Chapter 1. Introduction to machine vision.pdf

XỬ LÝ ẢNH TRONG CƠ ĐIỆN TỬ
Machine Vision
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TRƯỜNG ĐẠI HỌC BÁCH KHOA HÀ NỘI
Giảng viên: TS. Nguyễn Thành Hùng
Đơn vị: Bộ môn Cơ điện tử, Viện Cơ khí
Hà Nội, 2021

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Chapter 1. Introduction to machine vision
1. Introduction
2. Basic elements of machine vision system
3. Classification
4. Technical specifications
5. Designing a Machine Vision System
Phân đoạn ảnh

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1. Introduction
❖Definition
➢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, usually in industry.
➢Machine vision is a term encompassing a large number of technologies,
software and hardware products, integrated systems, actions, methods and
expertise.
➢Machine vision as a systems engineering discipline can be considered distinct
from computer vision, a form of computer science.
➢It attempts to integrate existing technologies in new ways and apply them to
solve real world problems.

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1. Introduction
❖Definition
➢The overall machine vision process includes planning the details of the
requirements and project, and then creating a solution. During run-time, the
process starts with imaging, followed by automated analysis of the image and
extraction of the required information.

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1. Introduction
❖Definition
https://en.wikipedia.org/wiki/Glossary_of_machine_vision

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1. Introduction
❖Application: Locate
➢To find the object and report its position and orientation.

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1. Introduction
❖Application: Measure
➢To measure physical dimensions of the object

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1. Introduction
❖Application: Inspect
➢To validate certain features

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1. Introduction
❖Application: Inspect

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1. Introduction
❖Application: Identify

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1. Introduction
3. Classification

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2.1. Overview
2.2. Illumination
2.3. Imaging
2.4. Image processing and analysis

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❑ 2.1. Overview
http://www.digikey.com/en/articles/techzone/2012/jan/versatile-leds-drive-machine-vision-in-automated-manufacture

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❑ 2.1. Overview
OK NG
How to automatically detect the defect?

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❑ 2.1. Overview
❖Illumination
➢Illumination: is the way an object is lit up and lighting is the actual lamp that
generates the illumination.
❖Imaging (Camera and lens)
➢The term imaging defines the act of creating an image.

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❑ 2.1. Overview
❖Image processing and analysis
➢This is where the desired features are extracted automatically by algorithms and
conclusions are drawn.
➢A feature is the general term for information in an image, for example a
dimension or a pattern.
➢Algorithms are also referred to as tools or functions.

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2.1. Overview
2.2. Illumination
2.3. Imaging

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❑ 2.2. Illumination
❖The goal of lighting in machine vision is to obtain a robust application by:
➢Enhancing the features to be inspected.
➢Assuring high repeatability in image quality.
SICK IVP, “Machine Vision Introduction,” 2006.

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❖Illumination Principles
Light can be described as waves with three properties:
➢Wavelength or color, measured in nm (nanometers)
➢Intensity
➢Polarization.

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➢ The spectral response of a sensor is the sensitivity curve for different
wavelengths.
Spectral response of a
gray scale CCD sensor.
Maximum sensitivity
is for green (500 nm).

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➢ The optical axis is a thought line through the center of the lens, i.e. the direction
the camera is looking.

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❖Lighting Types >> Ring Light
➢ A ring light is mounted around the optical axis of the lens, either on the camera or
somewhere in between the camera and the object.

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❖Lighting Types >> Ring Light
Pros
• Easy to use
• High intensity and short exposure time possible
Ambient light.
Cons
• Direct reflections, called hot spots, on reflective surfaces
Ring light. The printed matte s
urface is evenly illuminated. Ho
t spots appear on shiny
surfaces (center), one for each
of the 12 LEDs of the ring light.

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❖Lighting Types >> Spot Light
➢ A spot light has all the light emanating from one direction that is different from the
optical axis. For flat objects, only diffuse reflections reach the camera.
Mainly diffuse reflections
reach the camera
Object
Spot light

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❖Lighting Types >> Spot Light
Pros
• No hot spots
Cons
• Uneven illumination
• Requires intense light since it is
dependent on diffuse reflections

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❖Lighting Types >> Backlight
➢ The backlight principle has the object being illuminated from behind to produce a
contour or silhouette.

