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Updated: Jun 17, 2026

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents

Utilizing a Reconfigurable Maze System to Enhance the Reproducibility of Spatial Navigation Tests in Rodents

Published on: December 2, 2022

Evolving mazes from images.

Liang Wan¹, Xiaopei Liu, Tien-Tsin Wong

¹The Chinese University of Hong Kong and City University of Hong Kong, Hong Kong. liangwan@cityu.edu.hk

IEEE Transactions on Visualization and Computer Graphics

|January 16, 2010

Summary

This summary is machine-generated.

We developed a new reaction diffusion (RD) simulator using cellular neural networks to create image-based mazes. This method allows for intuitive control and generates mazes that preserve image structures.

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Area of Science:

Computer Vision
Computational Imaging
Algorithmic Art

Background:

Traditional reaction diffusion (RD) methods offer limited control over pattern generation.
Generating complex, image-resembling mazes with precise structural preservation is challenging.

Purpose of the Study:

To introduce a novel reaction diffusion (RD) simulator for evolving image-resembling mazes.
To enhance control over maze generation, preserving salient image structures.
To enable intuitive user interaction for maze appearance modification.

Main Methods:

Developed a reaction diffusion (RD) simulator on a cellular neural network platform.
Implemented user-controlled "painting" brushes for spatially varying maze appearance.
Integrated a solution path generation guided by user-specified curves.

Main Results:

Successfully evolved mazes that faithfully preserve interior structures of source images.
Generated mazes with controllable regular and organic appearances and varying passage spacing.
Demonstrated seamless maze generation from composite source images.

Conclusions:

The proposed RD simulator offers high controllability and intuitive "painting" for maze generation.
The method effectively preserves image structures and creates visually appealing, seam-free mazes.
This approach provides a powerful tool for generating complex mazes from diverse image inputs.