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Halcon帮助文档classification
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Classifying an object means to assign an object to one of several available classes. When working with images, the objects usually are pixels or regions. Objects are described by features, which comprise, e.g., the color or texture for pixel objects, and the size or specific shape features for region objects. To assign an object to a specific class, the individual class boundaries have to be known. These are built in most cases by a training using the features of sample objects for which the classes are known. Then, when classifying an unknown object, the class with the largest correspondence between the feature values used for its training and the feature values of the unknown object is returned.
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Solution Guide II-D
Classification
HALCON 18.05 Progress
How to use classification, Version 18.05
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.
Copyright © 2008-2018 by MVTec Software GmbH, München, Germany
MVTec Software GmbH
Protected by the following patents: US 7,062,093, US 7,239,929, US 7,751,625, US 7,953,290, US 7,953,291, US 8,260,059,
US 8,379,014, US 8,830,229. Further patents pending.
Microsoft, Windows, Windows Server 2008/2012/2012 R2/2016, Windows 7/8/8.1/10, Microsoft .NET, Visual C++, and Visual
Basic are either trademarks or registered trademarks of Microsoft Corporation.
All other nationally and internationally recognized trademarks and tradenames are hereby recognized.
More information about HALCON can be found at: http://www.halcon.com/
About This Manual
In a broad range of applications classification is suitable to find specific objects or detect defects in images. This
Solution Guide leads you through the variety of approaches that are provided by HALCON.
After a short introduction to the general topic in section 1 on page 7, a first example is presented in section 2 on
page 11 that gives an idea on how to apply a classification with HALCON.
Section 3 on page 15 then provides you with the basic theories related to the available approaches. Some hints how
to select the suitable classification approach, a set of features or images that is used to define the class boundaries,
and some samples that are used for the training of the classifier are given in section 4 on page 29.
Section 5 on page 33 describes how to generally apply a classification for various objects like pixels or regions
based on various features like color, texture, or region features. Section 6 on page 63 shows how to apply classi-
fication for a pure pixel-based image segmentation and section 7 on page 81 provides a short introduction to the
classification for optical character recognition (OCR). For the latter regions are classified by region features.
Finally, section 8 on page 99 provides some general tips that may be suitable when working with complex classi-
fication tasks.
The HDevelop example programs that are presented in this Solution Guide can be found under the directory into
which the HDevelop example programs have been installed. The path to this directory can be determined with the
operator call get_system ('example_dir', ExampleDir).
Contents
1 Introduction 7
2 A First Example 11
3 Classification: Theoretical Background 15
3.1 Classification in General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2 Euclidean and Hyperbox Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.3 Multi-Layer Perceptrons (MLP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.4 Support-Vector Machines (SVM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.5 Gaussian Mixture Models (GMM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.6 K-Nearest Neighbors (k-NN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.7 Deep Learning (DL) and Convolutional Neural Networks (CNNs) . . . . . . . . . . . . . . . . . 23
4 Decisions to Make 29
4.1 Select a Suitable Classification Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.2 Select Suitable Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.3 Select Suitable Training Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5 Classification of General Features 33
5.1 General Approach (Classification of Arbitrary Features) . . . . . . . . . . . . . . . . . . . . . . . 33
5.2 Involved Operators (Overview) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
5.2.1 Basic Steps: MLP, SVM, GMM, and k-NN . . . . . . . . . . . . . . . . . . . . . . . . . 36
5.2.2 Basic Steps: Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.2.3 Advanced Steps: MLP, SVM, GMM, and k-NN . . . . . . . . . . . . . . . . . . . . . . . 38
5.2.4 Advanced Steps: Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5.3 Parameter Setting for MLP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5.3.1 Adjusting create_class_mlp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5.3.2 Adjusting add_sample_class_mlp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.3.3 Adjusting train_class_mlp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
5.3.4 Adjusting evaluate_class_mlp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
5.3.5 Adjusting classify_class_mlp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.4 Parameter Setting for SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.4.1 Adjusting create_class_svm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.4.2 Adjusting add_sample_class_svm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
5.4.3 Adjusting train_class_svm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.4.4 Adjusting reduce_class_svm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.4.5 Adjusting classify_class_svm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5.5 Parameter Setting for GMM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.5.1 Adjusting create_class_gmm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.5.2 Adjusting add_sample_class_gmm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5.5.3 Adjusting train_class_gmm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
5.5.4 Adjusting evaluate_class_gmm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.5.5 Adjusting classify_class_gmm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.6 Parameter Setting for k-NN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.6.1 Adjusting create_class_knn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.6.2 Adjusting add_sample_class_knn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.6.3 Adjusting train_class_knn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.6.4 Adjusting set_params_class_knn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
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