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    1 Guide to HALCON Methods
    1.1 Color Inspection
    1.2 Completeness Check
    1.3 Identification
    1.4 Measuring and Comparison 2D
    1.5 Measuring and Comparison 3D
    1.6 Object Recognition 2D
    1.7 Object Recognition 3D
    1.8 Position Recognition 2D
    1.9 Position Recognition 3D
    1.10 Print Inspection
    1.11 Quality Inspection
    1.12 Robot Vision
    1.13 Security System
    1.14 Surface Inspection
    1.15 Texture Inspection
    2 Image Acquisition
    2.1 Basic Concept
    2.1.1 Open Image Acquisition Device
    2.1.2 Acquire Image(s)
    2.1.3 Close Image Acquisition Device
    2.1.4 A First Example
    2.2 Extended Concept
    2.2.1 Open Image Acquisition Device
    2.2.2 Set Parameters
    2.2.3 Acquire Image(s)
    2.3 Programming Examples
    2.4 Selecting Operators
    2.4.1 Open Image Acquisition Device
    2.4.2 Set Parameters
    2.4.3 Acquire Image(s)
    2.4.4 Close Image Acquisition Device
    2.5 Tips & Tricks
    2.5.1 Direct Access to External Images in Memory
    2.5.2 Unsupported Image Acquisition Devices
    3 Region Of Interest
    3.1 Basic Concept
    3.1.1 Create Region
    3.1.2 Create ROI
    3.1.3 A First Example
    3.2 Extended Concept
    3.2.1 Segment Image(s)
    3.2.2 Draw Region
    3.2.3 Create Region
    3.2.4 Process Regions
    3.2.5 Align ROIs Or Images
    3.2.6 Create ROI
    3.2.7 Visualize Results
    3.3 Programming Examples
    3.3.1 Processing inside a User Defined Region
    3.3.2 Interactive Partial Filtering of an Image
    3.3.3 Inspecting the Contours of a Tool
    3.4 Selecting Operators
    3.4.1 Segment Image(s)
    3.4.2 Draw Region
    3.4.3 Create Region
    3.4.4 Process Regions
    3.4.5 Align ROIs Or Images
    3.4.6 Create ROI
    3.4.7 Visualize Results
    3.5 Relation to Other Methods
    3.6 Tips & Tricks
    3.6.1 Reuse ROI
    3.6.2 Effect of ROI Shape on Speed Up
    3.7 Advanced Topics
    3.7.1 Filter masks and ROIs
    3.7.2 Binary Images
    4 Blob Analysis
    4.1 Basic Concept
    4.1.1 Acquire Image(s)
    4.1.2 Segment Image(s)
    4.1.3 Extract Features
    4.1.4 A First Example
    4.2 Extended Concept
    4.2.1 Use Region Of Interest
    4.2.2 Align ROIs Or Images
    4.2.3 Rectify Image(s)
    4.2.4 Preprocess Image(s) (Filtering)
    4.2.5 Extract Segmentation Parameters
    4.2.6 Segment Image(s)
    4.2.7 Process Regions
    4.2.8 Extract Features
    4.2.9 Transform Results Into World Coordinates
    4.2.10 Visualize Results
    4.3 Programming Examples
    4.3.1 Crystals
    4.3.2 Atoms
    4.3.3 Analyzing Particles
    4.3.4 Extracting Forest Features from Color Infrared Image
    4.3.5 Checking a Boundary for Fins
    4.3.6 Bonding Balls
    4.3.7 Surface Scratches
    4.4 Selecting Operators
    4.4.1 Acquire Image(s)
    4.4.2 Use Region Of Interest
    4.4.3 Align ROIs Or Images
    4.4.4 Rectify Image(s)
    4.4.5 Preprocess Image(s) (Filtering)
    4.4.6 Extract Segmentation Parameters
    4.4.7 Segment Image(s)
    4.4.8 Process Regions
    4.4.9 Extract Features
    4.4.10 Transform Results Into World Coordinates
    4.4.11 Visualize Results
    4.5 Relation to Other Methods
    4.5.1 Methods that are Useful for Blob Analysis
    4.5.2 Methods that are Using Blob Analysis
    4.5.3 Alternatives to Blob Analysis
    4.6 Tips & Tricks
    4.6.1 Connected Components
    4.6.2 Speed Up
    4.7 Advanced Topics
    4.7.1 Line Scan Cameras
    4.7.2 High Accuracy
    5 1D Measuring
    5.1 Basic Concept
    5.1.1 Acquire Image(s)
    5.1.2 Create Measure Object
    5.1.3 Measure
    5.1.4 Destroy Measure Object
    5.2 Extended Concept
