Machine vision (MV) is a technology used for automatic inspection and analysis in industrial applications, emphasizing automatic inspection and robot guidance. Computer vision involves acquiring and analyzing images to produce decisions, encompassing broader applications like object detection and facial recognition. Both fields are intertwined, with MV focusing on specific industrial uses of computer vision, and advancements in tools have made these technologies more accessible for varied applications.

1. What is Machine Vision
• Machine vision (MV) is the technology and
methods used to provide imaging-based
automatic inspection and analysis for such
applications as automatic inspection, process
control, and robot guidance in industry.
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2. Applications of Machine Vision
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o Automated Train Examiner (ATEx) Systems
o Automatic PCB inspection
o Wood quality inspection
o Final inspection of sub-assemblies
o Engine part inspection
o Label inspection on products
o Checking medical devices for defects
o Final inspection cells
o Robot guidance and
o checking orientation of components
o Packaging Inspection
o Medical vial inspection
o Food pack checks
The primary uses for machine vision are automatic inspection and industrial
robot guidance. Other machine vision applications include:
o Verifying engineered components
o Wafer Dicing
o Reading of Serial Numbers
o Inspection of Saw Blades
o Inspection of Ball Grid Arrays (BGAs)
o Surface Inspection
o Measuring of Spark Plugs
o Molding Flash Detection
o Inspection of Punched Sheets
o 3D Plane Reconstruction with Stereo
o Pose Verification of Resistors
o Classification of Non-Woven Fabrics

3. Application of Machine Vision
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4. What is Computer Vision
• Computer vision is a field that includes methods
for acquiring, processing, analyzing, and
understanding images and, in general, high-
dimensional data from the real world in order to
produce numerical or symbolic information,
e.g., in the forms of decisions.
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5. Why Learn Computer Vision?
o Computer vision is built upon the fields of mathematics, physics, biology,
engineering, and of course, computer science.
o There are many fields related to computer vision, such as machine
learning, signal processing, robotics, and artificial intelligence.
o Even though it is a field built on advanced concepts, more and more
tools are making it accessible to everyone from hobbyists to vision
engineers to academic researchers.
o It is an exciting time in this field, and there are an endless number of
possibilities for applications.
o One of the things that makes it exciting is that these days, the hardware
requirements are inexpensive enough to allow more casual developers
entry into the field, opening the door to many new applications and
innovations. 5

6. Applications of Computer Vision?
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Object Detection/Classification
People Tracking (Airport Security) Entertainment (Kinect)
Film Industry (visual FX)
Facial Recognition
Traffic Monitoring
Vehicle Detection

7. Difference between Computer Vision and
Machine Vision
Computer vision and machine vision are different terms for
overlapping technologies.
o Computer vision refers in broad terms to the capture and automation of
image analysis with an emphasis on the image analysis function across a
wide range of theoretical and practical applications.
o Machine vision traditionally refers to the use of computer vision in an
industrial or practical application or process where it is necessary to
execute a certain function or outcome based on the image analysis done
by the vision system.

8. If you would like to Learn More
on Image Processing Course
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Course Description
Learn the basic concepts, tools, and functions that you will need to build
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• Together we will build a strong foundation in Image Processing with
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• LabVIEW Vision Development Toolkit Download and Installation
• Basic Feature Detection
• Circle, Color and Edge Detection Algorithms
• Advance Feature Detection - Pattern Matching, Object Tracking, OCR,
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9. If you would like to Learn More
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Starting with the installation of the LabVIEW Vision
Development Toolkit, this course will take you through the
main and fundamental Image Processing tools used in industry
and research. At the end of this course you will be able to
create the following Apps:
• App 1 - Counting M&Ms in an Image,
• App 2 - Color Segmentation and Tracking,
• App 3 - Coin Blob detection
• App 4 - Blob Range Estimation
• App 5 - Lane Detection and Ruler Width Measurement
• App 6 - Pattern or Template Matching to detect Complex
Objects
• App 7 - Object Tracking
• App 8 - Bar code Recognition
• App 9 - Optical Character Recognition (OCR)
With these basic and advanced algorithms mastered, the
course will take you through the basic operation of the theory
behind each algorithm as well how they applied in real world
scenarios.