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❖Lighting Types >> Backlight
Pros
• Very good contrast
• Robust to texture, color, and ambient light
Cons
• Dimension must be
larger than object
Ambient light.
Backlight: Enhances contours
by creating a silhouette

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❖Lighting Types >> Darkfield
➢ Darkfield means that the object is illuminated at a large angle of incidence.

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❖Lighting Types >> Darkfield
Pros
• Good enhancement of scratches, protruding
edges, and dirt on surfaces
Cons
• Mainly works on flat surfaces with small features
• Requires small distance to object
• The object needs to be somewhat reflective
Ambient light. Darkfield: Enhances relief co
ntours, i.e., lights up edges

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❖Lighting Types >> On-Axis Light
➢ When an object needs to be illuminated parallel to the optical axis, a semi-
transparent mirror is used to create an on- axial light source.

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❖Lighting Types >> On-Axis Light
Pros
• Very even illumination, not hot spots
• High contrast on materials with different
reflectivity
Cons
• Low intensity requires long exposure times
• Cleaning off semi-transparent mirror (beam-
splitter) often needed
Inside of a can
as seen with
ambient light
Inside of the same can
as seen with a coaxial
(on-axis) light

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❖Lighting Types >> Dome Light
➢ The dome light produces the needed uniform light intensity inside of the dome
walls.

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Pros
• Works well on highly reflective
materials
• Uniform illumination, except for the
darker middle of the image. No hot
spots
Cons
• Low intensity requires
long exposure times
• Dimensions must be
larger than object
• Dark area in the middle of
the image

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Ambient light. On top of the key numbers is a curved,
transparent material causing direct reflections.
The direct reflections are eliminated by
the dome light’s even illumination.

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❖Lighting Types >> Laser Light
➢ A 2D camera with a laser line can provide a cost efficient solution for low-contrast
and 3D inspections.
Pros
• Robust against ambient light
• Allows height measurements (z parallel
to the optical axis).
• Low-cost 3D for simpler applications
Cons
• Laser safety issues
• Data along y is lost in favor of z (height) data
• Lower accuracy than 3D cameras

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❖Lighting Types >> Laser Light
Ambient light. Contract lens containers, the left
is facing up (5mm high at cross) and the right is
facing down (1mm high at minus sign.
The laser line clearly shows the
height difference.

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❖Lighting Variants and Accessories >> Strobe or Constant light
➢ A strobe light is a flashing light.
➢ Strobing allows the LED to emit higher light intensity than what is achieved with a
constant light by turbo charging.

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❖Lighting Variants and Accessories >> Diffusor Plate
➢ The diffusor plate converts direct light into diffuse.
➢ The purpose of a diffusor plate is to avoid bright spots in the image, caused by
the direct light's reflections in glossy surfaces.
Two identical white bar lights, with diffusor
plate (top) and without (bottom).

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❖Lighting Variants and Accessories >> LED Color
➢ LED lightings come in several colors. Most common are red and green. There are
also LEDs in blue, white, UV, and IR.
➢ Different objects reflect different colors. A blue object appears blue because it
reflects the color blue.
➢ Therefore, if blue light is used to illuminate a blue object, it will appear bright in a
gray scale image.

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❖Lighting Variants and Accessories >> LED Color

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❖Lighting Variants and Accessories >> Optical Filters
➢ An optical filter is a layer in front of the sensor or lens that absorbs certain
wavelengths (colors) or polarizations.
➢ Two main optical filter types are used for machine vision:
1
2
Polarization filter: Only transmits light with a certain polarization. Light changes its
polarization when it is reflected, which allows us to filter out unwanted reflections.
Band-pass filter: Only transmits light of a certain color, i.e. within a certain
wavelength interval. example, a red filter only lets red through

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❖Lighting Variants and Accessories >> Optical Filters
Original image Image seen by gray
scale camera with
ambient light and
without filter
Red light and a
red band-pass filter
Green light and a
green band-pass filter

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2.1. Overview
2.2. Illumination
2.3. Imaging

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❑ 2.3. Imaging
➢ The term imaging defines the act of creating an image.
➢ Imaging has several technical names: Acquiring, capturing, or grabbing
➢ To grab a high-quality image → the number one goal for a successful vision
application.

