Comparison and evaluation of geometric calibration methods for infrared cameras to perform metric measurements on a plane.

Geometric information from infrared images can complement the information about the measured infrared radiation. However, this requires geometric camera calibration. In this work, three calibration methods for infrared cameras are compared: a direct and an iterative estimation of the transformation between image and world coordinates and a complete camera calibration method using a specifically designed calibration target. The three methods are compared and the obtained performance for metric measurements on a plane is evaluated. The results indicate acceptable performance in the three cases, with the complete method clearly outperforming the two others with an average error of only 0.060 mm, which represents 0.08% error of the measured distance.

Evaluation of calving indicators measured by automated monitoring devices to predict the onset of calving in Holstein dairy cows.

Dystocias are common in dairy cows and often adversely affect production, reproduction, animal welfare, labor, and economics within the dairy industry. An automated device that accurately predicts the onset of calving could potentially minimize the effect of dystocias by enabling producers to intervene early. Although many well-documented indicators can detect the imminence of calving, research is limited on their effectiveness to predict calving when measured by automated devices. The objective of this experiment was to determine if a decrease in vaginal temperature (VT), rumination (RT), and lying time (LT), or an increase in lying bouts (LB), as measured by 3 automated devices, could accurately predict the onset of calving within 24, 12, and 6 h. The combination of these 4 calving indicators was also evaluated. Forty-two multiparous Holstein cows housed in tie-stalls were fitted with a temperature logger inserted in the vaginal cavity 7±2 d before their expected calving date; VT was recorded at 1-min intervals. An ear-attached sensor recorded rumination time every hour based on ear movement while an accelerometer fitted to the right hind leg recorded cow position at 1-min intervals. On average, VT were 0.3±0.03°C lower, and RT and LT were 41±17 and 52±28 min lower, respectively, on the calving day compared with the previous 4 d. Cows had 2±1 more LB on the calving day. Of the 4 indicators, a decrease in VT≥0.1°C was best able to predict calving within the next 24 h with a sensitivity of 74%, specificity of 74%, positive and negative predictive values of 51 and 89%, and area under the curve of 0.80. Combining the indicators enhanced the performance to predict calving within the next 24, 12, and 6 h with best overall results obtained by combining the 3 devices for prediction within the next 24 h (sensitivity: 77%, specificity: 77%, positive and negative predictive values: 56 and 90%, area under the curve: 0.82). These results indicate that a device that could simultaneously measure these 4 calving indicators could not precisely determine the onset of calving, but the information collected would assist dairy farmers in monitoring the onset of calving.

Analysis of a new method of measurement and visualization of indoor conditions by infrared thermography.

We present a new tool of measurement and visualization of the indoor ambient parameters (air temperature, air speed, and mean radiant temperature) by infrared thermography. The theoretical fundamentals are discussed. They include a design stage, the mathematical modeling, the measurement procedure, and the performance evaluation through error analysis. The measuring system consists of a set of auxiliary devices (targets) arranged on a measurement grid. An infrared camera measures their temperature histories. From each temperature chronology, the three ambient parameters are locally deduced together by solving an inverse heat transfer problem. The results are then mapped to provide a 2D or 3D visualization. The question of identifiability is addressed leading to a robust parameter estimation algorithm. The robustness of the algorithm is tested for a wide range of noisy data during a numerical experiment. The numerical data are built by varying the air speed from 0 ms(-1) to 2 ms(-1) with a step of 0.2 ms(-1), the air temperature from 15 °C to 30 °C with a step of 3 °C and the mean radiant temperature from 15 °C to 30 °C with a step of 3 °C. It appears that stability and repeatability are guaranteed by the presented method for the range of ambient parameters and accuracy usually found and needed in indoor conditions. Brief illustrative experimental results are given as an initial validation of the method. Since the spatial distributions of these ambient parameters are obtained qualitatively and quantitatively, the method is suitable for indoor microclimate mapping, visualization of air patterns, building inspection, thermal comfort assessment, etc.

Automatic setup deviation measurements with electronic portal images for pelvic fields.

The purpose of this work was to develop a fully automatic tool for the detection of setup deviation for small pelvic field using, in external beam radiotherapy, an electronic portal imaging device (EPID). The algorithm processes electronic portal images of prostate cancer patients. No fiducial points or user interventions are needed. Deviation measurements are based on bone edge detection performed with Laplacian of a Gaussian (LoG) operator. Two bone edge images are then correlated, one of which is a reference image taken as the first fraction image for the purpose of this study. The electronic portal images (EPI) also show band artefacts which are removed using the morphological top-hat transform. The algorithm was first validated with 59 phantom images acquired in clinical treatment conditions with known displacements. The algorithm was then validated with 79 clinical images where bone contours were delineated manually. For the phantom images, the setup deviations were measured with a absolute mean error of 0.59 mm and 0.47 mm with a standard deviation of 0.64 mm and 0.42 mm, horizontally and vertically, respectively. A second validation was performed using clinical prostate cancer images. The measured patient displacements have an absolute mean error of 0.48 mm and 1.41 mm with a standard deviation of 0.58 mm and 1.30 mm in the X and Y directions, respectively. The algorithm execution time on a SUN workstation is 5 s. This algorithm shows good potential as a setup deviation measurement tool in clinical practice. The possibility of using this algorithm combined with decision rules based on statistical observations is very promising.