Robust automated multiple view inspection

Pizarro, Luis; Mery, Domingo; Delpiano, Rafael; Carrasco, Miguel

doi:10.1007/s10044-007-0075-9

Cited by 18 publications

(6 citation statements)

References 19 publications

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“…They are, respectively, defined as the number of flaws that are correctly classified and the number of misclassified regular (7,8) in view 3, and (9, 10) in view 4). We observe that after tracking in 2, 3, and 4 views there are only two tracks in T 6 that could be tracked in 5 and 4 views, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…a original sequence, b keypoints, c classified keypoints, d detected monocular keypoints, e matched keypoints, f reprojected 3D points (blue) and neighbor keypoints (red), and g final detection Testing experiments were carried out by recognizing the three mentioned classes ('clips', 'springs', and 'razor blades') in 45 different sequences of 4 views (15 sequences for each class). 7 The size of an individual image was 1,430 × 900 pixels. In these experiments there were 30 clips, 75 springs, and 15 razor blades to be recognized.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Applications in X-ray Testing

Mery

2015

Computer Vision for X-Ray Testing

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In this chapter, relevant applications on X-ray testing are described. We cover X-ray testing in (i) castings, (ii) welds, (iii) baggage, (iv) natural products, and (v) others (like cargos and electronic circuits). For each application, the state of the art is presented. Approaches in each application are summarized showing how they use computer vision techniques. A detailed approach is shown in each application and some examples using Matlab are given in order to illustrate the performance of the methods.Cover image: 3D representation of the X-ray image of a wheel (X-ray image C0023_0001 colored with 'sinmap' colormap).

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Section: Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

Applications in X-ray Testing

Mery

2015

Computer Vision for X-Ray Testing

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“…Various approaches to automated flaw detection in aluminium castings can be found in [1], [3][4][5][6] . Those works proposed flaw detection methods without obtaining a priori information of flaws or the object's structure.…”

Section: Flaw Detection In Aluminium Die Castings Using Simultaneous mentioning

confidence: 99%

Flaw detection in aluminium die castings using simultaneous combination of multiple views

Pieringer¹,

Mery²

2010

Insight - Non-Destructive Testing and Condition Monitoring

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Recently, X-rays have been adopted as the principal nondestructive testing method to identify flaws within an object that are undetectable to the naked eye. Automatic inspection using radiographic images has been made possible by incorporating image processing techniques into the process. In a previous work, we proposed a framework to detect flaws in aluminium castings using multiple views. The process consisted of flaw segmentation, matching and finally tracking the flaws along the image sequence. While the previous approach required effective segmentation and matching algorithms, this investigation focuses on a new detection approach. The proposed method combines, simultaneously, information gathered from multiple views of the scene; this does not require searching for correspondences or matching. By gathering all the projections from a 3D point, obtained from a sliding box in the 3D space, we train a classifier to learn to detect simulated flaws using all the evidence available. This paper describes our proposed method and presents its performance record in flaw detections using various classifiers. Our approach yields promising results: 94% of true positives detected with 95% sensitivity in real flaws. We conclude that simultaneously combining information from different points of view is a robust approach to flaw identification.

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“…Kumar et al [17] presented a novel approach for multiclass weld flaw classification by means of a gray-level co-occurrence matrix-(GLCM-) based texture feature extraction technique. Previous work on a multiple-view detection method was conducted by Mery et al who introduced a method for precisely detecting defects by tracking and analyzing the correspondence between the different views [18][19][20]. Girshick et al [21] proposed a method for detection that extracted proposed regions from the input image and computed CNN features for classification.…”

Section: Introductionmentioning

confidence: 99%

Blowhole Detection Based on Bidirectional Enhancement and Omnidirectional Analysis for X‐Ray Inspection of Castings

Zhao

Liang

et al. 2019

Mathematical Problems in Engineering

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Small blurred blowholes are difficult to detect, especially those in castings having a complex structure. In this paper, we propose a blowhole detection method based on bidirectional enhancement and omnidirectional analysis for X-ray inspection of castings. After obtaining the X-ray images of a casting from the X-ray inspection equipment, there are two main processes to detect the blowholes. First, in the extraction process for the potential defect region, we propose a method based on bidirectional enhancement that highlights the blurred defects and models the nondefective structure. Second, in the precise defect identification process, we propose a method to distinguish real defects by 3D model fitting-based omnidirectional analysis. The experimental results indicate that the proposed method has a higher performance in blowhole detection than comparable methods.

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Robust automated multiple view inspection

Cited by 18 publications

References 19 publications

Applications in X-ray Testing

Applications in X-ray Testing

Flaw detection in aluminium die castings using simultaneous combination of multiple views

Blowhole Detection Based on Bidirectional Enhancement and Omnidirectional Analysis for X‐Ray Inspection of Castings

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