    5.2.1 Radiometrically Calibrate Image(s)
    5.2.2 Align ROIs Or Images
    5.2.3 Rectify Image(s)
    5.2.4 Create Measure Object
    5.2.5 Transform Results Into World Coordinates
    5.2.6 Visualize Results
    5.3 Programming Examples
    5.3.1 Inspecting a Fuse
    5.3.2 Inspect Cast Part
    5.3.3 Inspecting an IC Using Fuzzy Measuring
    5.3.4 Measuring Leads of a Moving IC
    5.3.5 Inspect IC
    5.4 Selecting Operators
    5.4.1 Acquire Image(s)
    5.4.2 Radiometrically Calibrate Image(s)
    5.4.3 Align ROIs Or Images
    5.4.4 Rectify Image(s)
    5.4.5 Create Measure Object
    5.4.6 Measure
    5.4.7 Transform Results Into World Coordinates
    5.4.8 Visualize Results
    5.4.9 Destroy Measure Object
    5.5 Relation to Other Methods
    5.5.1 Alternatives to 1D Measuring
    5.6 Tips & Tricks
    5.6.1 Suppress Clutter or Noise
    5.6.2 Reuse Measure Object
    5.6.3 Use an Absolute Gray Value Threshold
    5.7 Advanced Topics
    5.7.1 Fuzzy Measuring
    5.7.2 Evaluation of Gray Values
    6 Edge Extraction (Pixel-Precise)
    6.1 Basic Concept
    6.1.1 Acquire Image(s)
    6.1.2 Filter Image
    6.1.3 Extract Edges
    6.1.4 Process Edges
    6.1.5 A First Example
    6.2 Extended Concept
    6.2.1 Use Region Of Interest
    6.2.2 Filter Image
    6.2.3 Extract Edges
    6.2.4 Process Edges
    6.2.5 Visualize Results
    6.3 Programming Examples
    6.3.1 Aerial Image Interpretation
    6.3.2 Segmenting a Color Image
    6.4 Selecting Operators
    6.4.1 Acquire Image(s)
    6.4.2 Use Region Of Interest
    6.4.3 Filter Image
    6.4.4 Extract Edges
    6.4.5 Process Edges
    6.4.6 Visualize Results
    6.5 Relation to Other Methods
    6.5.1 Alternatives to Edge Extraction (Pixel-Precise)
    6.6 Tips & Tricks
    6.6.1 Speed Up
    7 Edge Extraction (Subpixel-Precise)
    7.1 Basic Concept
    7.1.1 Acquire Image(s)
    7.1.2 Extract Edges Or Lines
    7.1.3 A First Example
    7.2 Extended Concept
    7.2.1 Radiometrically Calibrate Image(s)
    7.2.2 Use Region Of Interest
    7.2.3 Extract Edges Or Lines
    7.2.4 Determine Contour Attributes
    7.2.5 Process XLD Contours
    7.2.6 Transform Results Into World Coordinates
    7.2.7 Visualize Results
    7.3 Programming Examples
    7.3.1 Measuring the Diameter of Drilled Holes
    7.3.2 Angiography
    7.4 Selecting Operators
    7.4.1 Acquire Image(s)
    7.4.2 Radiometrically Calibrate Image(s)
    7.4.3 Use Region Of Interest
    7.4.4 Extract Edges Or Lines
    7.4.5 Determine Contour Attributes
    7.4.6 Process XLD Contours
    7.4.7 Transform Results Into World Coordinates
    7.4.8 Visualize Results
    7.5 Relation to Other Methods
    7.5.1 Alternatives to Edge Extraction (Subpixel-Precise)
    8 Contour Processing
    8.1 Basic Concept
    8.1.1 Create XLD Contours
    8.1.2 Process XLD Contours
    8.1.3 Perform Fitting
    8.1.4 Extract Features
    8.1.5 A First Example
    8.2 Extended Concept
    8.2.1 Create XLD Contours
    8.2.2 Process XLD Contours
    8.2.3 Perform Fitting
    8.2.4 Transform Results Into World Coordinates
    8.2.5 Extract Features
    8.2.6 Convert And Access XLD Contours
    8.2.7 Visualize Results
    8.3 Programming Examples
    8.3.1 Measuring Lines and Arcs
    8.3.2 Close gaps in a contour
    8.3.3 Extract Roads
    8.4 Selecting Operators
    8.4.1 Create XLD Contours
    8.4.2 Process XLD Contours
    8.4.3 Perform Fitting
    8.4.4 Transform Results Into World Coordinates
    8.4.5 Extract Features
    8.4.6 Convert And Access XLD Contours
    8.4.7 Visualize Results
    8.5 Relation to Other Methods
    8.5.1 Alternatives to Contour Processing
