.

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Algorithm Workflow

1. Stage 1: Detect grid lines in the source image using Hough line transformation as the base template.

   • Visualization:

2. Stage 2: Perform initial coarse cross correspondence of image by mapping the rhombus region from target image with the source image.

   • Visualization:

3. Stage 3: Perform template matchign across two image to determine plausible region where corresponding intersection for source image exist in the target image.

   • Visualization:

4. Stage 4: Perform sub-pixel detection using template matching using the template generated from stage 1 with the determined search region in stage 3.

   • Visualization:

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Output Results

The final output includes a fully matched grid pattern between the source and target images, as shown below:

• Description: This image demonstrates the final matched grid, illustrating the algorithm's effectiveness in accurately determining correspondences.

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Requirements and Setup

• Dependencies: Python 3.8+, OpenCV, NumPy, SciPy
• Installation:

  pip install -r requirements.txt