What is Machine Vision? - ANSWER-It is the automatic acquisition and analysis of images to obtain desired data for controlling or evaluating a specific part or activity
Definition of Machine Vision - ANSWER-- Automated AND Non Contact ‐
- Acquisition AND Analysis
- Data/information delivery
- Technologies AND methods
- An engineering discipline
Benefits of Using Machine Vision - ANSWER-- Help eliminate dedicated mechanical solutions
- Provide flexibility in automated processes
- Help to improve quality, enable related technologies, and reduce costs
MV (as a set of methods) - What is Image Acquisition? - ANSWER-A critical part of machine vision that is required in order to achieve an image that can provide the information needed in the application.
What is Image Analysis? (Machine Vision - as a set of methods) - ANSWER-The overall process of extracting information from the image.
Includes tasks like pre processing, feature extraction, object segmentation, identification, measurement ‐
and more.
What is Data/Results Integration? (Information gained from the image....) - ANSWER-Making real world ‐
decisions about the information gained from the image. The link to the automation process "Machine Vision" or "Computer Vision" (definition, differences) - ANSWER-Computer vision most commonly refers to the use of AI techniques for classification of objects (e.g. neural networks and deep learning) to make computers "see" in a perceptive way that mimics humans; streaming video and continuous process
Machine vision most commonly refers to the use of discrete feature extraction and rule based ‐
comparisons to make decisions directly on image data, 1 1 relationship part to process ‐
"Machine Vision" vs "Computer Vision" (definition continued) - ANSWER-Machine vision uses a wide variety of tools including those that are most often considered exclusive to "computer vision" (deep learning for example) along with rule based or discrete feature extraction and analysis ‐
Machine vision is not necessarily a subset of computer vision and computer vision is not necessarily a subset of machine vision
In some cases, the capability of the tools described as rule based/discrete (machine vision) and learning ‐ ‐
based (computer vision) overlap and either might work well for a target application
MV Definition - "Inspect" - ANSWER-Check presence/absence, detect defects, verify assembly, differentiate colors, count objects
MV Definition - "Locate/Guide" - ANSWER-Find randomly oriented features or object is 2D and 3D space,
perhaps provide real-world coordinates for robotic or motion guidance
MV Definition - "Measure" - ANSWER-Precisely measure objects or features in both 2D and 3D space.
MV Definition - "Identify/Sort" - ANSWER-Differentiate closely related objects or features, read codes and print, sort/count objects based on size, color or other features.
What is the Key to Success (understanding MV Market & Industry) [goal of CVP] - ANSWER-Being able to competently specify and implement the technology
True/False - Industrial PC or Embedded PC Based Systems include "Smart Cameras" - ANSWER-False
ASMV - ANSWER-Application Specific Machine Vision