Artificial Intelligence | Class 10th Part B: Unit 5: Computer Vision | NCERT syllabus | #cbse

Introduction to Computer Vision
Computer Vision is a field of Artificial Intelligence that enables computers to see, analyze, and understand images and videos like humans do.
It involves training machines to detect objects, recognize patterns, and make decisions based on visual inputs.
For example, when you use Google Lens to scan a plant and get its name, you're using computer vision in action.
Even before AI became common, computers were already used to process images.
Now, with AI and machine learning, these image-processing techniques have become much smarter.
The main goal of computer vision is to recognize, classify, and understand objects or patterns in visual data
For example, identifying a person’s face, reading a number plate, or detecting a road sign.
Applications of Computer Vision
Computer Vision is widely used in many fields. let’s explore how it's used in real life across different fields — especially when combined with Machine Learning (ML).

Facial Recognition
Facial recognition is a popular use of computer vision. It helps unlock phones, laptops, and even smart doors by recognizing a person’s face. In law enforcement, it’s used to find missing people or identify suspects. At airports, it helps match a passenger’s face with their ID or passport. In smart homes, cameras can tell if the person at the door is a family member or a stranger.
Document Verification
In banks, airports, and government offices, computer vision helps scan and verify documents like passports, ID cards, or property papers. For example, when applying for a passport online, the system uses CV to check if the photo and signature match the records. This speeds up processes, reduces fraud, and improves accuracy.
Medical Diagnosis
In hospitals, computer vision helps doctors detect diseases early. For instance, it can analyze X-rays, MRI scans, or blood images to find signs of tumours, fractures, or infections. During surgery, it can even monitor blood loss in real time. This saves lives by making diagnosis faster and more accurate.


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