    8.6 Advanced Topics
    8.6.1 Line Scan Cameras
    9 Matching
    9.1 Basic Concept
    9.1.1 Acquire Image(s)
    9.1.2 Create (Train) Model
    9.1.3 Find Model
    9.1.4 Destroy Model
    9.1.5 A First Example
    9.2 Extended Concept
    9.2.1 Radiometrically Calibrate Image(s)
    9.2.2 Rectify Image(s)
    9.2.3 Use Region Of Interest
    9.2.4 Determine Training Parameters
    9.2.5 Create (Train) Model
    9.2.6 Find Model
    9.2.7 Visualize Results
    9.3 Programming Examples
    9.3.1 Creating a Model for the ``Green Dot''
    9.3.2 Locating ``Green Dots''
    9.3.3 Distinguishing coins
    9.3.4 Locate Components on a PCB
    9.3.5 Check the State of a Dip Switch
    9.3.6 Locating a Pipe Wrench in Different States
    9.3.7 Creating a Mosaic Image
    9.3.8 Locate Brochure Pages
    9.3.9 Locate Road Signs
    9.4 Selecting Operators
    9.4.1 Acquire Image(s)
    9.4.2 Radiometrically Calibrate Image(s)
    9.4.3 Rectify Image(s)
    9.4.4 Use Region Of Interest
    9.4.5 Determine Training Parameters
    9.4.6 Create (Train) Model
    9.4.7 Find Model
    9.4.8 Visualize Results
    9.4.9 Destroy Model
    9.5 Relation to Other Methods
    9.5.1 Methods that are Using Matching
    9.5.2 Alternatives to Matching
    9.6 Tips & Tricks
    9.6.1 Speed Up
    9.7 Advanced Topics
    9.7.1 High Accuracy
    9.7.2 Use Timeout
    10 3D Matching
    10.1 Basic Concept
    10.1.1 Access 3D Object Model
    10.1.2 Create Approach-Specific 3D Model
    10.1.3 Destroy 3D Object Model
    10.1.4 Acquire Search Data
    10.1.5 Find Approach-Specific 3D Model
    10.1.6 Destroy Approach-Specific 3D Model
    10.1.7 A First Example
    10.2 Extended Concept
    10.2.1 Inspect 3D Object Model
    10.2.2 Inspect Approach-Specific 3D Model
    10.2.3 Re-use Approach-Specific 3D Model
    10.2.4 Use Region Of Interest
    10.2.5 Visualize Results
    10.3 Programming Examples
    10.3.1 Recognize 3D Clamps and Their Poses in Images
    10.3.2 Recognize Pipe Joints and Their Poses in a 3D Scene
    10.4 Selecting Operators
    10.4.1 Access 3D Object Model
    10.4.2 Inspect 3D Object Model
    10.4.3 Create Approach-Specific 3D Model
    10.4.4 Destroy 3D Object Model
    10.4.5 Inspect Approach-Specific 3D Model
    10.4.6 Re-use Approach-Specific 3D Model
    10.4.7 Acquire Search Data
    10.4.8 Use Region Of Interest
    10.4.9 Find Approach-Specific 3D Model
    10.4.10 Visualize Results
    10.4.11 Destroy Approach-Specific 3D Model
    10.5 Relation to Other Methods
    10.5.1 Alternatives to 3D Matching
    11 Variation Model
    11.1 Basic Concept
    11.1.1 Acquire Image(s)
    11.1.2 Create Variation Model
    11.1.3 Align ROIs Or Images
    11.1.4 Train Variation Model
    11.1.5 Prepare Variation Model
    11.1.6 Compare Variation Model
    11.1.7 Destroy Variation Model
    11.1.8 A First Example
    11.2 Extended Concept
    11.2.1 Check Model Quality
    11.2.2 Clear Training Data
    11.2.3 Visualize Results
    11.3 Programming Examples
    11.3.1 Inspect a Printed Logo Using a Single Reference Image
    11.3.2 Inspect a Printed Logo under Varying Illumination
    11.4 Selecting Operators
    11.4.1 Acquire Image(s)
    11.4.2 Create Variation Model
    11.4.3 Align ROIs Or Images
    11.4.4 Train Variation Model
    11.4.5 Check Model Quality
    11.4.6 Prepare Variation Model
    11.4.7 Clear Training Data
    11.4.8 Compare Variation Model
    11.4.9 Visualize Results
    11.4.10 Destroy Variation Model
    12 Classification
    12.1 Basic Concept
    12.1.1 Acquire Image(s)
    12.1.2 Create Classifier
    12.1.3 Train Classifier
    12.1.4 Classify Data
