Computer vision is a field of artificial intelligence (AI) that focuses on enabling machines to understand and interpret visual information, such as images and videos, similar to how humans do. The goal of computer vision is to allow machines to recognize objects, detect patterns, and analyze scenes, which can then be used to make decisions or perform tasks. For example, in image classification, computer vision models can identify the contents of an image, such as distinguishing between a cat and a dog. Another application is object detection, where the system identifies and locates objects in an image, such as recognizing and marking the location of pedestrians in a self-driving car’s camera feed. Facial recognition is another well-known use of computer vision, where systems can identify or verify a person’s identity based on facial features. Overall, computer vision leverages algorithms like convolutional neural networks (CNNs) to process and understand visual data, making it an essential tool in applications across healthcare, automotive, and security sectors.
What is computer vision?

- Retrieval Augmented Generation (RAG) 101
- The Definitive Guide to Building RAG Apps with LangChain
- AI & Machine Learning
- Natural Language Processing (NLP) Basics
- Exploring Vector Database Use Cases
- 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
Is AutoML suitable for small datasets?
AutoML can be suitable for small datasets, but there are several factors to consider when determining its effectiveness.
What is knowledge distillation and how can it help optimize embedding models?
Knowledge distillation is a technique where a smaller, more efficient model (called the "student") is trained to mimic t
What are the ethical challenges with few-shot and zero-shot learning?
Few-shot and zero-shot learning are techniques designed to train machine learning models with minimal labeled data. Whil