·Table of Contents ·Materials Characterization and testing | Improvement in automated Aluminum Casting Inspection by Finding Correspondence of Potential Flaws in Multiple Radioscopic ImagesDomingo Mery, Dieter FilbertInstitute for Measurement Technology and Control Engineering, Technical University of Berlin Einsteinufer 19, Sekr. E2 D-10587 Berlin, Germany Tel.: ++49 +30 314 73431, ++49 +30 314 22541 Fax: ++49 +30 314 25717 e-mail: domingo.mery@tu-berlin.de,dieter.filbert@tu-berlin.de www: http://ima.ee.tu-berlin.de Nejila Parspour YXLON X-Ray International GmbH Essener Str. 99, Gebäude 227 D-22419 Hamburg, Germany Tel.: ++49 +40 5 27 29-306 Fax: ++49 +40 5 27 29-371 e-mail: nejila.parspour@hbg.yxlon.com www: http://www.yxlon.de Contact |
1. INTRODUCTION
Fig 1: Classic image processing method to detect flaws in a casting. |
Fig 2: (a) Two flaws in a radioscopic image of an aluminum wheel (the circles were manually marked). (b) Detection of potential flaws using PXV5000 (see black regions). |
The paper is organized as follows. Section 2 describes the PXV 5000 algorithm to recognize potential flaws. The correspondence finding of potential flaws in multiple radioscopic images is given in Section 3. The results obtained on radioscopic images are shown in Section 4. Finally, our conclusions and suggestions are presented in Section 5.
Fig 3: Radioscopic sequence of an aluminum wheel. |
The recognition of potential flaws identifies regions in radioscopic images that may correspond to real defects. This process takes place in each radioscopic image of the sequence without considering the information about the correspondence between them. The method of detect recognition, namely the software PXV 5000, is illustrated in Fig. 4. The automatic defect recognition will be performed in several steps. A brief description of these steps is given below:
Fig 4: PXV-5000 algorithm to detect flaws. |
Grabbing:
Image grabbing digitizes the TV-input signal from the camera by using an image intensifier. A number of frames is averaged for noise reduction. Using a flat panel detector the detector will be exposed during the grabbing phase for a certain time needed to receive a low noise image. The image is digitized by flat panel itself.
Shrinking:
Depending on the adjusted image size the grabbed image is shrinked. The image size for standard processing is 256x256, for special processing it can be adjusted.
Registration:
The image registration measures the 2-dimensional position (translation) and the brightness of the image contents. A plausibility test against a reference image of the appropriate position is performed. The measured position deviation is taken into account for further processing steps.
Gray level Normalization:
The gray level normalization equalizes the resulting gray level contrast of a given defect in different regions (different thickness) of the object.
Filtering:
The filtering step emphasizes all image structures, which are suspected to be defects. An ideal filter would filter out defects only. But this is not possible, since irrelevant object structures (regular structures) similar to defects will be enhanced as well. Another dilemma is, that a filtering operator does not produce high output in cases where a real defect is superimposed by some complicated object structure. In order to solve these problems using PXV 5000 two types of filters are available: the so called standard filter, used for homogeneous objects, and special filters for non homogeneous objects with complicated structured areas. The standard filter has been developed at Philips Research Laboratories and will be applied by homogeneous areas. The special filters are based on classical image processing filters and will be used by non homogeneous highly structured areas. PXV 5000 allows the using of seven different filters within one image.
Masking:
The masking step suppresses all irrelevant image structures which lay outside of a processing region mask.
Segmentation/Threshholding:
The segmentation step produces 2 binary images by simple thresholding. The first is called the marker image. It marks all structures, which are suspected to be defects. The second binary image contains the structures with true shape.
Identification/Measuring:
The identification step identifies all structures with adjacent pixels. These are called segments. The segments are listed in a so called segment set. The measuring function measures features of all segments in the segment set. Features are: position, area, perimeter, Feret coordinates, compactness, elongation, minimum gray level, mean gray level, maximum gray level.
Matching:
The matching step removes all segments representing regular structures from the segment set. This is done by comparing of the detected segments with the features of segments which are collected in so called segment model. The segment model contains all segments caused by regular structures obtained during a learning phase.
Classification:
The classification step classifies the remaining segments in the segment set according to the inspection specification.
3.1 Matching in two views
Matching requires the position and the features that describe the properties of each detected potential flaw. As position we take the center of gravity of the region in the X-ray image. The used features are area, perimeter, circularity, average gray level and contrast. We match region r_{1} of image 1 with region r_{2} of image 2 (see Fig. 5), if they fulfill all following matching conditions:
Fig 5: Epipolar geometry in two views. |
Epipolar constraint:
the centers of gravity of the regions must satisfy the epipolar constraint. Epipolar geometry can be useful in finding correspondences between two projections: As shown in Fig. 5, the possible 3D points M that may have caused the projected point x_{1} in first image are on the beam (C_{1},x_{1}), where C_{1} is the location of X-ray source at first projection. Therefore, the corresponding points of x_{1} are located on the projection of this beam on image 2 by C_{2}, the location of X-ray source at second projection. The epipolar constraint is that, for a given a point x_{1} in image 1, its possible matches in image 2 must lie on the epipolar line of point x_{1} [2, 8].
Similarity condition:
the regions must be similar enough. To evaluate this criterion we calculate a degree of similarity as an Euclidean distance between the feature vectors of the regions. This degree of similarity of the regions must be small enough.
Correct location in 3D:
the 3D point reconstructed from the centers of gravity of the regions must belong to the space occupied by the casting. Using a linear triangulation technique [4] we estimate the corresponding 3D point , that may produce the coordinates of the centers of gravity of the regions. We examine if resides in the volume of the casting, which dimensions are usually a priori known (e.g. a wheel is assumed to be a cylinder).
3.2 Tracking in more views
In the tracking problem, it is required to find trajectories of regions in different views. The tracking in three views of a flaw using epipolar geometry is simple: if two corresponding points x_{1} and x_{2} are matched in image 1 and 2, the corresponding point x_{3} in image 3 must belong to the epipolar lines of x_{1} and x_{2} in the third image. Therefore, x_{3} belongs to the intersection of these two lines [10]. However, this approach is not well-defined, if the epipolar lines are equal. Additionally, the compute of the intersection cannot be done directly. Shashua introduced the trifocal tensors to solve the correspondence problem in three views linearly [12, 4]. Using trifocal tensors one can directly compute the coordinates of the third image corresponding point from the coordinates of the first two ones (reprojection).
Using the results of previous section and the trifocal tensors, we can establish the correspondence in three views if we seek all possible links of three regions (r_{1},r_{2},r_{3}) that satisfy the following trifocal conditions:
Fig 6a: Matching in 2 views. | Fig 6b: Tracking in 3 views. | Fig 6c: Tracking in 4 views and detection. |
Fig 6:Finding correspondence of potential flaws in the radioscopic sequence. |
Fig 7: False and real detections in each step. |
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