https://github.com/alecruces/compvisionexploration
Traversing the landscape of computer vision, from foundational image processing to advanced deep learning architectures
https://github.com/alecruces/compvisionexploration
bag-of-words cnn computer-vision image-processing sift
Last synced: 8 months ago
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Traversing the landscape of computer vision, from foundational image processing to advanced deep learning architectures
- Host: GitHub
- URL: https://github.com/alecruces/compvisionexploration
- Owner: alecruces
- Created: 2024-04-08T19:08:12.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-10T10:56:09.000Z (about 2 years ago)
- Last Synced: 2025-04-14T12:54:08.910Z (about 1 year ago)
- Topics: bag-of-words, cnn, computer-vision, image-processing, sift
- Language: Jupyter Notebook
- Homepage:
- Size: 33.5 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Exploring Computer Vision: From Image Processing to Deep Learning
---
## Description
This project delves into computer vision and deep learning for image tasks. Utilizing tools such as OpenCV, TensorFlow, and PyTorch, image pre-processing, filtering, and transformation techniques are implemented. Scale Invariant Feature Transform (SIFT) algorithm is used for feature matching, and bag-of-visual-words for image classification. The project also involves experimentation with Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNNs) to tackle intricate image tasks.
## List of laboratories
* Lab 2: Computer Vision Foundations: Images and Filtering
* Lab 3: SIFT Algorithm
* Lab 4: Logo Contest: Visual Logo Recognition Contest
* Lab 5: Bag-of-visual-words for Image Classification
* Lab 6: Deep Neural Networks for Image Classification (Part I): Multilayer
Perceptron (MLP)
* Lab 7: Deep Neural Networks for Image Classification (Part II): Convolutional Neural Networks (CNNs)
## Keywords
Computer Vision, Image Processing, Deep Learning, Convolutional Neural Networks, Multilayer Perceptron, SIFT Algorithm, Bag-of-Visual-Words
## Data
* CIFAR10
* MNIST
## Methods
* Perceptron
* Feed Forward Network
* L1 and L2 Regularization
* Grid Search and Early Stopping
* Convolutional Neural Networks
* Recurrent Neural Networks (LSTM, GRU)
* Transformer
* Autoencoders (Shallow Linear and Non-linear Autoencoders, Deep *Autoencoder)
*Variational Autoencoders (VAE)
## Software
• OpenCV
• TensorFlow
• Pytorch
## Files
* Codes:
1. `Lab2_ImageFiltering.ipynb`: Computer Vision Foundations: Images and Filtering
2. `Lab3_SIFT.ipynb`: SIFT Algorithm
3. `Lab4_Logo_Contest.ipynb`: Logo Contest: Visual Logo Recognition Contest
4. `Lab5_BoW.ipynb`: Bag-of-visual-words for Image Classification
5. `Lab6_MLP.ipynb`: Deep Neural Networks for Image Classification (Part I): Multilayer
Perceptron (MLP)
6. `Lab7_CNN.ipynb`: Deep Neural Networks for Image Classification (Part II): Convolutional Neural Networks (CNNs)