Glossary of machine vision

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Glossary of Machine Vision

Machine vision is a field of computer science that focuses on enabling computers to see, identify, and process images in the same way that human vision does, and then provide the appropriate output. It is closely related to computational intelligence, computer vision, and artificial intelligence. This glossary provides definitions for common terms used in the field of machine vision.

A

• Algorithm: A set of rules or instructions designed to perform a specific task or solve a specific problem. In machine vision, algorithms analyze the images captured by cameras to detect, classify, and interpret visual information.
• Artificial Intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems. AI applications in machine vision include pattern recognition, object identification, and decision-making.

B

• Binary Image: An image consisting only of black and white pixels, commonly used in machine vision for simplifying the analysis by reducing the complexity of the image.
• Blob Analysis: A process in machine vision used to identify and characterize different regions (blobs) in a binary image.

C

• CCD (Charge-Coupled Device): A type of image sensor used in digital cameras and machine vision systems to capture images.
• CMOS (Complementary Metal-Oxide-Semiconductor): Another type of image sensor found in digital cameras and machine vision systems, known for lower power consumption compared to CCD sensors.
• Computer Vision: A field of study that focuses on enabling computers to interpret and understand visual information from the world around them, as humans do.

D

• Deep Learning: A subset of machine learning that uses algorithms inspired by the structure and function of the brain's neural networks to learn from large amounts of data. Deep learning is increasingly used in machine vision for complex tasks like image recognition.

E

• Edge Detection: A technique used in machine vision to identify the boundaries of objects within images. It is a crucial step in many applications, including object detection and scene interpretation.

F

• Feature Extraction: The process of identifying and isolating various desired characteristics of an image, such as edges, corners, or blobs, to simplify the process of image analysis.

G

• Grayscale Image: An image in which each pixel represents a shade of gray, as opposed to a color image. Grayscale images are often used in machine vision because they simplify the analysis by reducing the amount of information in the image.

H

• Histogram: A graphical representation of the distribution of pixel intensities in an image. Histograms are used in machine vision for tasks such as image thresholding and contrast adjustment.

I

• Image Processing: The technique of performing operations on images to enhance them or extract information. Image processing is a fundamental aspect of machine vision.

L

• Learning Algorithm: An algorithm that improves its performance at some task over time with experience. Learning algorithms are central to machine vision systems that adapt and improve their accuracy.

M

• Machine Learning: A subset of AI that involves the development of algorithms that allow computers to learn from and make decisions based on data. Machine learning is a critical component of advanced machine vision systems.

O

• Object Recognition: The ability of a machine vision system to identify and classify objects within an image or a sequence of images.

P

• Pixel: The smallest unit of an image that can be displayed and processed on a digital display device. Pixels are the basic building blocks of digital images.

R

• Resolution: The detail an image holds, typically measured in pixels for digital images. Higher resolution means more image detail.

S

• Sensor: A device that detects and responds to some type of input from the physical environment. In machine vision, sensors, such as CCD or CMOS, capture images to be analyzed.

T

• Template Matching: A technique in machine vision where a pre-defined template or shape is compared with segments of an image to locate instances of the template within the image.

V

• Vision System: A system that uses a combination of hardware (cameras, sensors) and software (algorithms, AI) to capture and analyze images for tasks such as inspection, identification, and guidance.