image processing fundamentals - vision online pattern matching •gauging •barcode...
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Image Processing Fundamentals
Nicolas Vazquez Principal Software Engineer
National Instruments
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Agenda • Objectives and Motivations • Enhancing Images • Checking for Presence • Locating Parts • Measuring Features • Identifying and Verifying Components
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Class Goals • Teach the fundamental image processing tools available
in machine vision software • Provide some background into how the algorithms work • Accelerate your machine vision learning curve
• What not to expect
– Learn how to develop a complete vision system – Learn specific, proprietary functions – 3D vision, color or other advanced topics – Discussion of the benefits of different application development
environments
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Image Processing for Machine Vision
• Objective – To extract useful information present in an image, in a limited
time • Secondary
– To display an image for users • Not
– Improve appearance of image in general • Used for
– Image pre-processing • Minimize variations of information in the image • Prepare the image for processing and measurement
– Application specific processing • Use image to count, locate, and measure attributes
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Image Types
• Grayscale – 8-bit: pixel values range from 0 to 255 – 16-bit: pixel values range from 0 to
65535 • Color
– Composed of 3 grayscale images (RGB) • Other types
– Binary: pixel values: 0 and 1 • Commonly used to identify objects of
interest in an image • Usually the result of image processing
step – Floating point
• Usually a result of a computation – Complex
Binary Image
Grayscale Image
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What is an Ideal Image?
• Range of grayscale values – Spread out between 0 and 255 – No pixels “saturated” at 255 (for most applications)
• Loss of information, impossible to distinguish between saturated pixels
• Good contrast – Between the parts of the image of interest
• Repeatable
In short, an ideal image requires the least number of image processing steps to obtain the result.
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Class Organization
Enhance • Filter
noise or unwanted features
• Remove distortion
• Calibrate images
Check • Measure
intensity • Create
particles • Analyze
particles
Locate • Match
patterns • Match
geometry • Set-up
coordinate systems
Identify • Read text
(OCR) • Read 1D
barcodes • Read 2D
codes
Measure • Detect
edges • Measure
distance • Calculate
geometry
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Class Organization
Enhance • Filter
noise or unwanted features
• Remove distortion
• Calibrate images
Check • Measure
intensity • Create
particles • Analyze
particles
Locate • Match
patterns • Match
geometry • Set-up
coordinate systems
Identify • Read text
(OCR) • Read 1D
barcodes • Read 2D
codes
Measure • Detect
edges • Measure
distance • Calculate
geometry
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Motivation
Read characters on a textured surface
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Motivation
Not possible to cleanly segment characters
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Motivation
Results without preprocessing
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Motivation
Read characters on a textured surface
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Motivation
Image after periodic pattern is removed
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Motivation
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Motivation
Results without preprocessing
Results with pre-processing
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Objective of Image Preprocessing • Process an image so that the resulting image is more
suitable than the original for a specific application
• A preprocessing method that works well for one application may not be the best method for another application
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Image Preprocessing
• Pre-processing occurs before the application specific processing
Acquisition Preprocessing Application
Specific Processing
• Shading Correction
• De-blurring • De-noising • Contrast
Enhancement • Feature
Enhancement
• Intensity Measurements
• OCR • Pattern
Matching • Gauging • Barcode • Particle
Analysis
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Enhancement Techniques • Spatial Domain: pixel-wise operations
– Brightness, contrast and gamma – Lookup tables – Gray morphology – Filtering (smoothing, median, general convolution)
• Frequency Domain – Deblurring – Filtering
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Brightness
• Adds a constant grayscale to all of the image
• Can improve appearance but not useful for most image processing steps
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Contrast
• Used to increase or decrease contrast
• Normal = 45 (degree line) – High = Higher than 45 – Low = Lower than 45
• Typical use is to improve edge detection
• Sacrifices one range of values to improve another
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Gamma
• Nonlinear contrast enhancement
• Higher gamma improves contrast for larger grayscale values
• Does not cause saturation
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Lookup Tables • A lookup table is a function that maps grayscale
values in an image to new grayscale values, to create a new result image – For example: reverse, square, power…
Power (x=1.5) Reverse Square
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Histogram • Indicates the number of pixels at each gray level • Provides a good description of the composition of an image • Helps to identify various populations