    12.1.5 Destroy Classifier
    12.1.6 A First Example
    12.2 Extended Concept
    12.2.1 Train Classifier
    12.2.2 Re-use Training Samples
    12.2.3 Re-use Classifier
    12.2.4 Evaluate Classifier
    12.2.5 Visualize Results
    12.3 Programming Examples
    12.3.1 Inspection of Plastic Meshes via Texture Classification
    12.3.2 Classification with Overlapping Classes
    12.4 Selecting Operators
    12.4.1 Acquire Image(s)
    12.4.2 Create Classifier
    12.4.3 Train Classifier
    12.4.4 Re-use Training Samples
    12.4.5 Re-use Classifier
    12.4.6 Evaluate Classifier
    12.4.7 Classify Data
    12.4.8 Visualize Results
    12.4.9 Destroy Classifier
    12.5 Relation to Other Methods
    12.5.1 Methods that are Useful for Classification
    12.5.2 Methods that are Using Classification
    12.5.3 Alternatives to Classification
    12.6 Tips & Tricks
    12.6.1 OCR for General Classification
    12.7 Advanced Topics
    12.7.1 Selection of Training Samples
    13 Color Processing
    13.1 Basic Concept
    13.1.1 Acquire Image(s)
    13.1.2 Decompose Channels
    13.1.3 Process Image (Channels)
    13.1.4 A First Example
    13.2 Extended Concept
    13.2.1 Demosaick Bayer Pattern
    13.2.2 Transform Color Space
    13.2.3 Train Colors
    13.2.4 Use Region Of Interest
    13.2.5 Classify Colors
    13.2.6 Compose Channels
    13.2.7 Visualize Results
    13.3 Programming Examples
    13.3.1 Robust Color Extraction
    13.3.2 Sorting Fuses
    13.3.3 Completeness Check of Colored Game Pieces
    13.3.4 Inspect Power Supply Cables
    13.3.5 Locating Board Components by Color
    13.4 Selecting Operators
    13.4.1 Acquire Image(s)
    13.4.2 Demosaick Bayer Pattern
    13.4.3 Decompose Channels
    13.4.4 Transform Color Space
    13.4.5 Train Colors
    13.4.6 Use Region Of Interest
    13.4.7 Process Image (Channels)
    13.4.8 Classify Colors
    13.4.9 Compose Channels
    13.4.10 Visualize Results
    13.5 Tips & Tricks
    13.5.1 Speed Up
    13.6 Advanced Topics
    13.6.1 Color Edge Extraction
    13.6.2 Color Line Extraction
    14 Texture Analysis
    14.1 Basic Concept
    14.1.1 Acquire Image(s)
    14.1.2 Apply Texture Filter
    14.1.3 Compute Features
    14.1.4 A First Example
    14.2 Extended Concept
    14.2.1 Rectify Image(s)
    14.2.2 Scale Down Image(s)
    14.2.3 Use Region Of Interest
    14.2.4 Align ROIs Or Images
    14.2.5 Apply Texture Filter
    14.2.6 Compute Features
    14.2.7 Visualize Results
    14.2.8 Use Results
    14.3 Programming Examples
    14.3.1 Detect Defects in a Texture with Novelty Detection
    14.3.2 Detect Defects in a Web Using Dynamic Thresholding
    14.3.3 Classification of Different Types of Wood
    14.4 Selecting Operators
    14.4.1 Acquire Image(s)
    14.4.2 Rectify Image(s)
    14.4.3 Scale Down Image(s)
    14.4.4 Use Region Of Interest
    14.4.5 Align ROIs Or Images
    14.4.6 Apply Texture Filter
    14.4.7 Compute Features
    14.4.8 Visualize Results
    14.4.9 Use Results
    14.5 Relation to Other Methods
    14.5.1 Methods that are Using Texture Analysis
    14.6 Advanced Topics
    14.6.1 Fast Fourier Transform (FFT)
    14.6.2 Texture Analysis in Color Images
    14.7 More Information About Texture Features
    14.7.1 Entropy and Anisotropy ([file:../reference/reference_hdevelop.pdf]entropy_gray)
    14.7.2 Cooccurrence Matrix ([file:../reference/reference_hdevelop.pdf]gen_cooc_matrix)
    14.7.3 Features of the Cooccurrence Matrix
    14.8 More Information About Texture Filtering
    14.8.1 The Laws Filter ([file:../reference/reference_hdevelop.pdf]texture_laws)
    15 Bar Code
    15.1 Basic Concept
