Automated detection of photogrammetric pipe features
Date
2018-03-16
Authors
Szabo, Jason Leslie
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Abstract
This dissertation presents original computer vision algorithms to automate the identification of piping and photogrammetric piping features in individual digital images of industrial installations.
Automatic identification of the pixel regions associated with piping is the core original element of this work and is accomplished through a re-representation of image information (light intensity versus position) in a light intensity versus gradient orientation data space. This work is based on the physics of scene illumination/reflectance and evaluates pixel regions in a hierarchy of data abstractions to identify pipe regions without needing specific information about pipe edges, illumination, or reflectance characteristics. The synthesis of correlated information used in this image segmentation algorithm provides a robust technique to identify potential pipe pixel regions in real images.
An additional unique element of this work is a pipe edge identification methodology, which uses the information from this light intensity versus gradient orientation data space to localize the pipe edge search space (in both a pixel position and gradient orientation sense). This localization provides a very specific, perspective independent, self-adaptive pipe edge filter. Pipe edges identified in this manner are then incorporated into a robust region joining algorithm to address the issue of region fragmentation (caused by occluding components and shadows).
Automated photogrammetric feature identification is also demonstrated through algorithmically recognizing the intersection of orthogonal pipe sections (with piping code acceptable diameter ratios) as potential T-junctions or 90-degree elbows. As pipe intersections, these image points are located with sub pixel resolution even though they cannot be identified by simple inspection.
The computer vision algorithms of this dissertation are robust physics based methods, applicable to the identification of piping and photogrammetric pipe features in real world images of industrial installations, being: perspective independent, albedo independent, and unaffected by inter-reflections. Automating these operator driven input tasks will improve the accuracy, ease-of-use, and cost effectiveness of implementing existing photogrammetric programs to the significant industrial problem of generating as-built piping drawings.
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Keywords
Computer vision, Photogrammetry