Print this page Machine Vision (MV)
Robert P. Jackson, P.E.
Course Outline
This course will examine in detail the subject of Machine Vision from inception to the status of the technology at the present time. We will discuss the course in a logical fashion moving through the sections as follows:
INTRODUCTION TO MACHINE VISION (MV)
HISTORY OF MV TECHNOLOGY AND PROGRESSION OF THE TECHNOLOGY FROM INCEPTION
BENEFITS OF APPLICATION FOR MANUFACTURING AND NON-MANUFACTURING SITUATIONS
BASIC OPERATION—SYSTEMS APPROACH
CRITICAL FACTORS
USES FOR MACHINE VISION
Inspection
- Color matching
- Subassembly verification
- Die attach bond inspection
- Location & alignment for pick and place
- Ball grid array inspection
- Measure solder paste levels
- Test tube cap and color inspection
- Vial reading verification
- Quality checking
- Package integrity
Gauging and Metrology
Guidance
- Wafer positioning
- Robotic guidance
Identification and Verification
Facial Recognition
Security
ANNUAL SALES-DOMESTIC AND GLOBAL
COST OF OPERATION
SYSTEM REQUIREMENTS
Hardware
Software
THEORY OF OPERATION
Image Acquisition
Image Processing and Analysis
Filtering
Thresholding
Pixel Counting
Segmentation
Edge Detection
Color Analysis
Neural Net Discovery
Pattern Recognition
Bar Code Data Matrix
Gauging and Metrology
STANDARDS
MAJOR COMPANIES WITHIN THE TECHNOLOGY
SUMMARY
This course includes
a multiple choice quiz at the end,
Learning Objective
At the completion of this course the student will have accomplished the following:
- Have an understanding of the overall technology, processes involved, and applications with MV technology.
- Have an idea as to the history of MV and the progression from initial uses
- Will understand benefits of technology and how those benefits improve efficiency and quality of manufacturing processes
- Will understand non-manufacturing uses for MV technology and specific applications
- Have an in-depth understanding of the theory of operation including:
- Image Acquisition
- Image Processing and Analysis
- Filtering
- Thresholding
- Pixel Counting
- Segmentation
- Edge Detection
- Color Analysis
- Neural Net Discovery
- Pattern Recognition
- Bar Code Data Matrix
- Gauging and Metrology
- Be able to identify uses for the technology
- Have an understanding of all equipment needed for specific applications
- Have an understanding of software necessary for developing successful imaging and processing of that image
- Have an appreciation for domestic and global sales and potential for future global sales
- Have an understanding of the basic start up and operating costs involved with the technology
- Will understand the six (6) critical factors with Machine Vision
- Will be introduced to standards both US and global specifying acceptable operation
- Will be given ideas as to the future of Machine Vision industry and the global implications of its use.
- Will be given complete glossary of terms
- Will be given partial list of successful vendors and major equipment and hardware companies contributing to technology
- Will understand how critical lighting is to successful imaging
Intended Audience
This 8 hour course is intended for individuals participating in the following professions:
- Industrial engineers involved with the design and layout of conveyor systems within a manufacturing or warehousing environment.
- Quality control engineers and those responsible for supervising QC operations where visual recognition of components or processes may be considered and employed.
- Warehouse managers working with products needing quality inspection.
- Engineers designing robotic systems required for precision placement of components.
- Personnel responsible for gauging and measurement of components.
- CFOs responsible for holding and cutting cost.
- Maintenance personnel responsible for maintaining conveyor systems.
- Six Sigma specialists and statisticians responsible for providing pass/fail data to management.
- Packaging engineers
- Manufacturing engineers
- CEOs interested in the overall operation of facilities
- IT specialists
- Maintenance technicians
- Individuals in professions responsible for providing home, retail and industrial security
- Law enforcement personnel
Benefit to Attendees
This eight (8) hour course is intended to provide necessary information so participants will gain an understanding of the technology and its use. We go considerably further than the basics, thereby making it possible to gain knowledge facilitating informed conversations with vendors, hardware specialists and IT personnel within the profession. Machine vision is a rapidly growing technology with developments each year that reduce the size and weight of camera equipment as well as improve software necessary for operation. In addition to the text, a complete glossary of terms will be provided to facilitate understanding of the vocabulary used on a day-to-day basis. The references provided will serve as material for further reading and knowledge.
Course Introduction
Machine vision is an evolving technology used to replace or complement manual inspections and measurements with digital cameras and image processing. This technology is used in a variety of different industries to automate production, increase production speed and yield, and to improve product quality. One primary objective is discerning the quality of a product when high-speed production is required. This industry is knowledge-driven and experiences an ever- increasing complexity of components and modules of machine vision systems. In the last few years, the markets pertaining to machine vision components and systems have grown significantly.