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Pixel values
# Pi
xels
Saturation
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Histogram Equalization
• Alters grayscale values of pixels so that they become evenly distributed across the full grayscale range
• The function associates an equal number of pixels per constant grayscale interval
• Takes full advantage of the available shades of gray
• Enhances contrast of the image without modifying the structure
Equalized
Original Image
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Histogram Equalization
Bright
Dark
Low Contrast
High Contrast
Cumulative Histogram
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Spatial Filtering
Filter Type Filters
Linear Highpass Gradient, Laplacian
Linear Lowpass Smoothing, Gaussian
Nonlinear Highpass
Gradient, Roberts Sobel, Prewitt, Differentiation, Sigma
Nonlinear Lowpass Median, Nth Order
Gradient
Gaussian
Sobel
Median
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Gray Morphology Morphology Function
Erosion Min(Neighbors)
Dilation Max(Neighbors)
Open Dilation(Erosion(I))
Close Erosion(Dilation(I))
Erosion
Dilation
Open
Close
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Frequency Domain Filtering • Standard filtering can be done in frequency domain
– Low Pass, High Pass, Band Pass, Band Stop, etc. • Compute the Fourier transform of the image • Multiply with the transfer function of the filter • Take the inverse Fourier transform to get the filtered image
Input Image FFT H(u,v) IFFT Output
Image
I(x,y) F(u,v) F(u,v).H(u,v) R(x,y)
Periodic noise Bandstop filtered 28
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Low Pass Filter Examples • Low Pass with Frequency Domain Filter
• Low Pass with Gaussian Filter
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High Pass Filtering Examples • Detect edges
• Sharpen image
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ENHANCE IMAGES: IMAGE CALIBRATION
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Types of Calibration • 2D spatial calibration
– Applied only to a plane – Corrects for lens and perspective distortion – Does not improve resolution of a measurement – Cannot compensate for poor lighting or unstable conditions
• 3D spatial calibration: x, y, z • Intensity calibration • Color
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Spatial Calibration
• Corrects for lens and perspective distortion • Allows the user to take real-world measurements from
image based on pixel locations.
known orientation offset
lens distortion
perspective
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Calibrating Your Image Setup • Acquire image of a calibration grid with known real-
world distances between the dots • Learn the calibration (mapping information) from its
perspective and distortion • Apply this mapping information to subsequent
images and measurements
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Image Spatial Calibration Example
Calibration grid
Acquired image
Calibrated measurements
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Image Correction • Use calibration to adjust image geometry so features are represented properly.
Calibration grid
Acquired image
Corrected Images 36
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Class Organization
Enhance • Filter
noise or unwanted features
• Remove distortion
• Calibrate images
Check • Measure
intensity • Create
particles • Analyze
particles
Locate • Match
patterns • Match
geometry • Set-up
coordinate systems
Identify • Read text
(OCR) • Read 1D
barcodes • Read 2D
codes
Measure • Detect
edges • Measure
distance • Calculate
geometry
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Region of Interest (ROI) • Region of Interest (ROI)
– A portion of the image upon which an image processing step may be performed – Can be defined statically (fixed) or dynamically (based on features located in the
image) • Used to process only pixels that are interesting
– Increases reliability – Reduces processing time
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Measure Intensity • Intensity statistics of Region of Interest (Search Area) can
be used as a very simple check for presence/absence
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Thresholding • Thresholding
– Converts each pixel value in an image to 0 or 1 according to the value of the original pixel
– Helps extract significant structures in an image
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Histogram and Thresholding
Original Image Binary Image
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Threshold Range
Histogram of Image
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Finding Gray Objects • Can also set upper and lower limits for pixel values • Pixels inside the bounds of the limit (red region) are
set to 1, and those outside the limit are set to 0
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Automatic Thresholding
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Global vs. Local Threshold
Global Threshold
Local Threshold 44
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Particles and Connectivity
• Thresholding creates binary image: – Pixels are either ‘0’ or ‘1’
• A particle in a binary image is a group of connected ‘1’ pixels
• Defining connectivity
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How Connectivity Affects a Particle
• Connectivity-4: two pixels are considered part of the same particle if they are horizontally or vertically adjacent
1 1 1
1 2 2 2 2 2 2
2 2 2 2
2 3
2
1
1
2
1 1
1 1 1 1 1
1 1 1 1 1 1
1
1
1 • Connectivity-8: two pixels
are considered part of the same particle if they are horizontally, vertically, or diagonally adjacent.
