Computer Vision is a subfield of Deep Learning and Artificial Intelligence where humans teach computers to see and interpret the world around them. Machine vision is the use of a camera or multiple cameras to inspect and analyze objects automatically, usually in an industrial or production environment. The overall machine vision process includes planning the details of the requirements and project and then creating a solution. During run-time, the process starts with imaging, followed by automated analysis of the image and extraction of the required information. It integrates existing technologies in new ways and applies them to solve real-world problems. For instance, used in other industries such as security, autonomous vehicles, food production, packaging, and logistics while also being included in robots and drones.

• Machine Vision vs. human vision
• Four main vision system components
• How does the Machine Vision work?
• Some of the common Machine Vision tasks
• Applications of Machine Vision

Machine Vision vs. human vision

Digital Image Processing, or Image Processing, in short, is a subset of Machin Vision. It deals with enhancing and understanding images through various algorithms.

Image processing is used in the early machine vision stages for tasks such as filtering (Neighborhood (Spatial) Processing), Segmentation, Edge Detection, and Geometric Operations.

 Not all image processing algorithms are typically used in machine vision systems. Examples of image processing algorithms that are of secondary concern to machine vision include de-blurring, image stitching, and image and video compression. 

Four main vision system components

Lenses and lighting, the image sensor or camera, the processor, and a method of communicating results, whether by physical input/output (I/O) connections or through other communications, are the four main parts of a vision system.

The lens captures the image and presents it to the sensor in the form of light. To optimize the vision system, the camera needs to be matched with the appropriate lens. Although there are many types of lenses, machine vision applications typically use a lens with a fixed focal length.

How does Machine Vision work?

The answer to this question is as follows on the most basic level:

1. Acquiring an image: Images, even large sets, can be acquired in real-time through video, photos, or 3D technology for analysis.
2. Processing the image: Deep Learning models automate much of this process, but the models are often trained by first being fed a thousand labeled or pre-identified images. 
3. Understanding he image: The final step is the interpretative step, where an object is identified or classified.

While the three steps outlining the basics of computer vision seem easy, processing and understanding an image via machine vision are quite difficult. For instance, the process begins when a sensor detects the presence of a product. The sensor then triggers a light source to illuminate the area and a camera to capture an image of the product or a component of the product. The frame-grabber (a digitizing device) translates the camera’s image into digital output. The digital file is saved on a computer so it can be analyzed by the system software. The software compares the file against a set of predetermined criteria to identify defects. If a defect is identified, the product will fail inspection.

Some of the common Machine Vision tasks

The evolution of machine vision saw the large-scale formalization of difficult problems into popular solvable problem statements. Division of topics into well-formed groups with proper nomenclature helped researchers around the globe identify problems and work on them efficiently. The most popular computer vision tasks that we regularly find in AI jargon include:

• Image classification
• Object detection
• Image segmentation
• Face and person recognition
• Edge detection
• Image restoration
• Feature matching

Applications of Machine Vision

Machine vision is used in various industrial and medical applications. Examples include: Electronic component analysis - Signature identification - Optical character recognition - Handwriting recognition - Object recognition - Pattern recognition - Materials inspection - Currency inspection- Medical image analysis. Also, Vision systems are very common sensory input to robotic and automation systems for example:

• In factories, they can be used for simple, but important, applications such as detecting the presence of a product at the desired location.
• Perhaps the most common application of machine vision is for quality control inspections.
• Robotic systems can use vision as input to their guidance system, not only to avoid obstacles but also for Visual Serving where the vision system guides the robot towards a goal.