What is Computer Vision

In simple terms, computer vision teaches computers to interpret what they see through cameras or images. As advances in AI continue to improve the accuracy and speed of computer vision systems, they are becoming increasingly useful in solving real-world problems, automating tasks, and supporting better decision-making across many industries.

Retail businesses use computer vision for automated checkout systems and inventory management, while manufacturers use it to inspect products for defects. It is also widely used in facial recognition systems, security surveillance, agriculture, robotics, and optical character recognition (OCR), which converts printed or handwritten text into digital text.

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How computer vision works

A typical computer vision system follows these steps:

  • 1. Capture an image or video using a camera or other imaging device.
  • 2. Preprocess the image by resizing, enhancing, or reducing noise.
  • 3. Extract features or use a deep learning model to identify important patterns.
  • 4. Analyze the image to recognize objects, faces, text, or actions.
  • 5. Produce a result, such as labeling objects or making a decision.

Today, most computer vision systems use deep learning, especially convolutional neural networks (CNNs) and Vision Transformers (ViTs), to achieve high accuracy.

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Tools and Framework

Some popular tools include:

  • - OpenCV for image processing
  • - PyTorch for deep learning
  • - TensorFlow for machine learning
  • - YOLO (You Only Look Once) for real-time object detection
  • - Detectron2 for object detection and segmentation
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