¿Cómo las computadoras entienden las imágenes?

Artificial intelligence and computer vision are revolutionizing the way we interact with technology. From creative applications to industrial solutions, these tools allow us to develop innovative projects with accessible resources. Discover how to implement computer vision systems and create interactive experiences using Python and popular libraries.

How do machine vision systems work?

The basis of computer vision is to convert images into numerical representations that computers can process. This process uses three-dimensional arrays to represent RGB colors and allows:

• Detect minutiae of the face, body and hands.
• Track movements in real time.
• Analyze specific patterns and features in images.

What tools do we need to get started?

The main libraries to develop computer vision projects are:

• OpenCV: Library specialized in computer vision.
• MediaPipe: Allows to detect characteristic points of the face, body and hands.
• Python: Base programming language.

Hardware requirements are minimal:

• Computer with i5 processor or similar.
• Integrated or external webcam.
• No dedicated GPU is required.

What are the applications of machine vision in industry?

Commerce and retail

• Behavioral analysis: Generates heat maps to study customer movement in stores.
• Autonomous systems: Implementation in grab and go stores for product tracking.
• Neuromarketing: Visual attention studies in products and advertising.

Medicine and health

• Preventive diagnosis: Analysis of medical images such as X-rays and CT scans.
• Early detection: Identification of pathologies in early stages.
• Monitoring: Patient tracking and movement analysis.

Security and surveillance

• CCTV monitoring: Real-time video analysis.
• Event detection: Automatic identification of specific situations.
• Access control: Face and body validation.

How to create interactive applications with artificial vision?

To develop creative projects such as drawing in the air, it is required:

1. Initial configuration:

   • Installation of necessary libraries.
   • Camera configuration.

2. Image processing:

   • Real-time frame capture.
   • Characteristic points detection.

3. Implementation of functionalities:

   • Motion tracking.
   • Generation of visual responses.

Machine vision opens up a world of possibilities for developing innovative solutions in various fields. From creative applications to complex industrial systems, this technology is transforming the way we interact with the digital world. What project would you like to develop using these tools? Share your ideas in the comments.