Computer Vision Techniques Overview 📸🔍

Computer vision is a dynamic field with numerous techniques that enable machines to interpret and understand visual data. Here's a concise guide to key methods:

1. Image Classification

Classifying images into predefined categories (e.g., "cat", "dog").

This technique forms the foundation for many applications, such as content moderation and object recognition.

2. Object Detection

Identifying and locating multiple objects within an image.

Common frameworks include YOLO and Faster R-CNN. For deeper insights, check our [Computer Vision Tutorial](/en/tutorials/computer-vision).

3. Semantic Segmentation

Labeling each pixel in an image to identify objects and their boundaries.

Used in autonomous driving and medical imaging.

4. Pose Estimation

Detecting key points on objects or humans to determine their spatial orientation.

Popular for motion analysis and augmented reality.

5. Image Generation

Creating new images using techniques like GANs (Generative Adversarial Networks).

Explore our [GANs Guide](/en/guides/generative-adversarial-networks) for hands-on examples.

6. Feature Extraction

Identifying distinctive elements (e.g., edges, textures) in images.

Crucial for tasks like image retrieval and pattern recognition.

For further learning, visit our Computer Vision Resources page to explore tools, datasets, and advanced topics. 🌐📚