Machine vision, also known as "industrial vision" or "vision systems", is primarily focused on computer vision relative to industrial manufacturing processes like defect detection. There is also a place for non-manufacturing processes like traffic control and healthcare purposes. The inspection processes are carried by responsive input needed for control; for example, robot control or default verification. The system setup consists of cameras capturing, interpreting and signaling individual control systems related to some pre-determined tolerance or requirement. These systems have increasingly become more powerful while at the same time easy to use. Recent advancements in machine vision technology, such as smart cameras and embedded machine vision systems, have increased the scope of machine vision markets for a wider application in the industrial and non-industrial sectors.
One example of a non-industrial application for machine vision is facial recognition. This technology is generally considered to be one facet of the biometrics technology suite. Facial recognition is playing a major role in identifying and apprehending suspected criminals as well as individuals in the process of committing a crime or unwanted activity. Casinos in Las Vegas are using facial recognition to spot “players” with shady records or even employees complicit with individuals trying to get even with “the house”. This technology incorporates visible and infrared modalities face detection, image quality analysis, verification and identification. Many companies use cloud-based image-matching technology to their product range providing the ability to apply theory and innovation to challenging problems in the real world.
There are six (6) basic and critical factors for choosing an imaging system. These are as follows:
- Resolution--While a higher resolution camera will help increase accuracy by yielding a clearer, more precise image for analysis, the downside is slower speed.
- Speed of Exposure—Products rapidly moving down a conveyor line will require much faster exposure speed from vision systems. Such applications might be candy or bottled products moving at extremely fast rates.
- Frame Rate--The frame rate of a camera is the number of complete frames that a camera can send to an acquisition system within a predefined time period, which is usually stated as a specific number of frames per second.
- Spectral Response and Responsiveness--All digital cameras that employ electronic sensors are sensitive to light energy. The wavelength of light energy that cameras are sensitive to typically ranges from approximately 400 nanometers to a little beyond 1000 nanometers. There may be instances in imaging when it is desirable to isolate certain wavelengths of light that emanate from an object, and where characteristics of a camera at the desired wavelength may need to be defined. A matching and selection process must be undertaken by application engineers to insure proper usage of equipment relative to the needs at hand.
- Bit Depth--Digital cameras produce digital data, or pixel values. Being digital, this data has a specific number of bits per pixel, known as the pixel bit depth. Each application should be considered carefully to determine whether fine or coarse steps in grayscale are necessary. Machine vision systems commonly use 8-bit pixels, and going to 10 or 12 bits instantly doubles data quantity, as another byte is required to transmit the data. This also results in decreased system speed because two bytes per pixel are used, but not all of the bits are significant. Higher bit depths can also increase the complexity of system integration since higher bit depths necessitate larger cable sizes, especially if a camera has multiple outputs.
- Lighting—Proper lighting is definitely necessary for success when applying machine vision to both manufacturing and non-manufacturing situations.
This document will provide background information necessary to understand all factors relative to machine vision for applications possible in today’s complex world, including facial recognition.
Course Content
The course content is in a PDF file:
Please click on the above underlined hypertext to view, download or print the document for your study. Because of the large file size, we recommend that you first save the file to your computer by right clicking the mouse and choosing "Save Target As ...", and then open the file in Adobe Acrobat Reader. If you still experience any difficulty in downloading or opening this file, you may need to close some applications or reboot your computer to free up some memory.
Course Summary
Machine vision is defined as follows: “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. The scope of MV is broad. MV is related to, though distinct from, computer vision.”
Machine vision, also known as "industrial vision" or "vision systems", is primarily focused on computer vision in the perspective of industrial manufacturing processes like defect detection; and in non-manufacturing processes like traffic control and healthcare purposes. The inspection processes are carried by responsive input needed for control; for example, robot control or default verification. The system setup consists of cameras capturing, interpreting and signaling individual control systems related to some pre-determined tolerance or requirement. These systems have increasingly become more powerful while at the same time easy to use. Recent advancements in machine vision technology, such as smart cameras and embedded machine vision systems, have increased the scope of machine vision markets for a wider application in the industrial and non-industrial sectors.
Machine vision also encompasses facial recognition technology which we will cover in this training document. We will also take a look at how security and law enforcement are facilitated using machine vision methodology.
Quiz
Once you finish studying the above course content, you need to take a quiz to obtain the PDH credits.
DISCLAIMER: The materials contained in the online course are not intended as a representation or warranty on the part of PDH Center or any other person/organization named herein. The materials are for general information only. They are not a substitute for competent professional advice. Application of this information to a specific project should be reviewed by a registered architect and/or professional engineer/surveyor. Anyone making use of the information set forth herein does so at their own risk and assumes any and all resulting liability arising therefrom.
