Compensation method for the influence of angle of view on animal temperature measurement using thermal imaging camera combined with depth image.

In the study, we proposed an animal surface temperature measurement method based on Kinect sensor and infrared thermal imager to facilitate the screening of animals with febrile diseases. Due to random motion and small surface temperature variation of animals, the influence of the angle of view on temperature measurement is significant. The method proposed in the present study could compensate the temperature measurement error caused by the angle of view. Firstly, we analyzed the relationship between measured temperature and angle of view and established the mathematical model for compensating the influence of the angle of view with the correlation coefficient above 0.99. Secondly, the fusion method of depth and infrared thermal images was established for synchronous image capture with Kinect sensor and infrared thermal imager and the angle of view of each pixel was calculated. According to experimental results, without compensation treatment, the temperature image measured in the angle of view of 74° to 76° showed the difference of more than 2°C compared with that measured in the angle of view of 0°. However, after compensation treatment, the temperature difference range was only 0.03-1.2°C. This method is applicable for real-time compensation of errors caused by the angle of view during the temperature measurement process with the infrared thermal imager.

[1]  Veronica Redaelli,et al.  Thermography : current status and advances in livestock animals and in veterinary medicine , 2013 .

[2]  Colm P. O'Donnell,et al.  Applications of thermal imaging in food quality and safety assessment , 2010 .

[3]  Paul S. Morley,et al.  Thermographic Eye Temperature as an Index to Body Temperature in Ponies , 2011 .

[4]  P. Baranowski,et al.  Detection of early apple bruises using pulsed-phase thermography , 2009 .

[5]  Victor Alchanatis,et al.  Study on temporal variation in citrus canopy using thermal imaging for citrus fruit detection , 2008 .

[6]  E C Alexopoulos,et al.  Introduction to multivariate regression analysis. , 2010, Hippokratia.

[7]  K. Schwartzkopf-Genswein,et al.  Influence of environmental factors on infrared eye temperature measurements in cattle. , 2014, Research in veterinary science.

[8]  C. Gariépy,et al.  Ante-mortem detection of PSE and DFD by infrared thermography of pigs before stunning. , 1989, Meat science.

[9]  Christian Ammon,et al.  Monitoring the body temperature of cows and calves using video recordings from an infrared thermography camera , 2012, Veterinary Research Communications.

[10]  Craig Packer,et al.  Detection of foot-and-mouth disease virus infected cattle using infrared thermography , 2008, The Veterinary Journal.

[11]  Aminaton Marto,et al.  Indirect measure of thermal conductivity of rocks through adaptive neuro-fuzzy inference system and multivariate regression analysis , 2015 .

[12]  D. Jayas,et al.  Applications of Thermal Imaging in Agriculture and Food Industry—A Review , 2011 .

[13]  P. Verboven,et al.  Thermographic surface quality evaluation of apple , 2006 .

[14]  Christian Ammon,et al.  Assessment of body temperature in sows by two infrared thermography methods at various body surface locations , 2013 .

[15]  M. Litwa,et al.  Influence of Angle of View on Temperature Measurements Using Thermovision Camera , 2010, IEEE Sensors Journal.

[16]  G. Qu,et al.  Information measure for performance of image fusion , 2002 .

[17]  J. Ballester,et al.  Using the Xbox Kinect Sensor for Positional Data Acquisition , 2013 .

[18]  H. Pinkerton,et al.  Factors affecting the accuracy of thermal imaging cameras in volcanology , 2006 .

[19]  Lene Juul Pedersen,et al.  Determining the emissivity of pig skin for accurate infrared thermography , 2014 .

[20]  Allan L. Schaefer,et al.  Early Detection and Prediction of Infection using Infrared Thermography , 2004, Recent trends in Management and Commerce.

[21]  Shirley P. N. Cani,et al.  Influence of Field of View of Thermal Imagers and Angle of View on Temperature Measurements by Infrared Thermovision , 2014, IEEE Sensors Journal.

[22]  Jeff Dozier,et al.  Effect of Viewing Angle on the Infrared Brightness Temperature of Snow , 1982 .

[23]  Kosuke Imai,et al.  Multivariate Regression Analysis for the Item Count Technique , 2011 .

[24]  F. Marinello,et al.  Application of the Kinect sensor for dynamic soil surface characterization , 2015, Precision Agriculture.

[25]  Lorenzo Bruzzone,et al.  Image fusion techniques for remote sensing applications , 2002, Inf. Fusion.

[26]  Ke Zhiyong,et al.  Research on some influence factors in high temperature measurement of metal with thermal infrared imager , 2011 .

[27]  Ilan Halachmi,et al.  Automatic assessment of dairy cattle body condition score using thermal imaging , 2013 .

[28]  Xiangzhi Bai,et al.  Infrared and Visual Image Fusion through Fuzzy Measure and Alternating Operators , 2015, Sensors.

[29]  Ling Shao,et al.  Enhanced Computer Vision With Microsoft Kinect Sensor: A Review , 2013, IEEE Transactions on Cybernetics.

[30]  Wengang Zheng,et al.  Pixel-line based clustering for the 3D point cloud generated by Kinect depth map , 2013, 2013 IEEE International Conference on Information and Automation (ICIA).