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❑ 2.3. Imaging
❖Basic Camera Concepts
➢ A simplified camera setup consists of camera, lens, lighting, and object.

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❑ 2.3. Imaging
❖Basic Camera Concepts: Digital Imaging
➢ A sensor chip is used to grab a digital image.
➢ On the sensor there is an array of lightsensitive pixels.
Sensor chip with an array
of light-sensitive pixels.

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❑ 2.3. Imaging
There are two technologies used for digital image sensors:
➢ CCD (Charge-Coupled Device)
➢ CMOS (Complementary Metal Oxide Semiconductor).

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❑ 2.3. Imaging
http://www.f4news.com/2016/05/09/ccd-vs-cmos-infographic/

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❑ 2.3. Imaging
❖Basic Camera Concepts: Lenses and Focal Length
➢ The lens (Objective) focuses the light that enters the camera in a way that
creates a sharp image.
Focused or sharp image. Unfocused or blurred image.

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❑ 2.3. Imaging
➢ The angle of view determines how much of the visual scene the camera sees.

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❑ 2.3. Imaging
➢ The focal length is the distance between the lens and the focal point.
➢ When the focal point is on the sensor, the image is in focus.

The spectral response of a sensor is the sensitivity curve for different
wavelengths. Camera
sensors can have a different spectral response than the human eye.
Lenses and Focal Length
▪ Focal length is related to angle of view in that a long focal length corresponds to a
small angle of view, and vice versa.
Basic Camera Concepts
❑ 2.3. Imaging

wavelengths. Camera
Field of View in 2D
▪ The FOV (Field of View) in 2D systems is the full area that a camera sees. The FOV
is specified by its width and height.
▪ The object distance is the distance between the lens and the object.
❑ 2.3. Imaging

wavelengths. Camera
Aperture and F-stop
▪ The aperture is the opening in the lens that controls the amount of light that is let
onto the sensor. In quality lenses, the aperture is adjustable.
❑ 2.3. Imaging

wavelengths. Camera
Aperture and F-stop
▪ The size of the aperture is measured by its F-stop value. A large F-stop value means
a small aperture opening, and vice versa.
▪ For standard CCTV lenses, the F-stop value is adjustable in the range between F1.4
and F16.
❑ 2.3. Imaging

wavelengths. Camera
Depth of Field
▪ The minimum object distance (sometimes abbreviated MOD) is the closest
distance in which the camera lens can focus and maximum object distance is the
farthest distance.
❑ 2.3. Imaging

wavelengths. Camera
Depth of Field
▪ The focal plane is found at the distance where the focus is as sharp as possible.
▪ Objects closer or farther away than the focal plane can also be considered to be in
focus. This distance interval where good-enough focus is obtained is called depth of
field (DOF).
❑ 2.3. Imaging

wavelengths. Camera
Depth of Field
❑ 2.3. Imaging

wavelengths. Camera
Depth of Field
▪ The depth of field depends on both the focal length and the aperture adjustment.
❑ 2.3. Imaging

wavelengths. Camera
Depth of Field
▪ By adding a distance ring between the camera and the lens, the focal plane (and
thus the MOD) can be moved closer to the camera. A distance ring is also referred to
as shim, spacer, or extension ring.
❑ 2.3. Imaging

wavelengths. Camera
Depth of Field
▪ A side-effect of using a distance ring is that a maximum object distance is
introduced and that the depth of field range decreases.
❑ 2.3. Imaging

wavelengths. Camera
Pixels and Resolution
▪ A pixel is the smallest element in a digital image. Normally, the
pixel in the image corresponds directly to the physical pixel on
the sensor.
▪ To the right is an example of a very small image with dimension
8x8 pixels. The dimensions are called x and y, where x
corresponds to the image columns and y to the rows.
Basic Image Concepts
❑ 2.3. Imaging

wavelengths. Camera
▪ Typical values of sensor resolution in 2D machine
vision are:
➢ VGA (Video Graphics Array): 640x480 pixels
➢ XGA (Extended Graphics Array): 1024x768 pixels
➢ SXGA (Super Extended Graphics Array):
1280x1024 pixels
❑ 2.3. Imaging

