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Baru, V. B.
- Leakage Detection on Building Façade using Thermal Image
Authors
1 Department of E &TC, Sinhgad College of Engineering, Pune, University of Pune, IN
2 Dept of E &TC, Sinhgad College of Engineering, Pune, IN
Source
Digital Image Processing, Vol 4, No 14 (2012), Pagination: 812-818Abstract
The goal of this research is to detect leakage on building facade using thermal image. Defining regions of interest precisely is one of the important problems in thermal image processing. Once the boundaries of the investigated objects are calculated, thermal parameters, such as average temperature, standard deviation, histograms, etc. can be easily obtained. The main aim is to find image features that describe leakage of building facade thermographs and to use these parameters to classify thermal image of building facade with and without leakage. Feature extraction from image and feature matching with stored features in feature vector are two key steps to find out leakage on building facade automatically. There are many different methods that describe image features. The basic feature consider is the edge, Detection of edge is a terminology in image processing and computer vision particularly in the areas of feature detection and extraction to refer to the algorithms which aims at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities. The need of edge detection is to find the discontinuities in depth, discontinuities in surface orientation, changes in material properties and variations in scene illumination. In this, a new edge extraction approach is presented, which is consisted of Canny edge detector. Secondly, there are large group of methods which are based on statistical parameters calculations. Parameters like mean value, standard deviation, skewness, kurtosis, energy, entropy etc. can be used to compare thermal images. Statistical features are calculated by using simple image processing techniques. Lastly region properties of an image such as area, centroids, and perimeters are considered. Based on the values of these features of a digital thermal image, we have made an attempt to classify the image in to two basic categories like normal image without leakage and defective image with leakage.
