Cover page and Table of Contents: PDF (size: 265KB)
Full Text (PDF, 265KB), PP.20-26
Views: 0 Downloads: 0
Histogram Equalization, Contrast Stretching, Image Enhancement, Image Segmentation and Feature Extraction, Rough Fuzzy Set Techniques
Detection of affected areas in images is a crucial step in assessing the depth of the affected area for municipal operators. These affected areas in the underground images, which are line images are indicative of the condition of buried infrastructures like sewers and water mains. These images identify affected areas and extract their properties like structures from the images, whose contrast has been enhanced... A Centroid Model for the Depth Assessment of Images using Rough Fuzzy Set Techniques presents a three step method which is a simple, robust and efficient one to detect affected areas in the underground concrete images. The proposed methodology is to use segmentation and feature extraction using structural elements. The main objective for using this model is to find the dimensions of the affected areas such as the length, width, depth and the type of the defects/affected areas. Although human eye is extremely effective at recognition and classification, it is not suitable for assessing defects in images, which might have spread over thousands of miles of image lines. The reasons are mainly fatigue, subjectivity and cost. Our objective is to reduce the effort and the labour of a person in detecting the affected areas in underground images. A proposal to apply rough fuzzy set theory to compute the lower and upper approximations of the affected area of the image is made in this paper. In this connection we propose to use some concepts and technology developed by Pal and Maji.
P. Swarnalatha, B.K. Tripathy, "A Centroid Model for the Depth Assessment of Images using Rough Fuzzy Set Techniques", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.3, pp.20-26, 2012. DOI:10.5815/ijisa.2012.03.03
Dubois, D. and Prade, H.: Rough fuzzy sets and Fuzzy rough sets, International journal of General Systems, vol.17, no.1, (1990), pp. 191 – 209.
L. A. Zadeh..: Fuzzy Sets, Information and Control, 11, (1965), pp. 338 - 353.
L.G. Brown: A survey of image registration techniques, ACM Computing Survey, 24 (4), (1992), pp. 352–376.
M. Heath, S. Sarkar, T. Sanocki and K.W. Bowyer: A robust visual method for assessing the relative performance of edge-detection algorithms, IEEE Transactions Pattern Analysis, Mach. Intel, 19 (12) (1997), pp.1338–1359.
M. Osama and Tariq Shehab-Eldeen and, “Automated detection of surface defects in water and sewer images”, Automation in Construction, 8,(1999), pp.581-588.
Pawlak, Z.: Rough sets, International jour. of information and computer science, 11, (1982),pp.341 – 356.
P.Maji and S.K. Pal, “Rough Set Based Generalized Fuzzy C-Means Algorithm and Quantitative Indices”, IEEE Transactions on Systems, Man, and Cybernetics-Part B, Cybernetics, Vol.37, no.6, December,(2007),pp.1529-1540.
S.K.Sinha and Paul W. Fieguth, “Automated detection of cracks in buried concrete image images”, Automation in Construction, 15, (2006), pp.58-72.
S.K.Sinha and Paul W. Fieguth, “Morphological segmentation and classification of underground image images”, Machine Vision and Applications, 17(1), (2006), pp.21-31.
S.K.Sinha and P.W. Fieguth, “Segmentation of buried concrete image images”, Automation in Construction, 15, (2006), pp.47-57.
Z. Pawlak, Rough Sets, Theoretical Aspects of Reasoning about Data. Dordrecht, the Netherlands, Kluwer, 1991.