Machine vision

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Machine vision is a field of technology that enables computers, robots, and automated systems to interpret and understand visual information captured from cameras and other imaging sensors. It encompasses the full pipeline from image acquisition and calibration to feature extraction, object recognition, depth estimation, and scene understanding. In robotics and AI, machine vision is used across an enormous range of applications: guiding industrial robot arms on assembly lines, enabling autonomous vehicles and agricultural robots to navigate complex environments, detecting and localizing objects for harvesting or inspection, and performing quality control in manufacturing and food processing. Techniques range from classical geometric methods and stereo vision to modern deep learning approaches such as Mask R-CNN, and sensors continue to evolve from standard frame cameras to event-based and neuromorphic devices. Machine vision matters because it serves as the primary perceptual bridge between robots and the physical world, providing the spatial and semantic awareness necessary for autonomous decision-making, precise manipulation, and safe operation in both structured industrial settings and unpredictable real-world environments.

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