Method of temperature measurement using image based on GRNN

Author

Hui, Liu ; Yun-sheng, Zhang ; Shuai, Wang

Author_Institution

Fac. of Mater. & Metall. Eng., Kunming Univ. of Sci. & Technol., Kunming, China

fYear

2009

fDate

17-19 June 2009

Firstpage

2992

Lastpage

2996

Abstract

As the temperature changing in the tubular furnace, the color of the reflect light from the tubular furnace will change also, so a method of temperature measurement using image based on generalized regression neural network (GRNN) is proposed. Firstly, the original color images were segmented and smoothed. Then the calculation of pixel´s R, G, B value of each image divided by the pixels number of the furnace mouth and its results are set to input vectors of GRNN network. GRNN is used to approximate the nonlinear relationship between temperature and the color value of each image. Finally, GRNN network is used to forecast the temperature, and compared with the results of BP network. The experimental results show that it is high speed and accuracy to apply GRNN to the method of temperature measurement using image.

Keywords

backpropagation; image segmentation; neural nets; regression analysis; temperature measurement; BP network; GRNN; generalized regression neural network; nonlinear relationship; temperature measurement; tubular furnace; Automation; Color; Electronic mail; Furnaces; Image segmentation; Inorganic materials; Materials science and technology; Neural networks; Pixel; Temperature measurement; BP Neural Network; Filter; GRNN; Image Segmentation; Temperature Measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2009. CCDC '09. Chinese

Conference_Location

Guilin

Print_ISBN

978-1-4244-2722-2

Electronic_ISBN

978-1-4244-2723-9

Type

conf

DOI

10.1109/CCDC.2009.5192279

Filename

5192279