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Particle measurements
Measures particle features including: location, orientation, area, perimeter, holes, shape equivalences, moments…
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Binary Morphology • Binary morphological functions extract and alter the
structure of particles in a binary image • Morphological functions remove unwanted information
caused by the thresholding process: – Noise particles – Removing holes within particles – Particles touching the border of an image – Particles touching each other – Particles with uneven borders
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Erosion • Decreases the size of objects in an image
– Removes a layer of pixels along the boundary of the particle – Eliminates small isolated particles in the background and removes
narrow peninsulas • Use Erode to:
– Separate particles for counting
Erosion
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Dilation • Increases the size of objects in an image
– Adds a layer of pixels around the boundary of an object (including the inside boundary for objects with holes)
– Eliminates tiny holes in objects – Removes gaps or bays of insufficient width
• Use Dilate to connect particles
Dilation
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Erosion vs. Dilation
Erosion
Dilation
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Open • An erosion followed by a dilation
– Remove small particles and smooth boundaries – Does not significantly alter the size or shape of particles – Borders removed by the erosion process are replaced by the dilation process
• Use Open To: – Eliminate small particles that constitute noise
Open
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Close
Close
• A dilation followed by an erosion – Fills holes and creates smooth boundaries – Does not significantly alter the size or shape of particles – Particles that do not connect after the dilation are not changed
• Use Close To: – Eliminate small holes that constitute noise
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Advanced Morphology • Advanced morphological functions are combinations
of operations, each of which is responsible for a single operation
• These functions execute the following tasks: – Remove small or large particles – Remove particles from an image border – Fill holes – Separate particles – Keep or remove particles identified by morphological
parameters – Segmenting the image
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Particle Filtering • Keeps or removes particles based on geometric features
– Area, width, height, aspect ratio and other features are commonly used to filter
• Typically used on binary images • Cleans up noisy images
Threshold Particle Filter
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(Keep Heywood Circularity Factor < 1.1)
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Application: Particle counting
Threshold
Particle Filter
Original
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Class Organization
Enhance • Filter
noise or unwanted features
• Remove distortion
• Calibrate images
Check • Measure
intensity • Create
particles • Analyze
particles
Locate • Match
patterns • Match
geometry • Set-up
coordinate systems
Identify • Read text
(OCR) • Read 1D
barcodes • Read 2D
codes
Measure • Detect
edges • Measure
distance • Calculate
geometry
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LOCATING PARTS: PATTERN MATCHING
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Introduction to Matching • Locates regions of a grayscale image that match a
predefined template – Calculate a score for each matching region – Score indicates quality of match
• Returns the XY coordinates, rotation angle and scale for each match
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Applications
• Presence Detection • Counting • Locating • Inspection
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How It Works • Two step process:
– Step 1: Learn Template • Extract information useful for uniquely characterizing the pattern • Organize information to facilitate faster search of the pattern in the
image
– Step 2: Match • Use the information present in the template to locate regions in
the target image • Emphasis is on search methods that quickly and accurately locate
matched regions
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Pattern Matching Methods
• Different ways to perform pattern matching based on the information extracted from the template
• Two common methods: – Correlation Pattern Matching
• Relies on the grayscale information in the image for matching
– Geometric Pattern Matching • Relies on edges and geometric features in the image for
matching
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Correlation Pattern Matching
• Assumes grayscale information present in the image
• Directly uses the underlying grayscale information in the image for matching
• Grayscale values in the pattern are matched to regions in the image using normalized cross-correlation
• Score ranges from 0-1000. • Used to allow imperfect match.
Template (Pattern)
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Good template Bad template
• When to use: – Template primarily characterized by grayscale information – Matching under uniform light changes – Little occlusion and scale changes in image – Good for the general case
Correlation Pattern Matching
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Correlation Pattern Matching
• When NOT to use correlation-based pattern matching: – Non-uniform lighting
– Occlusion more than 10%
– Scale changes
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Geometric Pattern Matching • Matching tool you can use to locate parts that
contain distinct edge information • Not useful when template is predominantly defined
by texture.
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GPM is Tolerant to…
Scale Changes
Non-uniform Lighting
Background Changes
Occlusion
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GPM – Feature-based
Image
Extract Curves
Template
Target Image
Extract Features
Match Features circles
parallel lines
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Feature Comparison
Feature CPM GPM Template contains texture-like information Yes Template contains geometric information Yes Yes Find multiple match locations Yes Yes Rotation Yes Yes Scale Yes Occlusion Yes Matching under non-uniform lighting Yes Sub-pixel match locations Yes Yes
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LOCATING PARTS: COORDINATE SYSTEMS
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Region of Interest (ROI) • Region of Interest (ROI)
– a.k.a. Search Area – A portion of the image upon which an
image processing step may be performed.
– The object under inspection must always appear inside the defined ROI in order to extract measurements from that ROI.
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• ROIs need to be repositioned when the location of the part varies
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Coordinate Systems • Defined by a reference point (origin) and angle within the image, or by the
lines that make up its axes • Allows you to define search areas that can move around the image with
the object you are inspecting • Usually based on a characteristic feature of the object under inspection
– Use pattern match, edge detection or geometry tools to locate features – Use features to establish coordinate system
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Coordinate Systems – Set Up
1) Define an origin – Locate an easy-to-find feature in
your reference image. Feature must be stable from image to image.
