Infrared Image Enhancement and Segmentation for Extracting the Thermal Anomalies in Electrical Equipment

Thermal anomalies in electrical equipments can be extracted if its infrared image is segmented properly.  However, segmenting the infrared image is a very challenging task due to blur and low quality of the obtained images. So, it is very important to enhance the image prior to segmentation. This paper proposes an image enhancement method by adjusting the intensity of the image. Firstly, the intensity of the original image is inverted. Then, the warm region is enhanced by subtracting the original image with the inverted image. Experimental results through different segmentation algorithms show that by enhancing the infrared image using the proposed algorithm could improve the segmentation performance. Ill. 4, bibl. 16, tabl. 1 (in English; abstracts in English and Lithuanian). DOI: http://dx.doi.org/10.5755/j01.eee.120.4.1465

[1]  Ying Li,et al.  An Efficient Method for Target Extraction of Infrared Images , 2010, AICI.

[2]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[3]  D. Mery,et al.  Segmentation of colour food images using a robust algorithm , 2005 .

[4]  Charles Hellier,et al.  Handbook of Nondestructive Evaluation , 2001 .

[5]  R. A. Epperly,et al.  A tool for reliability and safety: predict and prevent equipment failures with thermography , 1997, Record of Conference Papers. IEEE Industry Applications Society 44th Annual Petroleum and Chemical Industry Conference.

[6]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[7]  Joseph N. Wilson,et al.  Handbook of computer vision algorithms in image algebra , 1996 .

[8]  Shuhong Yang,et al.  Infrared Electric Image Segmentation Using Fuzzy Renyi Entropy and Chaos Differential Evolution Algorithm , 2011, 2011 International Conference on Future Computer Sciences and Application.

[9]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[10]  James M. Keller,et al.  Fuzzy Models and Algorithms for Pattern Recognition and Image Processing , 1999 .

[11]  Zhang Yi,et al.  Infrared image transition region extraction and segmentation based on local definition cluster complexity , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[12]  Meritxell Bach Cuadra,et al.  Multimodal Evaluation for Medical Image Segmentation , 2007, CAIP.

[13]  Naser A Hamadani Automatic target cueing in IR imagery , 1981 .

[14]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..