    15.1.1 Acquire Image(s)
    15.1.2 Create Bar Code Model
    15.1.3 Read Bar Code(s)
    15.1.4 Destroy Bar Code Model
    15.1.5 A First Example
    15.2 Extended Concept
    15.2.1 Use Region Of Interest
    15.2.2 Preprocess Image(s)
    15.2.3 Rectify Image(s)
    15.2.4 Create Bar Code Model
    15.2.5 Adjust Bar Code Model
    15.2.6 Read Bar Code(s)
    15.2.7 Check Print Quality
    15.2.8 Visualize Results
    15.3 Programming Examples
    15.3.1 How to Read Difficult Barcodes
    15.3.2 Reading a Bar Code on a CD
    15.3.3 Checking Bar Code Print Quality
    15.4 Selecting Operators
    15.4.1 Acquire Image(s)
    15.4.2 Use Region Of Interest
    15.4.3 Preprocess Image(s)
    15.4.4 Rectify Image(s)
    15.4.5 Create Bar Code Model
    15.4.6 Adjust Bar Code Model
    15.4.7 Read Bar Code(s)
    15.4.8 Destroy Bar Code Model
    15.4.9 Check Print Quality
    15.4.10 Visualize Results
    15.5 Relation to Other Methods
    15.5.1 Alternatives to Bar Code
    15.6 Advanced Topics
    15.6.1 Use Timeout
    16 Data Code
    16.1 Basic Concept
    16.1.1 Acquire Image(s)
    16.1.2 Create Data Code Model
    16.1.3 Read Data Code(s)
    16.1.4 Destroy Data Code Model
    16.1.5 A First Example
    16.2 Extended Concept
    16.2.1 Acquire Image(s)
    16.2.2 Rectify Image(s)
    16.2.3 Create Data Code Model
    16.2.4 Optimize Model
    16.2.5 Train Model
    16.2.6 Use Region Of Interest
    16.2.7 Read Data Code(s)
    16.2.8 Inspect Data Code(s)
    16.2.9 Check Print Quality
    16.2.10 Visualize Results
    16.3 Programming Examples
    16.3.1 Training a Data Code Model
    16.3.2 Reading 2D Data Codes on Chips
    16.4 Selecting Operators
    16.4.1 Acquire Image(s)
    16.4.2 Rectify Image(s)
    16.4.3 Create Data Code Model
    16.4.4 Optimize Model
    16.4.5 Train Model
    16.4.6 Use Region Of Interest
    16.4.7 Read Data Code(s)
    16.4.8 Inspect Data Code(s)
    16.4.9 Check Print Quality
    16.4.10 Visualize Results
    16.4.11 Destroy Data Code Model
    16.5 Advanced Topics
    16.5.1 Use Timeout
    17 OCR
    17.1 Basic Concept
    17.1.1 Acquire Image(s)
    17.1.2 Segment Image(s)
    17.1.3 Train OCR
    17.1.4 Read Symbol
    17.1.5 Destroy Classifier
    17.1.6 A First Example
    17.2 Extended Concept
    17.2.1 Use Region Of Interest
    17.2.2 Align ROIs Or Images
    17.2.3 Rectify Image(s)
    17.2.4 Preprocess Image(s) (Filtering)
    17.2.5 Extract Segmentation Parameters
    17.2.6 Segment Image(s)
    17.2.7 Train OCR
    17.2.8 Read Symbol
    17.2.9 Visualize Results
    17.3 Programming Examples
    17.3.1 Generating a Training File
    17.3.2 Creating and Training an OCR Classifier
    17.3.3 Reading Numbers
    17.3.4 "Best Before" Date
    17.3.5 Reading Engraved Text
    17.3.6 Reading Forms
    17.3.7 Segment and Select Characters
    17.3.8 Syntactic and Lexicon-Based Auto-Correction of OCR Results
    17.4 Selecting Operators
    17.4.1 Acquire Image(s)
    17.4.2 Use Region Of Interest
    17.4.3 Align ROIs Or Images
    17.4.4 Rectify Image(s)
    17.4.5 Preprocess Image(s) (Filtering)
    17.4.6 Extract Segmentation Parameters
    17.4.7 Segment Image(s)
    17.4.8 Train OCR
    17.4.9 Read Symbol
    17.4.10 Visualize Results
    17.4.11 Destroy Classifier
    17.5 Relation to Other Methods
    17.5.1 Alternatives to OCR
    17.6 Tips & Tricks
    17.6.1 Composed Symbols
    17.7 Advanced Topics
    17.7.1 Line Scan Cameras
    17.7.2 Circular Prints
    17.7.3 OCR Features
    17.8 Pretrained OCR Fonts
    17.8.1 Nomenclature for the Ready-to-Use OCR Fonts
    17.8.2 Ready-to-Use OCR Font 'Document'
    17.8.3 Ready-to-Use OCR Font 'DotPrint'
