Localization of Designed Texture on Glass Bottles

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    Localization of Designed Texture on Glass Bottles Page 1

    Technical report, IDE1032, June 2010

    Localization of designed texture

    On glass bottles

    Master Thesis in Intelligent Systems

    Abhishek Sharma Pallav Nandi Chaudhari

    850326-1250 850908-T314

    School of Information Science, Computer and Electrical Engineering

    Halmstad University

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    Localization of Designed Texture on Glass Bottles

    Preface

    This treatise is a masters thesis for the Master of Science program in intelligent systems at

    the Halmstad University, Sweden in the IDE department. This thesis deals with the

    localization of designed texture on glass bottles at the hot end. This is a research issue from

    the company Gedevelop located at Helsingborg, Sweden. The work is carried out with the

    guidance of Mr. Kenneth Nilsson. The data for the experiments are provided by Gedevelop.

    This project is implemented in the Matlab programming language on a personal computer

    system and experimental results on the proposed method are presented.

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    Localization of Designed Texture on Glass Bottles

    Abstract

    Hot end inspection of glass bottles necessitates the localization of designed textures

    on that bottle. In this paper we proposed a method to localize designed textures on a test

    bottle by matching its image to a certain number of reference images and then mapping the

    texture to the test bottle. The intended purpose of this algorithm is to localize the designed

    texture on the bottle so that the designed texture areas and other areas on the image of the

    bottle can be distinguished. The localization is done by finding the amount of rotation

    caused due to the motion of the conveyor belt. The average orientation angle feature is

    used in the matching. Reference databases are created which contain necessary information

    regarding the different textured regions. A selected portion of the image is used to match

    the bottle at the hot end with the same area of the reference bottles. The design texture is

    marked, according to the appropriate match. All the methods involved experiments and

    results and conclusions based upon them are presented subsequently.

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    Contents

    1 Introduction 1

    2. Background.... 4

    2.1 Real world scenario and their impact.. 5

    2.2 Texture recognition and localization..7

    2.2.1 Direct template matching7

    2.2.2 Feature based matching7

    2.3 Propose methods.. 8

    2.3.1 Reference model 8

    2.3.2 Computations on test image.9

    2.3.3 Limitations. .10

    2.3.3 Hot spot marking.11

    3. Method12

    3.1 Feature Extraction12

    3.1.1Dominant local direction...13

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    3.2 Skew detection and correction and target area

    extraction. 15

    3.2.1 Skew detection. 15

    3.2.2 Target area extraction..16

    3.3 Similarity Measure..22

    3.4 Hot spot marking.23

    4. Experiments and results..24

    4.1 Software environment..24

    4.2 Data24

    4.3Feature Extraction28

    4.4 Registration.31

    4.5 Matching32

    4.6 Hot Spot Marking.36

    5. Conclusions..38

    References.40

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    1

    1 IntroductionFault detection is an essential part of the glass manufacturing process. Different types of

    faults occur in glass containers during the manufacturing process [9]. The glass containers

    are always inspected by automatic systems. Sometimes human inspection is also done [14].

    Most of the inspection is done at the cold end of the manufacturing process. Cold end is a

    relatively time consuming process and fault detection is an independent part of this phase.

    The company Gedevelop[11] is looking for some method which can detect faults at the hot

    end phase, because hot end inspection may give some advantages over regular cold end

    inspection as an early warning of problems. Gedevelop is a system development company

    which develops and maintains systems for glass manufacturing plants. They have their

    camera system which can take pictures of bottles at the hot end and they are interested in

    finding manufacturing defects. For detection of faults by the analysis of images, it is

    necessary that the system must be able to differentiate between designed texture and

    textures made out of defects. One precise way of doing this is by localizing the designed

    texture on the glass containers. Once it islocalized, it will be easier to find the areas on the

    bottle where inspection should be made for defects.

    Hot end phase puts several constraints on the task of fault detection. The environment at

    the hot end is not a very friendly environment in which to perform an experiment.

    Traditional fault detection equipments cannot work there; no kind of handling of the bottles

    is possible. One possible way of inspection is vision based systems. There are also some

    limitations imposed by the situation. Multiple numbers of cameras cannot be used. The glass

    containers come very fast on the conveyor belt from the molding machine and there is very

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    2

    limited gap available between two bottles on the conveyor belt. Space is very limited for

    fitting multiple numbers of cameras so, normally, only one camera can look at a bottle at a

    certain time.

    In vision based systems, one of the possible ways to detect different kinds of faults

    e.g. cracks, foreign inclusions, etc, can be inspected by texture analysis. In that case, there

    must be a classification methodwhich can identify the designed texture on the bottles, so

    that the designed texture and textures made out of faults can be differentiated.

    In the identification of designed texture on glass bottles at the hot end, there are

    certain factors which, to a certain extent, provide the opportunity for it to be modeled in an

    easier way, because of its conceptual relevance to some very efficient texture recognition

    algorithms, such asoptical character recognition [2, 12]. On the other hand, the real world

    situation makes it difficult by imposing some constraints upon the image capturing task.

    Practically, when the bottle comes onto the conveyor belt from the forming machine, the

    camera can look only at one side of the bottle, which implies that the whole texture area on

    the bottle will not be visible to the camera. Due to the high speed of the belt, the bottle may

    be rotated to a certain degree. Because of this rotation, the same texture area will not

    always be captured by the camera.Keeping in mind all these points, here, we propose a

    method to localize the designed texture by finding the rotation angle of the bottle by

    matching it to certain number of references and then mapping thetexture to the test bottle.

    The method is passive in nature, because it does not directly extract the designedtexture.

    In our proposed method, the image pixels are represented by their average directions

    extracted from linear symmetric features [4] in a local neighborhood. Rather than using

    Matching procedure to find the texture directly, we used matching to find the amount of

    rotation. After finding the amount of rotation with respect to a reference image, the

    designed texture is marked according to the matching reference.

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    3

    This treatise is composed as follows:

    In chapter 2, the background, an overview of the real world situation in the present

    context and motivation for the proposed procedures, is explained. In chapter 3, the

    methods involved are discussed in detail, whereas in chapter 4 and chapter 5, the

    experiments and their results, and their future scopes, are described.

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    2 BackgroundTexture recognition is an image processing technique which has a wide variety of

    applications in different scientific research areas e .g. optical character recognition[2], face

    recognition[15] fingerprint recognition[1][8], biometrical identification of laboratory

    animals[13], medical image analysis[6], robot navigation [16]etc. For human eyes it is