NIST Technical Note 1630 

Evaluation of Image Quality of 

Thermal Imagers used by 

the Fire Service 

Francine Amon 

Andrew Lock 

NIST Technical Note 1630 

Evaluation of Image Quality of 

Thermal Imagers used by 

the Fire Service 

 

Francine Amon 

Andrew Lock 

Building and Fire Research Laboratory 

National Institute of Standards and Technology 

February, 2009 

U.S. Department of Commerce 

Carlos M. Gutierrez, Secretary 

National Institute of Standards and Technology 

Patrick D. Gallagher, Deputy Director 
Certain commercial entities, equipment, or materials may be identified in this 

 document in order to describe an experimental procedure or concept adequately. Such 

identification is not intended to imply recommendation or endorsement by the 

National Institute of Standards and Technology, nor is it intended to imply that the 

entities, materials, or equipment are necessarily the best available for the purpose. 

National Institute of Standards and Technology Technical Note 1630 

Natl. Inst. Stand. Technol. Tech. Note 1630, 36 pages (February 2009) 

CODEN: NSPUE2 
1 

ABSTRACT 

This document reports on the test results of an evaluation of the image quality of thermal 

imaging cameras used by the fire service. The test methods used were consistent with the image 

quality test methods included in Draft National Fire Protection Association (NFPA) 1801 

Standard on Thermal Imagers for the Fire Service. These tests measured the nonuniformity, 

spatial resolution, effective temperature range, and thermal sensitivity of fire service thermal 

imagers. Images used for these tests were collected using a high resolution visible camera 

focused on the thermal imager’s display while the thermal imager viewed a variety of thermal 

targets. The laboratory test results were evaluated in terms of a multivariate model of human 

perception, which was based on tests conducted on firefighters viewing thermal images of 

representative scenes in which a fire hazard may be present. 

1. INTRODUCTION 

Five different fire service thermal imagers were evaluated by the National Institute of Standards 

and Technology (NIST) Fire Research Division using performance metrics and testing protocols 

that were developed specifically for evaluating thermal imaging cameras utilized by firefighters, 

many of which have subsequently been integrated into Draft NFPA 1801 Standard on Thermal 

Imagers for the Fire Service [1]. The performance metrics for these tests were Field of View 

(FOV), Nonuniformity (NU), Spatial Resolution (SR), Effective Temperature Range (ETR), and 

Thermal Sensitivity (TS). The thermal imagers tested were considered “black boxes” in the 

sense that a target was placed in the field of view and the resulting image that appeared on the 

thermal imager’s display was captured by a high resolution Nikon D3*

 visible camera and 

processed using MATLAB software; the performance of the individual imaging components of 

the thermal imager was not measured. The methods of measurement and the results of these 

limited tests are presented in this report. The results of each individual test will be presented in 

the following five sections. 

While these objective laboratory test methods were being developed, a parallel effort was made 

to investigate the appropriate pass/fail criteria to apply to them. Work was performed in 

collaboration with the US Army’s Night Vision and Electronic Sensors Directorate (NVESD) in 

which the quality of thermal images common to firefighting applications was related to the 

ability of firefighters to perform a task. The task was to view a series of thermal images that had 

been degraded in specific ways and identify a potential fire hazard by clicking on it. The 

contrast (C), brightness (B), spatial resolution (R), and noise (N) of these images were each 

degraded to five different levels and the effects of these degraded factors on the test subject’s 

ability to perform the task were analyzed separately and in combination. It was determined that 

the interactions between the C, B, R, and N factors were at least as important as these factors 

were individually to the firefighters’ ability to identify fire hazards. In order to capture these 

important interactions in the pass/fail criteria, a multivariate model was created from the 

 

*

 Certain companies and commercial properties are identified in this paper in order to specify 

adequately the source of information or of equipment used. Such identification does not imply 

endorsement or recommendation by the National Institute of Standards and Technology, nor 

does it imply that this source or equipment is the best available for the purpose. 
2 

perception test results and is used in Section 2.2 to evaluate the imaging performance of the 

thermal imagers. An image quality indicator (P) is introduced to predict the probability of 

successfully performing the fire hazard identification task based on the combination of image 

quality factors (C, B, R, and N) measured in the images collected from the thermal imagers 

undergoing laboratory tests. In this way, human perception is used to determine the quality of 

the image while the imagers are tested using objective test methods. 

2. PERFORMANCE METRICS AND TEST METHODS 

General specifications of the imagers tested are listed in Table 1. In some cases, imagers were 

not subjected to the full gamut of tests due to testing equipment failures. With respect to sensor 

type, the imagers have Amorphous Silicon (ASi) or Vanadium Oxide (VOx) heat sensing 

material. 