– Set a coordinate system based on its location and orientation
2) Set up measurement ROIs in reference to the new origin – Acquire a new image – Locate reference point – Reposition measurement ROIs
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Class Organization
Enhance • Filter
noise or unwanted features
• Remove distortion
• Calibrate images
Check • Measure
intensity • Create
particles • Analyze
particles
Locate • Match
patterns • Match
geometry • Set-up
coordinate systems
Identify • Read text
(OCR) • Read 1D
barcodes • Read 2D
codes
Measure • Detect
edges • Measure
distance • Calculate
geometry
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Edge Detection Overview • Process of detecting transitions in an image • One of the most commonly used machine vision tools • Attractive because:
– Simple to understand and use – Localized processing – fast – Applicable to many applications
Tolerant to illumination changes
Different Edges
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1D Edge Detection Detect edge points along a line basic operation: 1) Get pixel values along the line 2) Compute gradient information 3) Find peaks and valleys (edge
locations) based on contrast, width, steepness….
4) Select edge(s) based on: – Order: first, last, first & last – Polarity: rising, falling – Score: best edge
Grad
ient
Val
ue
Pixe
l Val
ue
Position Along Line (pixels)
Position Along Line (pixels)
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Subpixel Accuracy
Subpixel location of edge can be computed using parabolic interpolation
Position Along Line (pixels)
Grad
ient
Val
ue
Subpixel Location
Parabolic Fit
Parabolic interpolation
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Edge Detector Tools
• Several high level edge tools are build on the single edge detectors
• Rake: – Used to find multiple edges and fit a
shape through them • Configurable search directions,
filtering options and sub-sampling ratios.
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Straight Edge (Line) Detection • Detect straight lines in an image
– Extension of 1D edge detection • Straight edge detection options:
– Rake-based – Projection-based – Hough transform-based
79 Projection-based find straight edge Locating multiple straight edges
Rake-based find straight edge
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Edge Detection Applications • Detect
Features • Alignment • Gauging • Inspection
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Application: Inspecting Parts
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Locating part using find straight edge Check for remnant plastic using intensity measurement
Check tips using pattern matching Check liquid level using find straight edge
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Application: Dimension Verification • Dimensional measurements, such as lengths, distance, and diameter
– Inline gauging inspections are used to verify assembly and packaging routines
– Offline gauging is used to judge product quality according to a sample
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Class Organization
Enhance • Filter
noise or unwanted features
• Remove distortion
• Calibrate images
Check • Measure
intensity • Create
particles • Analyze
particles
Locate • Match
patterns • Match
geometry • Set-up
coordinate systems
Identify • Read text
(OCR) • Read 1D
barcodes • Read 2D
codes
Measure • Detect
edges • Measure
distance • Calculate
geometry
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Identify
• 1D and 2D Codes • Marking methods • Reading • Examples
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1D Codes
• Applications using 1D bar codes have been around for over 35 years
• Barcode data is an index into a large central data storage
• Code is easily read by laser scanners • Low data capacity in large footprint
Code 3 of 9 Code 128 EAN 13
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2D Codes • Usually not an index into a database • Camera-based vision systems are preferred reading
method • High data capacity in small footprint
Data Matrix QR Code
PDF 417
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1D vs. 2D 1D Codes 2D Codes Low data capacity High data capacity Index into large database Self contained data Large footprint Small footprint Redundancy in Y dimension Error correction capability Readable by laser scanner Requires camera based reader Requires as much as 80% contrast Can be read in low contrast
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Optical Character Recognition (OCR)
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OCR/OCV
OCR/OCV: Optical Character Recognition/Verification: • Reads or verifies (printed) characters Typical steps: • Region of Interest around lines of text • Threshold • Character Segmentation • Compare to Library (classification) • Character is learned or recognized Optical Character Verification (OCV): • Compare character recognized against
golden reference
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Class Organization
Enhance • Filter
noise or unwanted features
• Remove distortion
• Calibrate images
Check • Measure
intensity • Create
particles • Analyze
particles
Locate • Match
patterns • Match
geometry • Set-up
coordinate systems
Identify • Read text
(OCR) • Read 1D
barcodes • Read 2D
codes
Measure • Detect
edges • Measure
distance • Calculate
geometry
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Contact Information
Nicolas Vazquez Principal Software Engineer
National Instruments 11500 N. Mopac Expwy Austin, Texas 78759 USA
Phone: +1 512-683-8494 Email: [email protected]
www.ni.com