    17.8.4 Ready-to-Use OCR Font 'HandWritten_0-9'
    17.8.5 Ready-to-Use OCR Font 'Industrial'
    17.8.6 Ready-to-Use OCR Font 'MICR'
    17.8.7 Ready-to-Use OCR Font 'OCR-A'
    17.8.8 Ready-to-Use OCR Font 'OCR-B'
    17.8.9 Ready-to-Use OCR Font 'Pharma'
    17.8.10 Ready-to-Use OCR Font 'SEMI'
    18 Stereo Vision
    18.1 Basic Concept
    18.1.1 Calibrate Stereo Camera System
    18.1.2 Acquire Image(s)
    18.1.3 Rectify Image(s)
    18.1.4 Reconstruct 3D Information
    18.2 Extended Concept
    18.2.1 Use Region Of Interest
    18.2.2 Transform Results Into World Coordinates
    18.2.3 Visualize Results
    18.3 Programming Examples
    18.3.1 Segment the Components of a Board With Binocular Stereo
    18.3.2 Reconstruct the Surface of Pipe Joints With Multi-View Stereo
    18.4 Selecting Operators
    18.4.1 Calibrate Stereo Camera System
    18.4.2 Acquire Image(s)
    18.4.3 Rectify Image(s)
    18.4.4 Use Region Of Interest
    18.4.5 Reconstruct 3D Information
    18.4.6 Transform Results Into World Coordinates
    18.4.7 Visualize Results
    18.5 Relation to Other Methods
    18.5.1 Methods that are Using Stereo Vision
    18.6 Tips & Tricks
    18.6.1 Speed Up
    18.7 Advanced Topics
    18.7.1 High Accuracy
    19 Visualization
    19.1 Basic Concept
    19.1.1 Handling Graphics Windows
    19.1.2 Displaying
    19.1.3 A First Example
    19.2 Extended Concept
    19.2.1 Handling Graphics Windows
    19.2.2 Displaying
    19.2.3 Mouse Interaction
    19.2.4 Gnuplot
    19.3 Programming Examples
    19.3.1 Displaying HALCON data structures
    19.4 Selecting Operators
    19.4.1 Handling Graphics Windows
    19.4.2 Displaying
    19.4.3 Mouse Interaction
    19.4.4 Gnuplot
    19.5 Tips & Tricks
    19.5.1 Saving Window Content
    19.5.2 Execution Time
    19.6 Advanced Topics
    19.6.1 Programming Environments
    19.6.2 Flicker-Free Visualization
    19.6.3 Remote Visualization
    19.6.4 Programmed Visualization
    Index
    Building Vision for BusinessMVTec Software GmbHSolution Guide IBasics
    How to use HALCON’s machine vision methods, Version 10.0 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without prior written permission of the publisher. Edition 1 Edition 1a Edition 1b Edition 2 Edition 2a Edition 2b Edition 3 June 2007 October 2007 April 2008 December 2008 June 2009 March 2010 October 2010 (HALCON 8.0) (HALCON 8.0.1) (HALCON 8.0.2) (HALCON 9.0) (HALCON 9.0.1) (HALCON 9.0.2) (HALCON 10.0) Copyright © 2007-2010 by MVTec Software GmbH, München, Germany Protected by the following patents: US 7,062,093, US 7,239,929. Further patents pending. Microsoft, Windows, Windows XP, Windows 2003, Windows Vista, Windows 7, Microsoft .NET, Visual C++, Visual Basic, and ActiveX are either trademarks or registered trademarks of Microsoft Corpora- tion. All other nationally and internationally recognized trademarks and tradenames are hereby recognized. More information about HALCON can be found at: http://www.halcon.com/ MVTec Software GmbH
    Contents 1 Guide to HALCON Methods 2 Image Acquisition 3 Region Of Interest 4 Blob Analysis 5 1D Measuring 6 Edge Extraction (Pixel-Precise) 7 Edge Extraction (Subpixel-Precise) 8 Contour Processing 9 Matching 10 3D Matching 11 Variation Model 12 Classification 13 Color Processing 14 Texture Analysis 15 Bar Code 16 Data Code 17 OCR 18 Stereo Vision 19 Visualization 5 13 19 29 47 61 71 81 95 125 141 153 171 187 209 231 243 273 283
    Index 295
