CVP BASIC EXAM 2026 FINAL STUDY
MATERIALS INCLUDING ALL QUESTIONS
AND FULL SOLUTIONS GRADED A+
⫸ Definition of Machine Vision Ans: - Automated AND Non‐Contact
- Acquisition AND Analysis
- Data/information delivery
- Technologies AND methods
- An engineering discipline
⫸ Benefits of Using Machine Vision Ans: - 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? Ans: 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)
Ans: 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....) Ans: Making real‐world decisions about the information
gained from the image. The link to the automation process
⫸ "Machine Vision" or "Computer Vision" (definition, differences)
Ans: 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)
Ans: 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
MATERIALS INCLUDING ALL QUESTIONS
AND FULL SOLUTIONS GRADED A+
⫸ Definition of Machine Vision Ans: - Automated AND Non‐Contact
- Acquisition AND Analysis
- Data/information delivery
- Technologies AND methods
- An engineering discipline
⫸ Benefits of Using Machine Vision Ans: - 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? Ans: 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)
Ans: 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....) Ans: Making real‐world decisions about the information
gained from the image. The link to the automation process
⫸ "Machine Vision" or "Computer Vision" (definition, differences)
Ans: 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)
Ans: 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
