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Computer Vision

Computer Vision 🖼️

The computer vision section explores techniques and technologies for enabling machines to interpret and understand visual information from the world. This area involves:

  • Image Processing: Techniques for manipulating and analyzing images, including filtering, transformation, and feature extraction.
  • Object Detection & Recognition: Methods for identifying and locating objects within images or video streams, utilizing algorithms like YOLO, SSD, and Faster R-CNN.
  • Image Classification: Using machine learning models to classify images into categories, with frameworks like TensorFlow and PyTorch.
  • Feature Matching: Techniques for finding corresponding features between images, useful in applications like panorama stitching and 3D reconstruction.
  • Deep Learning for Vision: Leveraging convolutional neural networks (CNNs) and other deep learning architectures to tackle complex vision tasks.
  • Applications: Practical uses of computer vision in areas like augmented reality, facial recognition, autonomous vehicles, and medical imaging.

This section includes hands-on projects, code implementations, and research papers that demonstrate the application of computer vision techniques in real-world scenarios.