Assessment of mould growth on building materials using spatial and frequency domain analysis techniques
The phenomenon of Sick Building Syndrome (SBS), Building Related Illness (BRI) and some other indoor related diseases have been attributed to mould and fungi exposure in the indoor environment. Despite the growing concern over mould and fungi infestations on building materials, little has been repor...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IJCSNS
2009
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/1829/ http://irep.iium.edu.my/1829/ http://irep.iium.edu.my/1829/1/Assesment_of_Mould_Growth_on_Building_Materials_using_Spatial_and_frequency_Domain_technique.pdf |
| Summary: | The phenomenon of Sick Building Syndrome (SBS), Building Related Illness (BRI) and some other indoor related diseases have been attributed to mould and fungi exposure in the indoor environment. Despite the growing concern over mould and fungi infestations on building materials, little has been reported in the literature on the development of an objective tool and criteria for measuring and characterizing the shape and the level of severity of such parasitic phenomenon. In this paper, an objective based approach of mould and fungi growth assessment using spatial and frequency domain information is proposed. The spatial domain analysis of the acquired Mould Infested Images (MII) is achieved using Ratio Test (RT), Compactness Test (CT) and Visual Test (VT) while the frequency domain analysis uses the popular Discrete Fourier Transform (DFT) implemented in the form of Fast Fourier Transform (FFT) in analyzing the boundary pixel sequence. The resulting frequency components (Fourier Descriptors (FD)) can now be analyzed or stored for reconstruction purposes. Application of structural similarity measures on the reconstructed MII in spatial domain shows that the use of relative low number of FD is sufficient for analyzing, characterizing and reconstruction of the original spatial domain boundary pixels. |
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