https://github.com/bpavan16/cv-labs
This repository includes the codes from the computer vision labs of my 5th-semester course, showcasing implementations of various topics.
https://github.com/bpavan16/cv-labs
computer-vision edge-detection implementation opencv python
Last synced: 3 months ago
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This repository includes the codes from the computer vision labs of my 5th-semester course, showcasing implementations of various topics.
- Host: GitHub
- URL: https://github.com/bpavan16/cv-labs
- Owner: bPavan16
- License: mit
- Created: 2025-01-16T08:43:29.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-16T08:49:29.000Z (4 months ago)
- Last Synced: 2025-01-16T10:07:16.859Z (4 months ago)
- Topics: computer-vision, edge-detection, implementation, opencv, python
- Language: Jupyter Notebook
- Homepage:
- Size: 26.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Image Processing and Computer Vision Techniques
This repository contains Python implementations and comparative analyses of various image processing and computer vision techniques. The goal is to provide a comprehensive toolkit for analyzing, detecting, and processing features in images while comparing the performance of different algorithms.
## Table of Contents
- [Comparative Analysis of Edge Detection Methods](#comparative-analysis-of-edge-detection-methods)
- [RANSAC Implementation for Robust Linear Regression](#ransac-implementation-for-robust-linear-regression)
- [Harris Corner Detection](#harris-corner-detection)
- [Feature Matching Using RANSAC](#feature-matching-using-ransac)
- [Difference of Gaussians (DoG) Implementation](#difference-of-gaussians-dog-implementation)
- [Setup and Usage](#setup-and-usage)
- [Results](#results)
- [Contributing](#contributing)
---
## Comparative Analysis of Edge Detection Methods
### Description
A Python script to compare the performance of Gaussian, Sobel, and Canny edge detection methods on a given set of images. Metrics such as edge detection accuracy, computational efficiency, and noise robustness are measured and presented in a comparative analysis report.
### Features
- Implements Gaussian, Sobel, and Canny edge detection.
- Measures and compares:
- Edge detection accuracy.
- Computational efficiency.
- Noise robustness.
- Generates a detailed analysis report.
---
## RANSAC Implementation for Robust Linear Regression
### Description
A Python function to implement the RANSAC algorithm for robust linear regression. The function estimates the parameters of a linear model that best fits the inliers in a dataset with outliers.
### Features
- Implements the RANSAC algorithm.
- Works with synthetic datasets containing known outliers.
- Visualizes inliers, outliers, and the fitted linear model.
---
## Harris Corner Detection
### Description
A Python function to implement the Harris corner detection algorithm for identifying corner points in images.
### Features
- Detects corner points using the Harris corner detector.
- Supports images with varying corner densities.
- Visualizes detected corner points overlaid on the original images.
---
## Feature Matching Using RANSAC
### Description
An extended implementation of the RANSAC algorithm for feature matching between two images based on their local descriptors.
### Features
- Detects keypoints using the Harris corner detector.
- Uses SIFT descriptors for matching keypoints.
- Applies RANSAC to estimate the transformation matrix between matched keypoints.
- Visualizes matched features.
---
## Difference of Gaussians (DoG) Implementation
### Description
A Python function to compute the Difference of Gaussians (DoG) for a given input image. The function performs convolution of the image with two Gaussian kernels of different standard deviations and computes their difference.
### Features
- Computes DoG for images at different scales.
- Visualizes the resulting images to observe the effect of scale.
---
## Setup and Usage
### Prerequisites
- Python 3.7 or higher.
- Required libraries: `numpy`, `opencv-python`, `matplotlib`, `scikit-image`.
### Installation
1. Clone the repository:
```bash
git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
### Running the Code
- For edge detection analysis:
```bash
python edge_detection_comparison.py
```
- For RANSAC linear regression:
```bash
python ransac_regression.py
```
- For Harris corner detection:
```bash
python harris_corner_detection.py
```
- For feature matching using RANSAC:
```bash
python feature_matching_ransac.py
```
- For DoG implementation:
```bash
python difference_of_gaussians.py
```
---
## Results
Each script generates visualizations and analysis reports. These outputs include:
- Edge detection results and performance metrics.
- RANSAC regression plots with inliers and outliers.
- Images with detected corners overlaid.
- Matched keypoints between images.
- DoG results at various scales.
---
## Contributing
Contributions are welcome! If you have suggestions for improvement or new features, feel free to create a pull request or open an issue.
### Steps to Contribute
1. Fork this repository.
2. Create a feature branch:
```bash
git checkout -b feature-name
```
3. Commit your changes:
```bash
git commit -m "Description of changes"
```
4. Push to the branch:
```bash
git push origin feature-name
```
5. Create a pull request.
---
## License
This project is licensed under the MIT License. See the `LICENSE` file for more details.
---