Adaptive Image Segmentation Based on Fast Thresholding and Image Merging

Zhang, Ye; Qu, Hongsong; Wang, Yanjie

doi:10.1109/icat.2006.32

Cited by 17 publications

(9 citation statements)

References 8 publications

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“…The sub-sampled image is thresholded using an adaptive threshold level [3]. We divide the image in regions, each region being equal with the estimated size in pixels of the code.…”

Section: Data Matrix Localizationmentioning

confidence: 99%

Data Matrix Code — A reliable optical identification of microelectronic components

Dita

Oteşteanu

Quint

2011

2011 IEEE 17th International Symposium for Design and Technology in Electronic Packaging (SIITME)

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In automatic manufacturing and assembling of printed circuit boards (PCB) one needs to reliably identify the microelectronic components. To achieve this, we propose a method for identification of electronic and microelectronic components by marking each component with a Data Matrix Code (DMC). Using this code, the checking system can recognize the position of each component and by decoding, it can extract information about the type and the manufacturing of the microelectronic component. Because of the very small sizes of the components, because of in general critical illumination conditions and because of the low reflexivity of the black plastic packages of the components, the image processing task of finding the code and extracting the information is much more challenging than in the case of standard DMC printed with black ink on white paper. To cope with this situation we propose a new robust method for Data Matrix Code identification.

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“…The sub-sampled image is thresholded using an adaptive threshold level [3]. We divide the image in regions, each region being equal with the estimated size in pixels of the code.…”

Section: Data Matrix Localizationmentioning

confidence: 99%

Data Matrix Code — A reliable optical identification of microelectronic components

Dita

Oteşteanu

Quint

2011

2011 IEEE 17th International Symposium for Design and Technology in Electronic Packaging (SIITME)

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“…Using an adaptive threshold level the image is thresholded [3]. Depending by the estimated pattern size in pixels, the Gray image is divided in round( Image size Estimated pattern size ) regions, each region being equal with the estimated size in pixels of the code.…”

Section: Dmc Localizationmentioning

confidence: 99%

Scanning industrial Data Matrix Codes

Dita

Otesteanu

Quint

2011

2011 19thTelecommunications Forum (TELFOR) Proceedings of Papers

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In this paper we present a solution to locate the Data Matrix Codes dotted on different materials in different orientations. Starting from the parameters of the industrial camera of the recognition system and knowing the real world size of the Data Matrix pattern, the proposed method can find the exact position and the orientation of the Data Matrix pattern in an image. In order to be independent of illumination variations or nonuniform background, we use an adaptive threshold method for the image thresholding. It is very difficult to recognize the pattern because the Data Matrix pattern is composed only of dots and to overcome this, we are using morphological operators to transform the pattern in a solid square. The distance between modules as well as the size and the orientation of the Data Matrix pattern are estimated out of the image. We tested the proposed algorithm with very good results for Data Matrix Codes dotted on different materials and different angles.

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“…Any pixel (x, y) is considered as a part of object if its intensity is greater than or equal to threshold value i.e., f(x, y) ≥T, else, the pixel is considered as the background [9]. As per the selection of thresholding value, two types of thresholding methods are in existence [10], global and local thresholding. When T is constant, the approach is called global thresholding otherwise it is called local thresholding.…”

Section: Methodsmentioning

confidence: 99%

Tropical Rain Forest Monitoring and Evaluation using Image Segmentation Approach

Hamid¹,

Gobithaasan²,

Sulaiman³

et al. 2016

World Congress on Electrical Engineering and Computer Systems and Science

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-The tropical deforestation is now being tracked by actors in the nongovernmental, academic, private and government sectors using several different sources of satellite imagery. This paper proposes a satellite image analysis system that can be used for deforestation related examination and monitoring activities in the tropics. In this paper, the extraction of deforestation area based on the image grey level information and the adaptive threshold were obtained by Otsu threshold method. Then, the edge of the image is smoothen and dilated to outline the deforestation area. Finally we calculate the area of deforestation by summation of all the white pixel. Experiments show that the proposed method has acceptable accuracy rate which above 96% when compared with ground truth data.

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Adaptive Image Segmentation Based on Fast Thresholding and Image Merging

Cited by 17 publications

References 8 publications

Data Matrix Code — A reliable optical identification of microelectronic components

Data Matrix Code — A reliable optical identification of microelectronic components

Scanning industrial Data Matrix Codes

Tropical Rain Forest Monitoring and Evaluation using Image Segmentation Approach

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Adaptive Image Segmentation Based on Fast Thresholding and Image Merging

Cited by 17 publications

References 8 publications

Data Matrix Code &#x2014; A reliable optical identification of microelectronic components

Data Matrix Code &#x2014; A reliable optical identification of microelectronic components

Scanning industrial Data Matrix Codes

Tropical Rain Forest Monitoring and Evaluation using Image Segmentation Approach

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Data Matrix Code — A reliable optical identification of microelectronic components

Data Matrix Code — A reliable optical identification of microelectronic components