{"id":24318183,"url":"https://github.com/bpavan16/cv-labs","last_synced_at":"2026-04-29T12:38:11.773Z","repository":{"id":272730329,"uuid":"917572375","full_name":"bPavan16/cv-labs","owner":"bPavan16","description":"This repository includes the codes from the computer vision labs of my 5th-semester course, showcasing implementations of various topics.","archived":false,"fork":false,"pushed_at":"2025-01-16T08:49:29.000Z","size":27436,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-10T18:47:40.938Z","etag":null,"topics":["computer-vision","edge-detection","implementation","opencv","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bPavan16.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-16T08:43:29.000Z","updated_at":"2025-03-08T13:00:42.000Z","dependencies_parsed_at":"2025-01-16T10:18:15.409Z","dependency_job_id":null,"html_url":"https://github.com/bPavan16/cv-labs","commit_stats":null,"previous_names":["bpavan16/cv-labs"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/bPavan16/cv-labs","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bPavan16%2Fcv-labs","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bPavan16%2Fcv-labs/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bPavan16%2Fcv-labs/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bPavan16%2Fcv-labs/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bPavan16","download_url":"https://codeload.github.com/bPavan16/cv-labs/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bPavan16%2Fcv-labs/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32426588,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-29T12:24:25.982Z","status":"ssl_error","status_checked_at":"2026-04-29T12:24:24.439Z","response_time":110,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","edge-detection","implementation","opencv","python"],"created_at":"2025-01-17T14:37:26.103Z","updated_at":"2026-04-29T12:38:11.743Z","avatar_url":"https://github.com/bPavan16.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Image Processing and Computer Vision Techniques\n\nThis 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.\n\n## Table of Contents\n\n- [Comparative Analysis of Edge Detection Methods](#comparative-analysis-of-edge-detection-methods)\n- [RANSAC Implementation for Robust Linear Regression](#ransac-implementation-for-robust-linear-regression)\n- [Harris Corner Detection](#harris-corner-detection)\n- [Feature Matching Using RANSAC](#feature-matching-using-ransac)\n- [Difference of Gaussians (DoG) Implementation](#difference-of-gaussians-dog-implementation)\n- [Setup and Usage](#setup-and-usage)\n- [Results](#results)\n- [Contributing](#contributing)\n\n---\n\n## Comparative Analysis of Edge Detection Methods\n\n### Description\nA 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.\n\n### Features\n- Implements Gaussian, Sobel, and Canny edge detection.\n- Measures and compares:\n  - Edge detection accuracy.\n  - Computational efficiency.\n  - Noise robustness.\n- Generates a detailed analysis report.\n\n---\n\n## RANSAC Implementation for Robust Linear Regression\n\n### Description\nA 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.\n\n### Features\n- Implements the RANSAC algorithm.\n- Works with synthetic datasets containing known outliers.\n- Visualizes inliers, outliers, and the fitted linear model.\n\n---\n\n## Harris Corner Detection\n\n### Description\nA Python function to implement the Harris corner detection algorithm for identifying corner points in images.\n\n### Features\n- Detects corner points using the Harris corner detector.\n- Supports images with varying corner densities.\n- Visualizes detected corner points overlaid on the original images.\n\n---\n\n## Feature Matching Using RANSAC\n\n### Description\nAn extended implementation of the RANSAC algorithm for feature matching between two images based on their local descriptors.\n\n### Features\n- Detects keypoints using the Harris corner detector.\n- Uses SIFT descriptors for matching keypoints.\n- Applies RANSAC to estimate the transformation matrix between matched keypoints.\n- Visualizes matched features.\n\n---\n\n## Difference of Gaussians (DoG) Implementation\n\n### Description\nA 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.\n\n### Features\n- Computes DoG for images at different scales.\n- Visualizes the resulting images to observe the effect of scale.\n\n---\n\n## Setup and Usage\n\n### Prerequisites\n- Python 3.7 or higher.\n- Required libraries: `numpy`, `opencv-python`, `matplotlib`, `scikit-image`.\n\n### Installation\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/your-username/your-repo-name.git\n   cd your-repo-name\n   ```\n2. Install dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n### Running the Code\n- For edge detection analysis:\n  ```bash\n  python edge_detection_comparison.py\n  ```\n- For RANSAC linear regression:\n  ```bash\n  python ransac_regression.py\n  ```\n- For Harris corner detection:\n  ```bash\n  python harris_corner_detection.py\n  ```\n- For feature matching using RANSAC:\n  ```bash\n  python feature_matching_ransac.py\n  ```\n- For DoG implementation:\n  ```bash\n  python difference_of_gaussians.py\n  ```\n\n---\n\n## Results\n\nEach script generates visualizations and analysis reports. These outputs include:\n- Edge detection results and performance metrics.\n- RANSAC regression plots with inliers and outliers.\n- Images with detected corners overlaid.\n- Matched keypoints between images.\n- DoG results at various scales.\n\n---\n\n## Contributing\n\nContributions are welcome! If you have suggestions for improvement or new features, feel free to create a pull request or open an issue.\n\n### Steps to Contribute\n1. Fork this repository.\n2. Create a feature branch:\n   ```bash\n   git checkout -b feature-name\n   ```\n3. Commit your changes:\n   ```bash\n   git commit -m \"Description of changes\"\n   ```\n4. Push to the branch:\n   ```bash\n   git push origin feature-name\n   ```\n5. Create a pull request.\n\n---\n\n## License\n\nThis project is licensed under the MIT License. See the `LICENSE` file for more details.\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbpavan16%2Fcv-labs","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbpavan16%2Fcv-labs","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbpavan16%2Fcv-labs/lists"}