| 1. |
- Landström, Anders, et al.
(author)
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Adaptive Morphological Filtering of Incomplete Data
- 2014
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In: 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2013). - Piscataway, NJ : IEEE Communications Society. - 9781479921263
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Conference paper (peer-reviewed)abstract
- We demonstrate how known convolution techniques for uncertain data can be used to set the shapes of structuring elements in adaptive mathematical morphology, enabling robust morphological processing of partially occluded or otherwise incomplete data. Results are presented for filtering of both gray-scale images containing missing data and 3D profile data where information is missing due to occlusion effects. The latter demonstrates the intended use of the method: enhancement of crack signatures in a surface inspection system for casted steel.The presented method is able to disregard unreliable data in a systematic and robust way, enabling adaptive morphological processing of the available information while avoiding any false edges or other unwanted features introduced by the values of faulty pixels.
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| 2. |
- Landström, Anders, et al.
(author)
-
Image reconstruction by prioritized incremental normalized convolution
- 2011
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In: Image analysis. - Berlin : Encyclopedia of Global Archaeology/Springer Verlag. - 9783642212260 ; , s. 176-185
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Conference paper (peer-reviewed)abstract
- A priority-based method for pixel reconstruction and incrementalhole filling in incomplete images and 3D surface data is presented.The method is primarily intended for reconstruction of occluded areasin 3D surfaces and makes use of a novel prioritizing scheme, based on apixelwise defined confidence measure, that determines the order in whichpixels are iteratively reconstructed. The actual reconstruction of individualpixels is performed by interpolation using normalized convolution.The presented approach has been applied to the problem of reconstructing3D surface data of a rock pile as well as randomly sampled imagedata. It is concluded that the method is not optimal in the latter case,but the results show an improvement to ordinary normalized convolutionwhen applied to the rock data and are in this case comparable to thoseobtained from normalized convolution using adaptive neighborhood sizes.
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| 3. |
- Landström, Anders, et al.
(author)
-
Sub-millimeter crack detection in casted steel using color photometric stereo
- 2014
-
In: 2013 International Conference on Digital Image Computing Techniques and Applications (DICTA 2013. - Piscataway, NJ : IEEE Communications Society. - 9781479921287
-
Conference paper (peer-reviewed)abstract
- A novel method for automated inspection of small corner cracks in casted steel is presented, using a photometric stereo setup consisting of two light sources of different colors in conjunction with a line-scan camera. The resulting image is separated into two different reflection patterns which are used to cancel shadow effects and estimate the surface gradient. Statistical methods are used to first segment the image and then provide an estimated crack probability for each segmented region. Results show that true cracks are successfully assigned a high crack probability, while only a minor proportion of other regions cause similar probability values. About 80% of the cracks present in the segmented regions are given a crack probability higher than 70%, while the corresponding number for other non-crack regions is only 5%. The segmented regions contain over 70% of the manually identified crack pixels. We thereby provide proof-of-concept for the presented method.
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