wavelengths. Camera
▪ The object resolution is the physical dimension on the object that corresponds to
one pixel on the sensor. Common units for object resolution are μm (microns) per
pixel and mm per pixel.
▪ Example: Object Resolution Calculation: FOV width = 50 mm, Sensor resolution =
640x480 pixels, Calculation of object resolution in x:
❑ 2.3. Imaging

wavelengths. Camera
Intensity
▪ The brightness of a pixel is called intensity. The intensity information is stored for
each pixel in the image and can be of different types. Examples:
➢ Binary: One bit per pixel.
➢ Gray scale: Typically one byte per pixel.
❑ 2.3. Imaging

wavelengths. Camera
Intensity
➢ Color: Typically one byte per pixel and color. Three bytes are needed to obtain full
color information. One pixel thus contains three components (R, G, B).
❑ 2.3. Imaging

wavelengths. Camera
Intensity
▪ When the intensity of a pixel is digitized and described by a byte, the information is
quantized into discrete levels. The number of bits per byte is called bit-depth.
❑ 2.3. Imaging

wavelengths. Camera
Exposure
▪ Exposure is how much light is detected by the photographic film or sensor. The
exposure amount is determined by two factors:
➢ Exposure time: Duration of the exposure, measured in milliseconds (ms). Also
called shutter time from traditional photography.
➢ Aperture size: Controls the amount of light that passes through the lens.
❑ 2.3. Imaging

wavelengths. Camera
Exposure
▪ If the exposure time is too short for the sensor to capture enough light, the image is
said to be underexposed. If there is too much light and the sensor is saturated, the
image is said to be overexposed.
❑ 2.3. Imaging

wavelengths. Camera
Gain
▪ Gain amplifies the intensity values after the sensor has already been exposed, very
much like the volume control of a radio (which doesn’t actually make the artist sing
louder). The tradeoff of compensating insufficient exposure with a high gain is
amplified noise.
❑ 2.3. Imaging

wavelengths. Camera
Contrast and Histogram
▪ Contrast is the relative difference between bright and dark areas in an image.
Contrast is necessary to see anything at all in an image.
❑ 2.3. Imaging

wavelengths. Camera
▪ A histogram is a diagram where the pixels are sorted in order of increasing intensity
values.
❑ 2.3. Imaging

wavelengths. Camera
❑ 2.3. Imaging

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2.1. Overview
2.2. Illumination
2.3. Imaging

❑ 2.4. Image processing and analysis
➢ The basic stages in image processing include: preprocessing, image
segmentation, feature extraction, and recognition and analysis.

wavelengths. Camera
▪ The captured image may have low contrast, noise, or contain some unnecessary
information.
▪ The main function of the preprocessing is to filter noise, increase contrast to make
images clearer and sharper.
▪ Some other functions such as converting color images to grayscale images,
extracting areas of interest (ROI - Region of Interest).
Preprocessing

wavelengths. Camera
Preprocessing
ROI Extraction
One ROI is created to verify the logotype (blue) and
another is created for barcode reading (green).
A ROI is placed around each pill in the blister pack
and the pass/fail analysis is performed once per ROI.

wavelengths. Camera
Preprocessing
Pixel Counting
Automotive part with crack. The crack is found using a darkfield illumination
and by counting the dark pixels inside the ROI.

wavelengths. Camera
Preprocessing
Digital Filters
Noisy version of original image. Image (left) after noise reduction.

wavelengths. Camera
Image segmentation
▪ Image segmentation is to split an input image into component areas to
represent analysis and image recognition.
▪ This is the most difficult part of image processing and is also error-prone,
losing the accuracy of the image processing. The result of object
identification depends very much on this stage.

wavelengths. Camera
Image segmentation
Original intensity-coded 3D image. Image after a binarization operation. Image after edge enhancement.

wavelengths. Camera
Image segmentation

wavelengths. Camera
Feature Extraction
▪ The output of the segmentation contains pixels of the image area
(segmented image) plus the code associated with the neighborhood.
▪ Features for image rendering called Feature Selection are associated with
separating image properties in the form of quantitative information.

