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CVP BASIC EXAM QUEST IONS &
ANSWERS
Question 1: What is Machine Vision?
CORRECT ANSWER: It is the automatic acquisition and
analysis of images to obtain desired data for controlling or evaluating a specific part or activity
Question 2: Definition of Machine Vision
CORRECT ANSWER: - Automated AND Non‐Contact -
Acquisition AND Analysis - Data/information delivery - Technologies AND methods - An engineering discipline
Question 3: Benefits of Using Machine Vision
CORRECT ANSWER: - Help eliminate dedicated
mechanical solutions - Provide flexibility in automated processes - Help to improve quality, enable related technologies, and reduce costs
Question 4: MV (as a set of methods) - What is Image
Acquisition?
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CORRECT 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.
Question 5: What is Image Analysis? (Machine Vision -
as a set of methods)
CORRECT ANSWER: The overall process of extracting
information from the image. Includes tasks like pre‐ processing, feature extraction, object segmentation, identification, measurement and more.
Question 6: What is Data/Results Integration?
(Information gained from the image....)
CORRECT ANSWER: Making real‐world decisions
about the information gained from the image. The link to the automation process
Question 7: "Machine Vision" or "Computer Vision"
(definition, differences)
CORRECT ANSWER: Computer vision most commonly
refers to the use of AI techniques for classification of
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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
Question 8: "Machine Vision" vs "Computer Vision"
(definition continued)
CORRECT 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
Question 9: MV Definition - "Inspect"
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CORRECT ANSWER: Check presence/absence, detect
defects, verify assembly, differentiate colors, count objects
Question 10: MV Definition - "Locate/Guide"
CORRECT ANSWER: Find randomly oriented features
or object in 2D and 3D space, perhaps provide real-world coordinates for robotic or motion guidance
Question 11: MV Definition - "Measure"
CORRECT ANSWER: Precisely measure objects or
features in both 2D and 3D space.
Question 12: MV Definition - "Identify/Sort"
CORRECT ANSWER: Differentiate closely related
objects or features, read codes and print, sort/count objects based on size, color or other features.
Question 13: What is the Key to Success (understanding
MV Market & Industry) [goal of CVP]