Computer vision is a field of computer science focused on enabling machines to interpret and understand visual information from the world. This involves processing and analyzing images or video to extract meaningful data such as objects, depth, motion, and patterns. Computer vision systems use algorithms and models to simulate human visual perception, which can be applied in numerous industries. Common applications include face recognition, where algorithms identify individuals based on their facial features, and object detection, which locates and classifies objects in images or videos, commonly used in surveillance or autonomous vehicles. Medical imaging is another application, where computer vision helps in detecting abnormalities such as tumors or fractures in X-ray or MRI scans. In manufacturing, computer vision is used for quality control, inspecting products on assembly lines for defects. The primary goal is to automate tasks that traditionally required human visual interpretation, improving accuracy, efficiency, and decision-making in various sectors.
What is computer vision and its application?

- How to Pick the Right Vector Database for Your Use Case
- Retrieval Augmented Generation (RAG) 101
- Embedding 101
- Getting Started with Zilliz Cloud
- AI & Machine Learning
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
Can data augmentation create bias in models?
Yes, data augmentation can create bias in models, even though its primary purpose is to improve model performance and ge
How would you compare a system that uses a smaller but highly relevant private knowledge base to one that searches a broad corpus like the entire web? (Consider answer accuracy, trustworthiness, and response time.)
When comparing a system using a smaller, highly relevant private knowledge base to one that searches a broad corpus like
How do organizations handle failover in disaster recovery?
Organizations handle failover in disaster recovery by establishing redundant systems and processes that kick in when pri