Definition of machine vision

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Photonics Dictionary

machine vision: Machine vision, also known as computer vision or computer sight, refers to the technology that enables machines, typically computers, to interpret and understand visual information from the world, much like the human visual system. It involves the development and application of algorithms and systems that allow machines to acquire, process, analyze, and make decisions based on visual data.

Key aspects of machine vision include:

Image acquisition: Machine vision systems use various devices, such as cameras or sensors, to capture visual information from the environment. The acquired images serve as input data for further analysis.

Image processing: The captured images undergo preprocessing and enhancement to improve their quality and highlight relevant features. Image processing techniques include filtering, noise reduction, and image correction.

Feature extraction: Machine vision algorithms identify and extract relevant features from the processed images. These features can include shapes, patterns, textures, colors, or other characteristics that are crucial for the analysis.

Pattern recognition: Machine vision systems use pattern recognition algorithms to interpret and recognize specific objects, shapes, or patterns within the images. This involves comparing extracted features to predefined templates or learning patterns through machine learning techniques.

Decision making: Based on the analysis of visual data, machine vision systems make decisions or take actions. This may involve sorting objects, guiding robotic movements, detecting defects in manufacturing processes, or providing information for autonomous vehicles.

Applications:

Manufacturing: Machine vision is extensively used in quality control, defect detection, and automation in manufacturing processes.

Medical imaging: It plays a crucial role in medical diagnostics, including image analysis for disease detection and surgical assistance.

Autonomous vehicles: Machine vision is a key component in the development of self-driving cars and other autonomous systems.

Security and surveillance: Machine vision is applied in video surveillance for the detection of anomalies or suspicious activities.

Augmented reality: Machine vision is employed in applications that overlay digital information onto the real-world environment.

Challenges:

Machine vision faces challenges such as handling variations in lighting conditions, occlusions, and the need for robust algorithms capable of adapting to diverse visual scenarios.