Angel3245 / Reflection-symmetry-signature

This project involves the development of a Python implementation for detecting reflection symmetry in shapes using shape signatures.

Python implementation of reflection symmetry detection of shapes based on shape signatures

This project involves the development of a Python implementation for detecting reflection symmetry in shapes using shape signatures based on Reflection symmetry detection of shapes based on shape signatures [1]. Reflection symmetry detection is crucial in various applications, including computer vision, image processing, and pattern recognition. Shape signatures provide a compact representation of the shape's geometric features, facilitating efficient symmetry analysis.

Reflection Symmetry Detection

The process of detecting reflection symmetry involves:

• Generating the shape signature: Extracting the shape signature from the binary image.
• Computing symmetry axes: Identifying potential axes of symmetry by analyzing the shape signature.
• Evaluating symmetry: Quantifying the degree of symmetry for each axis and determining the most symmetric one.

Dataset Description

The dataset used in this project consists of binary images of various shapes, including both symmetric and asymmetric shapes. These images are chosen to evaluate the robustness and accuracy of the symmetry detection algorithm.

Results comparison

• Using original image:

---

• Using skeleton:

Install the environment in a local device

The following steps must be followed to install the dependencies required for running the application:

1. Navigate to the project directory

cd (`project_path`)

2. Create a conda environment from a .yml file

conda env create -f environment.yml

3. Install dependencies

pip install -r requirements.txt

Project Structure

The project is organized as follows:

├── images
├── src
│   ├── centroid.py
│   ├── file.py
│   ├── symmetry.py

Dependencies

The main libraries used in this project include:

• OpenCV
• NumPy
• Matplotlib
• Scipy
• scikit-image

Evaluation

The evaluation process includes:

• Qualitative analysis: Visual comparison of detected symmetry axes on various shapes.
• Quantitative analysis: Using metrics such as symmetry accuracy (jaccard_similarity) to assess performance.

References

[1] Nguyen, T. P., Truong, H. P., Nguyen, T. T., & Kim, Y. (2022). Reflection symmetry detection of shapes based on shape signatures. Pattern Recognition, 128, 108667. DOI: 10.1016/j.patcog.2022.108667