Corrosion damage of industrial materials and artwork objects form an aspect of high importance
nowadays and gathers the interest of many researchers from different scientific fields. The main aim of these
research efforts is to extract reliable information on the extent and the types of degradation and thus to
propose techniques for effective reconstruction. A challenging issue in the field of corrosion damage
estimation is the development of non-destructive to the material evaluation methodologies.

The current work introduces a novel approach of deterioration damage analysis based on computer
vision techniques for non-destructive quantitative and qualitative evaluation of degradation effects on
stonework. Thus, we have developed various segmentation approaches each of which handling in a different
way the background in-homogeneities. The detection schemes, implemented in this work, aim at approaching
accurately the topology of corrosion patterns while preserving their shape and size features. Thus, methods of
adaptive thresholding, based on features of the local background, are initially employed while other
techniques that involve Region Growing segmentation or fusion of detection results are also tested. The
corrosion damage effects derived by the segmentation procedure are subsequently quantified by the means of
several statistical metrics. In this thesis we are also focused towards the performance evaluation and the
potential of segmentation processes in correctly detecting and localizing decay effects. A semi-automated
framework for validating the algorithms’ performance is thus developed. The framework implementation
includes image dataset depicting representative decay effects, ground truth overlays, and source code for
extracting ground truth matrixes and performance curves. This framework guarantees reliable and objective
estimation of segmentation algorithms’ performance while it allows informed experimental feedback for the
design of improved segmentation schemes. Further to exploiting the robust points of each segmentation
approach, this work also studies the corrosion mechanisms by investigating the way that degradation state is
reflected onto the size of the segmented decay areas and their relative intensities over the background. At the
final stage of this work we perform shape analysis on the segmented decay patterns. The analysis scheme is
mainly based on boundary information and aims at investigating the way that cleaning state/and or exposure
conditions are reflected on the segments’ shape features. Furthermore, through studying the decay patterns’
shape and in particular the existence of holes/and or nested regions within the body of the segmented areas
we can track the occurrence of specific degradation mechanisms. Shape features considered in combination
with size and intensity characteristics of degraded areas may aid the classification of corrosion damage.

Our detection methodologies and performance analysis framework is tested on a variety of images
capturing from micro- to macro-scale characteristics of corrosion damage. Thus, the current work involves an
examination of the limitations and the potential of various monitoring modalities to determine corrosion
damage. The experts inspect the entire detection procedure and performance evaluation and the derived
results proved to be in accordance with their own judgments and with previous chemical studies on the same
surfaces.