Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Civil Engineering

Image processing has been recently used to help controlling and monitoring many activities. This research integrates the image processing algorithms with 3D models to generate a system that is able to perform some of the monitoring activities by computers. Based on the created 3D model developed from stationary photos, an algorithms for material recognition is applied on separate images extracted from the model to identify the type of the structural element in the image. Then, different image denoising techniques are compared such as mean filter, median filter, Wiener filter and Split-Bregman iterations and the most efficient technique is implemented in the system. Subsequently, six different methods are used for image segmentation to separate the concerned object from the background; Color segmentation, region growing segmentation, histogram segmentation, local standard deviation segmentation, adaptive threshold segmentation and mean-shift cluster segmentation. This research proposes a system for defect detection and evaluation which is able to indicate the deformity positions and evaluate the defect in constructed building elements to support the subjective visual quality investigation of the durability of structural work and the aesthetics of an architectural work. This strategy depends on material recognition and defect feature analysis that evaluates the defect value in digital images using digital image processing methods

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