 

2.1 Field of View 

The field of view (FOV) for most of the thermal imagers was simply measured by placing a stiff 

plastic pointer along the centerline of the thermal imager’s lens and positioning the camera such 

that the centerline of the outer surface of the lens was in the center of a protractor. The thermal 

imager was rotated until the edge of a thermal target placed 1 m away appeared to just touch the 

right edge of the displayed image. The angular position on the protractor indicated by the 

pointer was recorded. The thermal imager was then rotated about the center of the protractor 

until the same edge of the thermal target appeared to just touch the left edge of the displayed 

image. The angular position on the protractor indicated by the pointer was again recorded. The 

horizontal FOV is equal to the sum of the left and right circumscribed angles. This process was 

performed three times for each imager and averaged to determine the horizontal FOV. The same 

procedure was conducted with the thermal imager rotated 90 degrees about the axis of its optical 

system to determine the vertical FOV. For Imager 2, which was tested at a later date than the 

other imagers, a rotating optical platform was used in the same way as described above, using 

angular markings inscribed on the platform rather than the plastic pointer. The results are 

provided in Table 2. 

The FOV was not included in the image quality perception tests conducted at NVSED because it 

is simply a measurement of the viewing angle of the thermal imager and thus does not directly 

affect image quality. Indirectly, image quality is related to the level of detail that is discernable 

Table 1. General specifications of thermal imagers tested. 

Imager Focal Plane Array Sensor 

1 320 x 240 ASi 

2 320 x 240 VOx 

3 160 x 120 ASi 

4 320 x 240 VOx 

5 80 x 60 VOx 
3 

within the field of view, and this is captured by measuring spatial resolution, which is an 

important factor in image quality and is measured in both the laboratory tests and the human 

perception tests. Also, the FOV does affect firefighter performance in that a narrower (versus 

wider) FOV limits the situational awareness of a firefighter that uses the imager to scan or 

navigate a smoke-filled room. 

The information provided in Table 2 shows that the design of thermal imager optical systems 

varies significantly, even in cases where the same sensing material and detector array size are 

used. The percent difference in average (combined vertical and horizontal) FOV between Imager 

1 and Imager 5 is 75 %. 

2.2 Nonuniformity 

Nonuniformity (NU) is a measure of the thermal imager’s response to a uniform thermal target [2, 

3]. A common malady of thermal imaging is the phenomenon whereby the individual 

subcomponents (pixels) of the thermal detector drift away from their initial values over time due 

to minor variations in the sensing material and electronic circuitry. To compensate for this drift, 

some types of imagers employ a shutter that closes over the detector and momentarily blocks 

incoming radiation, effectively resetting all the pixels to the ambient temperature of the shutter. 

The shutter is activated according to proprietary algorithms; this process is termed a 

nonuniformity correction (NUC). 

Three tests for each thermal imager were conducted at each of five target temperatures: 1 °C, 

30 °C, 100 °C, 160 °C, and 260 °C, which span the temperature range of interest to the fire 

service [4]. Well-characterized extended-area blackbodies were used as targets (a CI Systems 

SR800 was used for target temperatures below 260 °C and a CI Systems SR80-HT was used for 

the 260 °C target temperature). The thermal imager under test was positioned such that the 

image of the blackbody surface completely filled the field of view and the thermal imager was 

normal to the plane of the blackbody surface. 

A Nikon D3 digital visible camera was used to take ten 16-bit digital photos of the images 

displayed by the thermal imager under test for each target temperature. These images were 

Table 2. Horizontal and vertical FOV results.

Imager Vertical FOV (degrees) Horizontal FOV (degrees) Aspect Ratio 

1 33 ± 0.6 42.5 ± 0.6 0.78 to 1 

2 28 ± 0.6 33.5 ± 0.6 0.84 to 1 

3 33 ± 0.6 42.5 ± 0.6 0.78 to 1 

4 32 ± 0.6 43.0 ± 0.6 0.74 to 1 

5 19 ± 0.6 24.0 ± 0.6 0.79 to 1 

The uncertainty expressed in these test measurements is a combination of Type A (statistical) 

and Type B (other), with a coverage factor of 2 resulting in a 95 % confidence interval. See 

Section 5 for detailed uncertainty analysis. 
4 

initially stored in flash memory on the Nikon, and then transferred to a PC for further processing. 