    Guide to HALCON Methods 5 s d o h t e M o t i e d u G Chapter 1 Guide to HALCON Methods This manual introduces you to important machine vision methods. To guide you from your specific application to the sections of the documentation to read, this section lists common application areas and the methods used for them. Generally, a lot of applications use the following methods: • Image Acquisition on page 13 for accessing images via an image acquisition device or via file. • Visualization on page 283 for the visualization of, e.g., artificially created images or results of an image processing task. • Region of interest on page 19 for reducing the search space for a following image processing task. • Morphology (Reference Manual, chapter “Morphology”), e.g., for the elimination of small gaps or protrusions from regions or from structures in gray value images. Other methods are more specific and thus are suited for specific application areas. Additionally, some application areas are part of another application area. To make the relations more obvious, for the following application areas the corresponding methods and related application areas are listed: • Color Inspection (page 6) • Completeness Check (page 6) • Identification (page 6) • Measuring and Comparison 2D (page 7) • Measuring and Comparison 3D (page 7) • Object Recognition 2D (page 8) • Object Recognition 3D (page 8)
    6 Guide to HALCON Methods • Position Recognition 2D (page 8) • Position Recognition 3D (page 9) • Print Inspection (page 9) • Quality Inspection (page 10) • Robot Vision (page 10) • Security System (page 10) • Surface Inspection (page 11) • Texture Inspection (page 11) 1.1 Color Inspection For color inspection, see the descriptions for Color Processing on page 171. 1.2 Completeness Check Completeness checks can be realized by different means. Common approaches are: • Object and position recognition 2D/3D (see page 8 ff), which is suitable, e.g., when inspecting objects on an assembly line. • Variation Model on page 141, which compares images containing similar objects and returns the difference between them considering a certain tolerance at the object’s border. 1.3 Identification Dependent on the symbols or objects you have to identify, the following methods are suitable: • Identify symbols or characters – Bar Code on page 209 – Data Code on page 231 – OCR on page 243 • Identify general objects – Object and position recognition 2D/3D (see page 8 ff)
    1.4 Measuring and Comparison 2D 7 s d o h t e M o t i e d u G 1.4 Measuring and Comparison 2D For measuring 2D features in images, several approaches are available. In the Solution Guide III-B, sec- tion 4.1 on page 29, a graph leads you from the specific features you want to measure and the appearance of the objects in the image to the suitable measuring approach. Generally, the following approaches are common: • Blob Analysis on page 29 for objects that consist of regions of similar gray value, color, or texture. • Contour Processing on page 81 for objects that are represented by clear-cut edges. The contours can be obtained by different means: – Edge Filtering on page 61 if pixel precision is sufficient. – Edge and Line Extraction on page 71 if subpixel precision is needed. • Matching on page 95 for objects that can be represented by a template. Matching comprises different approaches. For detailed information about matching see the Solution Guide II-B. • 1D Measuring on page 47 if you want to obtain the positions, distances, or angles of edges that are measured along a line or an arc. More detailed information can be found in the Solution Guide III-A. 1.5 Measuring and Comparison 3D For measuring in 3D, the following approaches are available: • The approaches used for measuring and comparison 2D (see page 7) in combination with a camera calibration (see Solution Guide III-C, section 3.2 on page 40) for measuring objects that are viewed by a single camera and that lie in a single plane. • Pose estimation (Solution Guide III-C, chapter 4 on page 75) for the estimation of the poses of 3D objects that are viewed by a single camera and for which knowledge about their 3D model (e.g., known points, known circular or rectangular shape) is available. 