wavelengths. Camera
Feature Extraction
R. C. Gonzalez and R. E. Woods, “Digital Image Processing,” 4th edition, Prentice Hall, 2018.
Digital boundary with resampling
grid superimposed.
Result of resampling. 8-directional chain-coded boundary.

wavelengths. Camera
Feature Extraction
Alexander Hornberg, “Handbook of Machine Vision,” WILEY-VCH, Weinheim, 2006.
The smallest axis-parallel enclosing
rectangle of a region.
The smallest enclosing rectangle
of arbitrary orientation. The smallest enclosing circle.

wavelengths. Camera
Image recognition and analysis
▪ Image recognition is the process of identifying images.
▪ This process is usually obtained by comparing with the standard sample
that has been learned (or saved) before.
▪ Interpolation is a judgment based on the meaning of identification.
➢identification of parameter
➢identification of structure

wavelengths. Camera
Reference image for teaching. Matching in new image.

wavelengths. Camera

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1. Introduction
3. Classification

91
3. Classification
3.1. 1D vision systems

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3. Classification
❑ 3.1. 1D vision systems
➢ 1D vision analyzes a digital signal one line at a time instead of looking at a whole
picture at once.
➢ This technique commonly detects and classifies defects on materials
manufactured in a continuous process, such as paper, metals, plastics, and other
non-woven sheet or roll goods.
COGNEX, “Introduction to Machine Vision,” 2016.

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3. Classification
1D vision systems scan one
line at a time while the
process moves. In the
above example, a defect in
the sheet is detected.

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3. Classification
➢ Most common inspection cameras perform area scans that involve capturing 2D
snapshots in various resolutions.
2D vision systems can
produce images with
different resolutions

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3. Classification
➢ Another type of 2D machine vision–line scan–builds a 2D image line by line.
Line scan techniques build the 2D image one line at a time.

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3. Classification
➢ 3D machine vision systems typically comprise multiple cameras or one or more
laser displacement sensors.
➢ Multi-camera 3D vision in robotic guidance applications provides the robot with
part orientation information.
➢ These systems involve multiple cameras mounted at different locations and
“triangulation” on an objective position in 3-D space.

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3. Classification
3D vision systems typically employ
multiple cameras
3D inspection system using
a single camera

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3. Classification
➢ 3D laser-displacement sensor applications typically include surface inspection
and volume measurement, producing 3D results with as few as a single camera.
➢ A height map is generated from the displacement of the reflected lasers’ location
on an object.
➢ The object or camera must be moved to scan the entire product similar to line
scanning.

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1. Introduction
3. Classification

100
4. Technical Specifications
4.1. Parts
4.2. Part Presentation
4.3. Performance Requirements
4.4. Information Interfaces
4.5. Installation space
4.6. Environment

101
❑ 4.1. Parts
➢ Discrete parts or endless material (i.e., paper or woven goods) minimum and
maximum dimensions
➢ Changes in shape
➢ Description of the features that have to be extracted
➢ Changes of these features concerning error parts and common product variation
➢ Surface finish
➢ Color
➢ Corrosion, oil films, or adhesives
➢ Changes due to part handling, i.e., labels, fingerprints

102
4.1. Parts
4.6. Environment

103
❑ 4.2. Parts Presentation
➢ Regarding part motion, the following options are possible:
▪ indexed positioning
▪ continuous movement
➢ If there is more than one part in view, the following topics are important:
▪ number of parts in view
▪ overlapping parts
▪ touching parts

104
4.1. Parts
4.6. Environment

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❑ 4.3. Performance Requirements
➢ The performance requirements can be seen in the aspects of:
▪ accuracy and
▪ time performance

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❑ 4.3. Performance Requirements
➢ Time performance:
▪ cycle time
▪ start of acquisition
▪ maximum processing time
▪ number of production cycles from inspection to result using (for result
buffering)

107
4.1. Parts
4.6. Environment

108
❑ 4.4. Information Interfaces
➢ User interface for handling and visualizing results
➢ Declaration of the current part type
➢ Start of the inspection
➢ Setting results
➢ Storage of results or inspection data in log files or databases
➢ Generation of inspection protocols for storage or printout

