Image Processing
Published:
A comprehensive collection of hands-on projects in image processing, implemented in Python with Jupyter notebooks.
Sessions Overview
| Session | Topic | Key Techniques |
|---|---|---|
| BE 1 | Thresholding & Morphology | Binary segmentation, morphological operators, image cleanup |
| BE 2 | Geometric Transformations | Forward/backward mapping, coordinate systems, interpolation methods |
| BE 3 | Feature Detection | Harris corner detection, scale-space blob detection, feature matching |
| BE 4 | Bag of Visual Words | SIFT, KMeans clustering, TF-IDF, spatial pyramid pooling, image classification |
Key Concepts
- Image Segmentation: thresholding strategies, morphological operations, region-growing algorithms
- Geometric Vision: coordinate transformations, image warping with bilinear interpolation, evaluation metrics (MSE, PSNR, SSIM)
- Feature Extraction: Harris corner response, eigenvalue analysis, scale-invariant keypoint detection
- Visual Recognition: building BoVW pipelines from scratch, vocabulary learning, similarity-based retrieval, SVM classification
Quick Start
Requirements:
- Python 3.8+
- See
requirements.txtor install:
pip install numpy matplotlib scikit-image opencv-python scikit-learn scipy pandas jupyter
Run the notebooks:
Open any notebook in BE_session_*/ and execute cells top-to-bottom.
Repository Structure
.
├── BE_session_1/
│ ├── BE-Thresholding-Morphology-Student.ipynb
│ ├── Images/
│ └── defects/
├── BE_session_2/
│ ├── TD2_login1_login_2.ipynb
│ ├── parrot.jpg
│ └── ground_truth.npy
├── BE_session_3/
│ ├── BE3_login1_login_2.ipynb
│ └── [test images]
├── BE_session_4/
│ ├── BE4_BoVW_student1_student2.ipynb
│ └── TD4-Student/data-BE4/
│ ├── breastmnist_128.npz
│ └── SUN/ (10-class scene subset)
└── README.md
Technologies Used
- Image Processing: scikit-image, OpenCV (cv2)
- Numerical Computing: NumPy, SciPy
- Machine Learning: scikit-learn (KMeans, SVM)
- Visualization: Matplotlib
- Development: Jupyter Notebooks