Computer Vision VS Machine Vision
Computer vision and machine vision are related technologies that allow computers to “see” and analyze visual data such as images and videos. In this article, we’ll discuss difference between Computer Vision vs Machine Vision.
What is Computer Vision?
Computer vision is a scientific field that enables computers to derive meaningful information from digital images, videos and other visual inputs – and take actions based on that information. The main focus of computer vision is to automate the tasks that the human visual system can perform.
Some examples of computer vision applications are given below:
- Image classification – labeling images based on their visual content. For example, determining whether an image contains a dog or cat.
- Object detection – locating instances of objects within images. For example, finding faces in a photo.
- Image segmentation – dividing images into meaningful parts. For example, identifying individual objects in an image.
- Activity recognition – understanding motions and actions from video sequences. For example, distinguishing walking from running.
- Scene reconstruction – rebuilding 3D environments from 2D images.
Computer vision utilizes the concepts from fields like machine learning, deep learning, artificial intelligence, statistics and geometry to develop algorithms and statistical models that can process visual data and perform these complex tasks automatically. The output of a computer vision system is typically a prediction, classification or quantitative analysis based on the visual inputs.
What is Machine Vision?
Machine vision refers to industrial applications of computer vision technologies to automate manufacturing processes and quality control. The computer vision focuses on automating a wide range of human visual capabilities, whereas, machine vision is concerned with solving specialized vision tasks in industrial use cases.
The goal of machine vision is to provide reliable, fast and cost-effective inspection for manufacturing.
Some examples of machine vision applications are:
- Visual inspection – detecting defects and anomalies in manufactured products through image analysis. For example, checking for scratches or missing components.
- Optical character recognition (OCR) – reading text on objects, components and documentation during manufacturing.
- Barcode scanning – quickly identifying items and tracking inventory.
- Robot guidance – enabling machines and robots to navigate their environment using visual sensors.
- Alignment & measurement – precisely positioning and measuring parts to ensure proper assembly.
Machine vision systems utilize standardized cameras, lighting rigs, optics and sensors specifically calibrated for industrial environments. Dedicated machine vision software and tools are used to develop and deploy inspection solutions tailored to the manufacturing process requirements.
The output of a machine vision system is pass/fail or measurement data to immediately screen out defective parts or guide automated assembly based on the visual inspection. Machine vision provides the visual quality control needed for automated manufacturing processes to function efficiently.
Computer Vision VS Machine Vision
This table shows head-to-head comparison.
| Parameter | Computer Vision | Machine Vision |
|---|---|---|
| Purpose | Automate wide range of human visual capabilities | Automated industrial inspection for manufacturing |
| Applications | Consumer products, medical imaging, autonomous vehicles, drones, AR/VR | Automated manufacturing, product inspection, barcode reading, robot guidance |
| Environments | Varying, uncontrolled real-world environments | Controlled manufacturing environments optimized for inspection |
| Imaging Conditions | Uncontrolled lighting, angles, environments | Consistent lighting, positioning and camera setups |
| Speed | Prioritizes accuracy in decision making | Emphasizes speed for manufacturing throughput |
| Integration | Provides analysis and recommendations as output | Integrated into manufacturing automation equipment |
| Customization | Requires large datasets and training | Customized solutions for each use case |
| Advantages | Flexible, can automate complex visual tasks | Fast, reliable, cost-effective for manufacturing |
| Examples | Facial recognition, self-driving cars | Detecting defects in products, guiding robots |
| Implementation | Deep learning models trained on images/video | Dedicated hardware and software for factory setup |
Key Differences
- Purpose – Computer vision aims to automate a wide range of human visual capabilities. Machine vision focuses on solving specialized industrial inspection tasks.
- Applications – Computer vision powers consumer products, medical imaging, autonomous vehicles, drones, augmented reality and more. Machine vision enables automated manufacturing, product inspection, barcode reading, robot guidance, etc.
- Environments – Computer vision systems are designed to work under varying conditions like a self-driving car navigating real-world environments. Machine vision operates in controlled manufacturing settings tailored for inspection tasks.
- Imaging conditions – Computer vision algorithms have to process images and video captured in uncontrolled lighting, angles and environments. Machine vision benefits from consistent lighting, positioning, and camera setups optimized for the products being inspected.
- Speed – Computer vision prioritizes accuracy in decision making from visual inputs. Machine vision emphasizes speed, performing fast pass/fail analysis to meet manufacturing throughput targets.
- Integration – Computer vision systems provide analysis and recommendations as output. Machine vision seamlessly integrates into manufacturing setups, directly powering automation equipment without human intervention.
- Customization – Computer vision models require large datasets and significant training to master visual processing tasks. Machine vision solutions are precisely customized to each use case based on the product geometry, features of interest, defects, etc.
Final Thoughts
In short, computer vision and machine vision leverage similar technology foundations like deep learning and image processing algorithms. But computer vision takes a general approach for automating a wide range of human visual capabilities, while machine vision optimizes computer vision technology for specialized manufacturing inspection and automation applications.
