What is computer vision, and where is it used?

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Computer Vision (CV) is a field of Artificial Intelligence (AI) that enables machines to see, interpret, and understand visual information from the world—such as images, videos, and live camera feeds—similar to how humans use their eyes and brain. It uses techniques from image processing, machine learning, and deep learning to recognize patterns, detect objects, and make decisions based on visual input.

🔹 How Computer Vision Works

1. Image Acquisition – Capturing images/videos from cameras or sensors.

2. Preprocessing – Enhancing images (resizing, noise reduction, color adjustment).

3. Feature Extraction – Identifying shapes, edges, or patterns.

4. Recognition/Detection – Using AI models (CNNs, vision transformers) to classify objects, detect faces, or track motion.

5. Decision Making – Producing meaningful output (e.g., "This is a cat" or "Car detected on the road").

🔹 Where Computer Vision is Used

1. Healthcare – Diagnosing diseases from X-rays, MRIs, CT scans.

2. Autonomous Vehicles – Self-driving cars detecting pedestrians, traffic lights, and other vehicles.

3. Security & Surveillance – Facial recognition, anomaly detection in CCTV footage.

4. Retail & E-commerce – Virtual try-ons, product search by image.

5. Manufacturing – Quality inspection and defect detection in factories.

6. Agriculture – Monitoring crop health, detecting pests using drone images.

7. Sports Analytics – Tracking players, analyzing game strategies.

8. Social Media & Apps – Photo tagging (Facebook), filters (Instagram, Snapchat).

9. Robotics – Helping robots navigate and interact with objects.

10. Augmented Reality (AR) & Virtual Reality (VR) – Enabling immersive experiences with real-world tracking.

✅ In short:
Computer Vision = teaching machines to see and understand the world visually.
It powers technologies from medical imaging to self-driving cars and everyday apps like face unlock.

Read More:

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What is Natural Language Processing (NLP)? Give examples.

Explain the difference between classification and regression problems.

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