Shape-based matching: Defect inspection of glue process in vision system

Haniff, H. M.; Sulaiman, Marizan; Shah, Hairol Nizam Mohd; Teck, Lim Wee

doi:10.1109/isiea.2011.6108771

Cited by 12 publications

(7 citation statements)

References 9 publications

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“…Image information supporting production lines or solving special tasks independently and thoroughly are getting more and more involved into the industrial processes of many manufacturers. Nowadays, tasks like completeness checks (Haniff et al, 2011), inspections (Molleda et al, 2013), (pre-)selections of construction elements (Rodrigues et al, 2012) or quality tests (Gunasekaran, 1996) are already fulfilled using the potential of robust machine vision systems operating in real time. The required parameters are directly determined by the MVS and then transferred for further processing or evaluation.…”

Section: Related Workmentioning

confidence: 99%

“…Shape-based matching can be performed using two-or three-dimensional approaches. 2D methods can be found in (Haniff et al, 2011), (Xu et al, 2008) or (Fan et al, 2014) and are, in principle, suitable to achieve sub-pixel accuracies. 3D approaches determining the pose of an object like reported in (Ulrich et al, 2009) or (Reinbacher et al, 2010) have advantages over 2D methods but may lead to insufficient results according to real time capability or accuracy for specific tasks.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Determination of Steering Wheel Angles During Car Alignment by Image Analysis Methods

Mueller

Voegtle

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Optical systems for automatic visual inspections are of increasing importance in the field of automation in the industrial domain. A new application is the determination of steering wheel angles during wheel track setting of the final inspection of car manufacturing. The camera has to be positioned outside the car to avoid interruptions of the processes and therefore, oblique images of the steering wheel must be acquired. Three different approaches of computer vision are considered in this paper, i.e. a 2D shape-based matching (by means of a plane to plane rectification of the oblique images and detection of a shape model with a particular rotation), a 3D shape-based matching approach (by means of a series of different perspectives of the spatial shape of the steering wheel derived from a CAD design model) and a point-to-point matching (by means of the extraction of significant elements (e.g. multifunctional buttons) of a steering wheel and a pairwise connection of these points to straight lines). The HALCON system (HALCON, 2016) was used for all software developments and necessary adaptions. As reference a mechanical balance with an accuracy of 0.1° was used. The quality assessment was based on two different approaches, a laboratory test and a test during production process. In the laboratory a standard deviation of ±0.035° (2D shape-based matching), ±0.12° (3D approach) and ±0.029° (point-to-point matching) could be obtained. The field test of 291 measurements (27 cars with varying poses and angles of the steering wheel) results in a detection rate of 100% and ±0.48° (2D matching) and ±0.24° (point-to-point matching). Both methods also fulfil the request of real time processing (three measurements per second).

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Determination of Steering Wheel Angles During Car Alignment by Image Analysis Methods

Mueller

Voegtle

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The experimental results show that SH FCM algorithm contributes to fast processing time compared to conventional FCM [5]. Conversely, studied made by [6], presents a system to inspect defect during glue process using the shape-based matching technique. Three defects are classified which are the main defect, gap defect, bumper defect and bubble defect.…”

Section: Introductionmentioning

confidence: 99%

Shape Defect Detection for Product Quality Inspection and Monitoring System

Saad¹,

Rahman²,

Abdullah³

et al. 2017

EECSI

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This paper presents an automated computer vision system of shape defect detection for product quality inspection and monitoring system. Soft drink bottle is used as a tested product for the proposed system. The analysis framework includes data collection, pre-processing, morphological operation, feature extraction, and classification. Morphological operation technique is used to segment the image of the bottle via erosion and dilation process. Through this technique, the defect in the bottle structure is described from the feature set such as area, perimeter, major axis length and extend. Then, the bottle is classified either it is pass or rejects from the estimated parameters using Naïve Bayes classifier. The results have proven that the proposed system can be applied to differentiate bottle according to shape with 100% accuracy using 100 samples.

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“…There are many techniques that provide a solution in recognizing image or object in image processing such as region [1], edge-based features [2], feature extraction [3], shape context [4], Gaussian Curve [5] and etc. The work of image recognition based on HALCON application for shape-based matching is done in [6][7][8][9][10]. The researches discussed about the process involved in basic shape based matching algorithm together with Extended Region of Interest (ROI) function available in HALCON that fulfils shape based matching to find object based on a single model image with sub pixel accuracy.…”

Section: Introductionmentioning

confidence: 99%

The investigation on defect recognition system using gaussian smoothing and template matching approach

Harun

Yaakub

Abidin

et al. 2020

IJEECS

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<p>This paper investigates various approaches for automated inspection of gluing process using shape-based matching application. A new supervised defect detection approach to detect a class of defects in gluing application is proposed. Creating of region of interest in important region of object is discussed. Gaussian smoothing features is proposed in determining better image processing. Template matching in differentiates between reference and tested image are proposed. This scheme provides high computational savings and results in high defect detection recognition rate. The defects are broadly classified into three classes: 1) gap defect; 2) bumper defect; 3) bubble defect. This system does lessen execution time, yet additionally produce high precision in deformity location rate. It is discovered that the proposed framework can give precision at 95.77% recognition rate in recognizing imperfection for gluing application.</p>

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Shape-based matching: Defect inspection of glue process in vision system

Cited by 12 publications

References 9 publications

Determination of Steering Wheel Angles During Car Alignment by Image Analysis Methods

Determination of Steering Wheel Angles During Car Alignment by Image Analysis Methods

Shape Defect Detection for Product Quality Inspection and Monitoring System

The investigation on defect recognition system using gaussian smoothing and template matching approach

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