Development of an Automatic Stencil Inspection System Using Modified Hough Transform and Fuzzy Logic

Choi, Kyung-Jin; Lee, Younghyun; Moon, Jong-Woo; Park, Chong-Kug; Harashima, Fumio

doi:10.1109/tie.2006.885494

Cited by 23 publications

(11 citation statements)

References 15 publications

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“…These presuppose that some features of the object or region are previously known: straights, circular objects, etc. These include projections and Hough transform [68] or its variants modified HT [69]. Other methods based on models include Markov random field [70] used in fabric inspection and autoregressive spectral analysis [34].…”

Section: Detection Methodsmentioning

confidence: 99%

A Public Fabric Database for Defect Detection Methods and Results

Silvestre-Blanes

Albero-Albero

Miralles

et al. 2019

Autex Research Journal

111

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The use of image processing for the detection and classification of defects has been a reality for some time in science and industry. New methods are continually being presented to improve every aspect of this process. However, these new approaches are applied to a small, private collection of images, which makes a real comparative study of these methods very difficult. The objective of this paper was to compile a public annotated benchmark, that is, an extensive set of images with and without defects, and make these public, to enable the direct comparison of detection and classification methods. Moreover, different methods are reviewed and one of these is applied to the set of images; the results of which are also presented in this paper.

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Section: Detection Methodsmentioning

confidence: 99%

A Public Fabric Database for Defect Detection Methods and Results

Silvestre-Blanes

Albero-Albero

Miralles

et al. 2019

Autex Research Journal

111

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“…Hough transform [9]- [11] is famous for its efficiency in detecting straight lines. Though its computation complexity increases tremendously for the curved lines, which limits its applications greatly especially in on-line application, its feasibility in identification of the fuzzy laser lines is still tested from the detected edge images as shown in Fig.…”

Section: Evaluation Of State Of Art Methodsmentioning

confidence: 99%

Automatic Segmentation of Fuzzy Laser Lines with Sub-Pixel Accuracy from the Uneven Background During Robotic Arc Welding

Wang¹

2016

2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS)

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Abstract-Robust and automatic segmentation of the reflected laser lines from the arc light modified background is prerequisite for the subsequent measurement of the weld pool shape which is of great importance for monitoring robotic arc welding. In this paper, the two dimensional intensity distribution caused by the arc light in the captured image is modeled. Based on the model, we propose an efficient and effective method that comprises three parts: (1), spline enhancement to achieve sub-pixel accuracy; (2), a gradient detection filter to eliminate the uneven background; (3), an effective threshold selection method. Experimental results verified that the proposed method is significantly superior to other state of art methods in segmentation accuracy.

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“…[1] presents a system to inspect metal stencil that is used to print solder paste on pads of PCBs. In [11], defects in tested PCBs are identified by feature comparison between the detected images and template images based on a linkage information table.…”

Section: B Previous Aoi Methodsmentioning

confidence: 99%

“…EFECT detection by Automatic Optical Inspection (AOI) is a crucial step during the manufacturing process of Printed Circuit Boards (PCB) [1], [2]. As already predicted in the late 80's [3]- [5], increasing circuit complexity, diversity of defects, economic considerations and computational requirements raise difficulties for complete PCB validation.…”

mentioning

confidence: 99%

Solder Paste Scooping Detection by Multilevel Visual Inspection of Printed Circuit Boards

Benedek

Krammer

Janóczki

et al. 2013

IEEE Trans. Ind. Electron.

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Abstract-In this paper we introduce an automated Bayesian visual inspection framework for Printed Circuit Board (PCB) assemblies, which is able to simultaneously deal with various shaped Circuit Elements (CE) on multiple scales. We propose a novel Hierarchical Multi Marked Point Process (H M MPP) model for this purpose, and demonstrate its efficiency on the task of solder paste scooping detection and scoop area estimation, which are important factors regarding the strength of the joints. A global optimization process attempts to find the optimal configuration of circuit entities, considering the observed image data, prior knowledge, and interactions between the neighboring CEs. The computational requirements are kept tractable by a data driven stochastic entity generation scheme. The proposed method is evaluated on real PCB data sets containing 125 images with more than 10.000 splice entities.

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Development of an Automatic Stencil Inspection System Using Modified Hough Transform and Fuzzy Logic

Cited by 23 publications

References 15 publications

A Public Fabric Database for Defect Detection Methods and Results

A Public Fabric Database for Defect Detection Methods and Results

Automatic Segmentation of Fuzzy Laser Lines with Sub-Pixel Accuracy from the Uneven Background During Robotic Arc Welding

Solder Paste Scooping Detection by Multilevel Visual Inspection of Printed Circuit Boards

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