supriyabm-13

🎵 stereo-matching-algorithms-matlab - Fast Algorithms for Disparity Mapping

🚀 Getting Started

Welcome to the stereo-matching-algorithms-matlab repository! This application provides optimized stereo matching algorithms in MATLAB, including:

• Block Matching
• Dynamic Programming
• Semi-Global Matching
• Semi-Global Block Matching
• Belief Propagation

These algorithms help create disparity maps, allowing for advanced 3D vision applications.

📥 Download & Install

To get the software, visit the Releases page and download the latest version of the application. Click the link below:

Visit this page to download

Follow these steps to install the software:

1. Click the link above to reach the Releases page.
2. Locate the latest release version.
3. Download the zip file that corresponds to your operating system.
4. Extract the contents of the zip file to a folder on your computer.

🖥️ System Requirements

Before you start, make sure your computer can run this software. Here are the recommended requirements:

• MATLAB R2018a or newer
• At least 4 GB of RAM
• Minimum of 500 MB of free disk space
• Operating System: Windows, macOS, or Linux

⚙️ Running the Software

After downloading and installing the software, open MATLAB and follow these steps to run the algorithms:

1. Open MATLAB.
2. Navigate to the folder where you extracted the files.
3. You will find example scripts for each algorithm. Open an example script to see how to use that algorithm.
4. Modify the input images as needed and run the script.

Example

For instance, if you want to run the Block Matching algorithm:

1. Open the block_matching_example.m file.
2. Load the left and right images you want to use for disparity mapping. You can replace the sample images with your own.
3. Run the script by clicking the “Run” button in MATLAB.

🧑‍💻 Using the Algorithms

Here is a brief description of each algorithm available in the software:

Block Matching

The Block Matching algorithm is a simple yet effective method for finding corresponding pixels in stereo images. It splits the images into blocks and calculates the disparity for each block.

Dynamic Programming

Dynamic Programming provides an efficient approach for matching two stereo images, minimizing errors through path optimization. This leads to improved disparity maps.

Semi-Global Matching (SGM)

SGM combines local and global optimization techniques. It balances speed and accuracy, providing high-quality disparity maps in a reasonable amount of time.

Semi-Global Block Matching (SGBM)

SGBM is a variation of SGM that focuses on block-based processing. It ensures fast computation while retaining high accuracy in results.

Belief Propagation

This advanced algorithm uses probabilistic approaches for matching. It iteratively refines the disparity map, delivering precise results suitable for complex scenes.

📊 Example Results

Each algorithm produces a disparity map, which visually represents the depth of objects in the images. Example outputs can be found in the examples folder within the downloaded files. Open the results.m script to view the results generated by each algorithm.

🔍 Helpful Tips

• Ensure your images are well-aligned for better results.
• Experiment with different algorithms to find the best one for your needs.
• Check MATLAB’s documentation for additional help on running scripts.

🌟 Topics Covered

This repository covers a wide range of topics related to stereo matching:

• Belief Propagation
• Block Matching and Algorithms
• Disparity Maps
• Dynamic Programming
• Loopy Belief Propagation
• Semi-Global Matching
• SGM Algorithms

📚 Additional Resources

For a deeper understanding of stereo matching algorithms, consider visiting these resources:

1. Computer Vision: Algorithms and Applications
2. Matlab Documentation
3. 3D Vision Basics

Thank you for exploring the stereo matching algorithms in MATLAB. Your feedback and contributions are welcome!