109
4.1. Parts
4.6. Environment

110
❑ 4.5. Installation Space
➢ The possibility of aligning the illumination and the camera
➢ Is an insight into the inspection scene possible?
➢ What variations are possible for minimum and maximum distances between the
part and the camera?
➢ The distance between the camera and the processing unit

111
4.1. Parts
4.6. Environment

112
❑ 4.6. Environment
➢ Ambient light
➢ Dirt or dust that the equipment needs to be protected from shock or vibration that
affects the part of the equipment heat or cold
➢ Necessity of a certain protection class
➢ Availability of power supply

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1. Introduction
3. Classification

114
5.1. Camera Type
5.2. Field of View
5.3. Resolution
5.4. Choice of Camera, Frame Grabber, and Hardware Platform
5.5. Lens Design
5.6. Choice of Illumination
5.7. Mechanical Design
5.8. Electrical Design
5.9. Software
5.10. Costs

115
❑ 5.1. Camera Type
➢ Line scan camera
➢ Area scan camera
➢ 3D camera
Directions for a line scan camera.

116
5.1. Camera Type
5.2. Field of View
5.3. Resolution
5.5. Lens Design
5.9. Software
5.10. Costs

117
❑ 5.2. Field of View
The field of view is determined by the following factors:
➢ maximum part size
➢ maximum variation of part presentation in
translation and orientation
➢ margin as an offset to part size
➢ aspect ratio of the camera sensor
Field of view.

118
❑ 5.2. Field of View
Example:
Horizontal Vertical
maximum part size 10 mm 6 mm
tolerance in positioning 1 mm
margin 2 mm
aspect ratio 4:3
𝐹𝑂𝑉ℎ𝑜𝑟_𝑐𝑎𝑙 = 10 𝑚𝑚 + 1 𝑚𝑚 + 2 𝑚𝑚 = 13 𝑚𝑚 → 𝐹𝑂𝑉𝑣𝑒𝑟_𝑒𝑠𝑡 =
3
4
𝐹𝑂𝑉ℎ𝑜𝑟_𝑐𝑎𝑙 = 9.75 𝑚𝑚
𝐹𝑂𝑉𝑣𝑒𝑟_𝑐𝑎𝑙 = 6 𝑚𝑚 + 1 𝑚𝑚 + 2 𝑚𝑚 = 9 𝑚𝑚 < 𝐹𝑂𝑉𝑣𝑒𝑟_𝑒𝑠𝑡
𝐹𝑂𝑉ℎ𝑜𝑟 = 𝐹𝑂𝑉ℎ𝑜𝑟_𝑐𝑎𝑙 𝑎𝑛𝑑 𝐹𝑂𝑉
𝑣𝑒𝑟 = 𝐹𝑂𝑉𝑣𝑒𝑟_𝑒𝑠𝑡 𝐹𝑂𝑉 = 13 𝑚𝑚 × 9.75 𝑚𝑚

119
5.1. Camera Type
5.2. Field of View
5.3. Resolution
5.5. Lens Design
5.9. Software
5.10. Costs

120
❑ 5.3. Resolution
➢ camera sensor resolution
➢ spatial resolution
➢ measurement accuracy

121
❑ 5.3. Resolution
➢ Calculation of Resolution

122
❑ 5.3. Resolution
➢ Example: Measure the dimension of the object 10mmx6mm above with accuracy
0.01 mm.
▪ Using edge detection for dimention measurement → Nf = 1/3 pixel. But there is a
tolerance in positioning, the number of pixels for the smallest feature is set to 1
pixel (Nf = 1 pixel).
▪ Size of the smallest feature Sf = 0.01 mm

123
❑ 5.3. Resolution
▪ Camera resolution:
𝑅𝐶_ℎ𝑜𝑟 = 𝐹𝑂𝑉ℎ𝑜𝑟
𝑁𝑓
𝑆𝑓
= 13 𝑚𝑚
1 𝑝𝑖𝑥𝑒𝑙
0.01 𝑚𝑚
= 1300 𝑝𝑖𝑥𝑒𝑙𝑠
𝑅𝐶_𝑣𝑒𝑟 = 𝐹𝑂𝑉
𝑣𝑒𝑟
𝑁𝑓
𝑆𝑓
= 9.75 𝑚𝑚
0.01 𝑚𝑚
= 975 𝑝𝑖𝑥𝑒𝑙𝑠

