Pattern recognition is a key component of computer vision, where algorithms are trained to detect and identify patterns or regularities in visual data. In the context of computer vision, pattern recognition involves analyzing images or video frames to identify shapes, textures, or specific objects. This process can include tasks such as face recognition, where a system identifies individuals based on facial features, or optical character recognition (OCR), which converts handwritten or printed text into machine-readable data. Pattern recognition systems use machine learning techniques, such as support vector machines (SVMs) or neural networks, to train on large datasets and improve the accuracy of pattern identification over time. One example is in autonomous driving, where computer vision systems recognize road signs, pedestrians, and other vehicles based on learned patterns from data collected through cameras. In medical imaging, pattern recognition can be used to detect patterns in scans, such as identifying early signs of disease. Thus, pattern recognition provides the framework within which computer vision algorithms can perform more complex tasks like object detection, facial recognition, or medical diagnostics.
What is Computer Vision and pattern recognition?
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