3D reconstruction is an important subcategory of 3D measuring and comprises the following methods: • Stereo for measuring in images obtained by a binocular or multi-view stereo system on page 273. Further information can be found in the Solution Guide III-C, chapter 5 on page 99. • Laser triangulation using the sheet-of-light technique (Solution Guide III-C, chapter 6 on page 133) for measuring height profiles of an object by triangulating the camera view with a projected light line. • Depth from focus (Solution Guide III-C, chapter 7 on page 151) for getting depth information from a sequence of images of the same object but with different focus positions. • Photometric Stereo (Reference Manual, chapter “3D Reconstruction . Photometric Stereo”) for getting information about an object’s shape because of its shading behavior (e.g., by the operator phot_stereo).
    8 Guide to HALCON Methods 1.6 Object Recognition 2D For finding specific objects in images, various methods are available. Common approaches comprise: • Blob Analysis on page 29 for objects that are represented by regions of similar gray value, color, or texture. • Contour Processing on page 81 for objects that are represented by clear-cut edges. The contours can be obtained by different means: – Edge Filtering on page 61 if pixel precision is sufficient. – Edge and Line Extraction on page 71 if subpixel precision is needed. • Matching on page 95 for objects that can be represented by a template. Matching comprises different approaches. For detailed information about matching see the Solution Guide II-B. • Classification on page 153 for the recognition of objects by a classification using, e.g., Gaussian mixture models, neural nets, or support vector machines. For more detailed information about classification see the Solution Guide II-D. • Color Processing on page 171 for the recognition of objects that can be separated from the back- ground by their color. • Texture Analysis on page 187 for the recognition of objects that can be separated from the back- ground by their specific texture. • Movement detection (see section 1.13 on page 10) for the recognition of moving objects. 1.7 Object Recognition 3D For the recognition of 3D objects that are described by a 3D Computer Aided Design (CAD) model, see the descriptions for 3D Matching on page 125. For the recognition of planar objects that can be oriented arbitrarily in the 3D space, see the descriptions for perspective, deformable matching and descriptor- based matching in the chapter about Matching on page 95 for the uncalibrated case and in the Solution Guide III-C, chapter 4 on page 75 for the calibrated case. 1.8 Position Recognition 2D In parts, the approaches for measuring 2D features are suitable to get the position of objects. In the Solution Guide III-B, section 4.1 on page 29, a graph leads you from several specific features, amongst others the object position, and the appearance of the objects in the image to the suitable approach. For position recognition, in particular the following approaches are common: • Blob Analysis on page 29 for objects that consist of regions of similar gray value, color, or texture.
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