124
❑ 5.3. Resolution
▪ Object resolution (spatial resolution): assuming that a lens with a field of view of
14 mm ( > 13 mm) was chosen, a camera with resolution of 1440x1080 was
chosen.
𝑅𝑠 =
𝐹𝑂𝑉
𝑅𝐶
=
14 𝑚𝑚
= 0.01 𝑚𝑚/𝑝𝑖𝑥𝑒𝑙

125
❑ 5.3. Resolution
➢ Calculation of Resolution: Resolution for a Line Scan Camera

126
5.1. Camera Type
5.2. Field of View
5.3. Resolution
5.5. Lens Design
5.9. Software
5.10. Costs

127
❑ 5.4. Choice of Camera, Frame Grabber, and Hardware Platform
➢ Camera Model
▪ color sensor
▪ interface technology
▪ progressive scan for area cameras
▪ packaging size
▪ price and availability

128
➢ Frame Grabber
▪ compatibility with the pixel rate
▪ compatibility with the software library
▪ number of cameras that can be addressed
▪ utilities to control the camera via the frame grabber
▪ timing and triggering of the camera
▪ availability of on-board processing
▪ availability of general purpose I/O
▪ price and availability

129
➢ Pixel Rate
▪ This is the speed of imaging in terms of pixels per second.

130
➢ Pixel Rate
▪ For an area camera:
An overhead of 10% to 20% should be considered
due to additional bus transfer.

131
➢ Pixel Rate
▪ For a line scan camera:

132
➢ Hardware Platform
• Compatibility with frame grabber
• Operating system
• Development process
• means for a user-friendly human machine
interface
• Processing load
• Miscellaneous points: available interfaces,
memory, packaging size, price, and availability
▪ smart cameras
▪ compact vision systems
▪ PC-based systems

133
❑ Example about a camera
https://www.baslerweb.com/en/products/cameras/area-scan-cameras/ace/aca2440-75uc/

134

135

136
5.1. Camera Type
5.2. Field of View
5.3. Resolution
5.5. Lens Design
5.9. Software
5.10. Costs

137
❑ 5.5. Lens Design
➢ Focal Length
standoff distance
focal length
Magnification
lens extension
focus distance

138
➢ Focal Length

139
➢ Lens Flange Focal Distance
▪ This is the distance between the lens mount face and the image plane.

140
➢ Extension Tubes
▪ The lens extension l can be increased using the focus adjustmentof the lens.
▪ If the distance cannot be increased, extension tubes can be used to focus close
objects. As a result, the depth of view is decreased.
▪ For higher magnifications, such as from 0.4 to 4, macro lenses offer better image
quality.

141
➢ Lens Diameter and Sensor Size
Areas illuminated by the lens and camera;
the left side displays an appropriate choice.

142
➢ Sensor Resolution and Lens Quality
▪ As for high resolution cameras, the requirements on the lens are higher than
those for standard cameras.
▪ Using a low-budget lens might lead to poor image quality for high resolution
sensors, whereas the quality is acceptable for lower resolutions.

143
❑ Example about lens

144
https://www.baslerweb.com/en/products/vision-components/lenses/basler-lens-c23-1620-5m-p-f16mm/

145
https://www.baslerweb.com/en/products/vision-components/lenses/basler-lens-c23-1620-5m-p-f16mm/

146
5.1. Camera Type
5.2. Field of View
5.3. Resolution
5.5. Lens Design
5.9. Software
5.10. Costs

147
❑ 5.6. Choice of Illumination
➢ Concept: Maximize Contrast
▪ Direction of light: diffuse from all directions or directed from a range of angles
▪ Light spectrum
▪ Polarization: effect on surfaces, such as metal or glass

148
➢ Illumination Setups
▪ Backlight and
▪ Frontlight
• Diffused light
• Directed light
• Confocal frontlight
• Bright field
• Dark field

149
➢ Light Sources
▪ Fluorescent tubes
▪ Halogen and xenon lamps
▪ LED
▪ Laser

150
➢ Approach to the Optimum Setup
▪ A confirmation of the setup based on experiments with sample parts is
mandatory.
▪ The alignment of light, the part and the camera needs to be documented.
▪ To balance between similar setups images have to be captured and compared
for the maximum contrast.

