A machine vision system is a set of hardware and software designed to allow a computer or robot to "see" and interpret the visual world, much like how humans use their eyes. These systems typically consist of a camera, lens, lighting, and processing hardware or software. The camera captures images or video frames, which are then processed by the software to extract useful information. This can involve tasks such as object recognition, image segmentation, motion tracking, or pattern recognition. For example, in industrial automation, a machine vision system might use cameras to inspect products on a production line, check for defects, and verify dimensions. It can also help robots navigate and manipulate objects in environments like warehouses. Machine vision systems are used in a variety of industries, from automotive manufacturing (where they ensure parts are correctly assembled) to agriculture (where they help in crop monitoring and sorting). By automating visual tasks, machine vision systems can increase efficiency, reduce errors, and enhance the overall performance of machines and robots in various applications.
What is a machine vision system?
- Evaluating Your RAG Applications: Methods and Metrics
- AI & Machine Learning
- Natural Language Processing (NLP) Basics
- Exploring Vector Database Use Cases
- Mastering Audio AI
- 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
What could cause DeepResearch to be unable to access certain content or to provide only incomplete results?
DeepResearch might fail to access content or deliver incomplete results due to three primary categories of issues: techn
What are the differences between traditional and modern predictive analytics?
Traditional predictive analytics and modern predictive analytics differ primarily in their methods, tools, and scope of
How do organizations align data governance with business goals?
Organizations align data governance with business goals by establishing clear frameworks that integrate data policy with