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CVP Basic Exam QUESTIONS WITH Detailed Answer
Key For (2026) Exam
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.
pg. 1
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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
pg. 2
CVP Basic Exam QUESTIONS WITH Detailed Answer
Key For (2026) Exam
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.
pg. 1
, 2|Page
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
pg. 2