151
➢ Interfering Lighting
▪ The influences of different lamps on the images have to be checked.
▪ To avoid interfering, a spatial separation can be achieved by using different
camera stations.
▪ The part is imaged with different sets of cameras and illuminations.

152
5.1. Camera Type
5.2. Field of View
5.3. Resolution
5.5. Lens Design
5.9. Software
5.10. Costs

153
❑ 5.7. Mechanical Design
➢ As the cameras, lenses, standoff distances, and
illumination devices are determined, the mechanical
conditions can be defined.
➢ As for mounting of cameras and lights the
adjustment is important for installation, operation,
and maintenance.
➢ The devices have to be protected against vibration
or shock.
➢ The position of cameras and lights should be
changed easily.

154
5.1. Camera Type
5.2. Field of View
5.3. Resolution
5.5. Lens Design
5.9. Software
5.10. Costs

155
❑ 5.8. Electrical Design
➢ The power supply
➢ The housing of cameras and illumination
➢ The length of cables as well as their laying

156
5.1. Camera Type
5.2. Field of View
5.3. Resolution
5.5. Lens Design
5.9. Software
5.10. Costs

157
❑ 5.9. Software
➢ selection of a software library
➢ design and implementation of the application-specific software

158
❑ 5.9. Software
➢ Software Library
➢ Software Structure
▪ Image acquisition
▪ Preprocessing
▪ Feature localization
▪ Feature extraction
▪ Feature interpretation
▪ Generation of results
▪ Handling interfaces

159
❑ 5.9. Software
➢ General Topics
▪ Visualization of live images for all cameras
▪ Possibility of image saving
▪ Maintenance modus
▪ Log files for the system state
▪ Detailed visualization of the image processing
▪ Crucial processing parameters

160
5.1. Camera Type
5.2. Field of View
5.3. Resolution
5.5. Lens Design
5.9. Software
5.10. Costs

161
❑ 5.10. Costs
➢ The development costs
▪ project management
▪ base design
▪ hardware components
▪ software licenses
▪ software development
▪ installation
▪ test runs, feasibility tests, and acceptance test
▪ training
▪ documentation

162
❑ 5.10. Costs
➢ The operating costs
▪ maintenance, such as cleaning of the optical equipment
▪ change of equipment, such as lamps
▪ utility, for instance electrical power or compressed air if needed
▪ costs for system modification due to product changes

Quiz 1
Quiz Number 1 Quiz Type
OX Example Select
Question
Choose the lighting for measuring the radius R and r of following
object:
Example
A. Dome Light B. On-Axis Light
C. Darkfield D. Backlight
Answer
Feedback
R
r

Quiz 2
OX Example Select
Question
Assume that the object size = 10 cm x 20 cm, sensor resolution =
640x480 pixels. Calculate the object resolution?
Example
A. 0.31 mm/pixel B. 0.21 mm/pixel C. 0.17 mm/pixel D. 0.42
mm/pixel
Answer
Feedback

Quiz 3
OX Example Select
Question Splitting an input image into component areas is called:
Example
A. Image preprocessing B. Image segmentation
C. Image recognition D. Image representation
Answer
Feedback

Quiz 4
OX Example Select
Question The performance requirements of a machine vision system are:
Example
A. Accuracy B. Time performance
C. Both A and B D. None of the above
Answer
Feedback

Quiz 5
Quiz Number 5 Quiz Type OX Example Select
Question
Diameter Inspection of Rivets:
+ The nominal size of the rivets lies in a range of 3 mm to 4 mm
+ The required accuracy is 0.1 mm
+ The tolerance of part positioning is less than ±1 mm across the
optical axis and ±0.1 mm in the direction of the optical axis. The
belt stops for 1.5 s.
+ The maximum processing time is 2 s; the cycle time is 2.5s.
+ The maximum space for installing equipment is 500 mm.

Quiz 5
Quiz Number 5 Quiz Type OX Example Select
Question Bearing with rivet and disk

Chapter 1. Introduction to machine vision.pdf

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