Maze-Solving Application Using OpenCV

Abstract

This project implements a maze-solving application using computer vision techniques with OpenCV. The application leverages binarization, morphological operations, and labeling to efficiently identify and solve mazes. The report includes a detailed explanation of the methodology, key code excerpts, visual results, and performance analysis. It also explores the computational performance and scalability of the approach, offering insights for future improvements.

Introduction

Maze solving is a classic problem in computer vision, commonly used to demonstrate image processing and pathfinding algorithms. This project processes maze images using OpenCV, identifying paths and generating solutions. Key objectives include:

• Implementing image binarization and morphological operations.
• Identifying connected components using labeling.
• Solving the maze and visualizing the solution.
• Analyzing the computational performance of the implemented approach.

Maze-solving algorithms are widely used in robotics navigation, video game development, and artificial intelligence. This report provides step-by-step implementation details to ensure reproducibility and clarity for similar projects.

Problem Analysis

The input to the system is a grayscale maze image where:

• Pathways are represented by white pixels (value = 255).
• Walls are represented by black pixels (value = 0).

Challenges:

• Noise in the input image can obscure pathways.
• Accurate binarization of the image to distinguish walls and pathways.
• Ensuring scalability for larger or more complex mazes.

The computational efficiency of the solution is a key consideration, especially when processing high-resolution maze images.

Methodology

Image Binarization

The first step involves converting the grayscale image to a binary image, simplifying further processing. The binarization function iterates through each pixel and assigns a binary value based on a threshold to distinguish walls from pathways.

Morphological Operations

Morphological operations refine the maze structure by modifying pixel regions, ensuring pathways are continuous and walls are well-defined.

Erosion

Erosion reduces the size of white regions (pathways) by removing boundary pixels.

Dilation

Dilation expands white regions by adding pixels to their boundaries, ensuring continuity in pathways after erosion.

Identification of Regions and Paths

The algorithm assigns unique labels to connected components using recursive labeling.

Computational Performance

Performance is evaluated using OpenCV's getTickCount function for runtime and clock cycle measurements.

Results

The final output of the system is the solved maze with the identified pathway highlighted. The following figure shows an example of the final output.

Discussion

The approach effectively solves mazes using OpenCV's image processing capabilities. However, limitations include:

• Sensitivity to noise in input images.
• Dependence on maze structure for determining element size.

Future work can improve noise handling and extend the approach to handle more complex mazes. Optimizations in morphological operations could enhance computational performance.

Conclusion

This project demonstrates the application of computer vision techniques to solve mazes efficiently. The implemented system provides accurate results for structured mazes and lays the foundation for future improvements in noise robustness and scalability.

References

OpenCV Documentation: https://docs.opencv.org/
Maze-solving algorithms in computer vision literature.
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Wikipedia Images: https://it.wikipedia.org/wiki/