https://github.com/gsaini/computer-vision-case-studies
This repository is a personal collection of computer vision case studies, study materials, notebooks, datasets, and small projects.
https://github.com/gsaini/computer-vision-case-studies
artificial-intelligence case-study computer-vision machine-learning python
Last synced: 3 days ago
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This repository is a personal collection of computer vision case studies, study materials, notebooks, datasets, and small projects.
- Host: GitHub
- URL: https://github.com/gsaini/computer-vision-case-studies
- Owner: gsaini
- Created: 2025-10-10T04:21:33.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-11-10T01:19:35.000Z (7 months ago)
- Last Synced: 2026-06-14T23:34:31.339Z (3 days ago)
- Topics: artificial-intelligence, case-study, computer-vision, machine-learning, python
- Language: Jupyter Notebook
- Homepage: https://app.devin.ai/wiki/gsaini/computer-vision-case-studies
- Size: 72.8 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Computer Vision Case Studies
Welcome to the Computer Vision Case Studies repository! This collection is curated to help learners and practitioners explore real-world computer vision problems, solutions, and techniques. Here you'll find study materials, annotated notebooks, datasets, and small projects to deepen your understanding of computer vision concepts and applications.
## Contents
- **Case Studies:** Step-by-step walkthroughs of computer vision projects, including problem statements, approaches, code, and results.
- **Notebooks:** Jupyter notebooks demonstrating image processing, feature extraction, object detection, and more.
- **Datasets:** Sample datasets for experimentation and practice.
- **Reference Material:** Essential resources for building foundational knowledge.
## Reference Material
- [Hands-On Image Processing with Python](https://github.com/PacktPublishing/Hands-On-Image-Processing-with-Python)
A practical guide covering image processing techniques in Python, including filtering, transformations, and feature extraction.
- [Fundamentals of Image Processing (PDF)](./reference-material/fundamentals_of_image_processing.pdf)
A comprehensive document explaining core concepts, mathematical foundations, preprocessing methods, filtering, and feature extraction.
- [CNN Features off-the-shelf: an Astounding Baseline for Recognition](https://arxiv.org/pdf/1403.6382.pdf)
- [Explanatory Graphs for CNNs: which reveals the knowledge hierarchy hidden inside conv-layers of a pre-trained CNN](https://arxiv.org/pdf/1812.07997.pdf)
- [TensorFlow tutorials for image classification [Classification of images into their respective categories using Tensorflow]](https://www.tensorflow.org/tutorials/images/classification)
- [Transfer Learning using Tensorflow [Implementation of Transfer Learning using different pre-trained architectures in CNN]](https://www.tensorflow.org/tutorials/images/transfer_learning)
- [Data Augmentation using Tensorflow](https://www.tensorflow.org/tutorials/images/data_augmentation)
- [Leonardo Araujo Santos's gitbook explaining the CNNs [Indepth Mathematical Explanation about CNNs and different layers in CNNs]](https://leonardoaraujosantos.gitbook.io/artificial-inteligence/machine_learning/deep_learning)
## Getting Started
1. **Clone the repository:**
```bash
git clone https://github.com//computer-vision-case-studies.git
```
2. **Explore the notebooks:**
Open the `case-studies/` directory and start with the introductory examples.
## Contributing
Contributions are welcome! Please submit pull requests for new case studies, improved notebooks, or additional reference materials.
## 🧠License & Attribution
Some educational materials and slides in this repository are sourced from **[DeepLearning.AI](https://www.deeplearning.ai/)**
and are shared under the terms of the
**[Creative Commons Attribution-ShareAlike 2.0 License (CC BY-SA 2.0)](https://creativecommons.org/licenses/by-sa/2.0/legalcode)**.