The processing steps were as follows: 

1. Convert the images from the proprietary Nikon NEF format to 16-bit TIFF files. 

2. Convert the TIFF files to grayscale as defined per IEC 61966-2-1 (1999) [5]. 

3. Use a MATLAB program to define a region of interest (ROI) that encompasses at least 

90 % of the FOV of the thermal imager under test. Exclude or remove symbols, icons, 

and text from this ROI. 

4. Filter out the high-frequency noise created by oversampling the thermal imager’s display. 

This was accomplished with an averaging filter which averaged over an area whose width 

and height was determined by the frequency of the noise. 

5. Calculate the NU for each image using the following equation [3] 

μ

σ NU = (1) 

Where σ is the standard deviation and μ is the mean, respectively, of pixel intensity 

values in the ROI. Retain the original pixel intensity values for later use. 

6. Rank the ten NU values collected at each target temperature, then discard the highest NU

value from the data set. This step is performed to mitigate the affect of an ill-timed NUC 

that may occur during the collection of images. 

7. Average the NU values for the three individual tests conducted at each target temperature. 

This is the NU value reported for each target temperature in Table 3. 

The accuracy of the temperature measurements described in this section is discussed in Section 5, 

uncertainty analysis. 

Table 3. NU values for a range of set point temperatures.

Imager NU 1 °C NU 30 °C NU 100 °C NU 160 °C NU 260 °C 

1 0.324 ± 0.019 0.497 ± 0.049 0.619 ± 0.021 0.471 ± 0.010 0.161 ± 0.100 

2 0.457 ± 0.023 0.360 ± 0.046 0.281 ± 0.013 0.296 ± 0.005 0.235 ± 0.022 

3 0.395 ± 0.145 0.393 ± 0.043 0.544 ± 0.007 0.702 ± 0.038 0.665 ± 0.013 

4 0.524 ± 0.042 0.355 ± 0.096 0.617 ± 0.024 0.172 ± 0.016 0.761 ± 0.038 

5 0.239 ± 0.007 0.339 ± 0.004 0.397 ± 0.032 0.404 ± 0.214 0.349 ± 0.006 

The uncertainty expressed in these test measurements is a combination of Type A (statistical) 

and Type B (other), with a coverage factor of 2 resulting in a 95 % confidence interval. See 

Section 5 for detailed uncertainty analysis. 
5 

A representative image from each of the thermal imagers, taken at the target temperature of 

100 °C is shown in Figure 1. Note that these images have been modified to remove identifying 

symbols and icons that would normally reside within the displayed imaged. 

2.3 Spatial Resolution 

The spatial resolution (SR) performance metric is a measure of the ability of firefighter thermal 

imagers to reproduce the details of a scene or target [6]. The spatial resolution test is used both 

to measure an imager’s spatial resolution and to determine whether a design robustness test, e.g., 

the corrosion resistance test, has impacted the imager’s ability to produce images of acceptable 

quality. 

This test was performed three times for each imager and the results were averaged to determine 

the SR. Similar to the nonuniformity test, the SR was measured by viewing an image on the 

thermal imager’s display with a high resolution digital visible camera. The thermal imager 

viewed a thermal target comprised of two sets of converging lines, as shown in Figure 2. This 

target is a portion of the target used by the ISO Spatial Resolution Standard ISO 12233 [7]. The 

target foreground and background were coated with well-characterized black paint having an 

emissivity of 0.94 ± 0.05. The characterization of the black paint was performed by the paint 

manufacturer. 

A B 

 Imager 1 Imager 3 Imager 4 Imager 5 

Figure 1. Representative images used for laboratory measurements of nonuniformity. The target 

temperature is 100 oC. 

Figure 2. Spatial resolution thermal target. The foreground (black markings) is held at a 

constant temperature of 3 °C above ambient. The target size is 61 cm measured along the 

centerline of each of the two converging line sets. 
6 

The thermal imager under test was placed 1 m from the target, normal to the plane of the target, 

and oriented to focus on the center of the target. A Nikon D3 digital visible camera was used to 

take ten 16-bit digital photos of the images displayed by the thermal imager under test, as shown 

in the left image in Figure 3. These images were initially stored in flash memory on the Nikon, 

then transferred to a personal computer (PC) for further processing. The processing steps were 

as follows: 

1. Convert the images from the proprietary Nikon NEF format to 16-bit TIFF files. 

2. Convert the TIFF files to grayscale as defined per IEC 61966-2-1 (1999) [5]. 

3. Filter out the high-frequency noise created by oversampling the thermal imager’s display. 

This was accomplished with an averaging filter which averaged over an area whose width 

and height was determined by the frequency of the noise. 

4. Use a MATLAB program to rotate the image 45 degrees and select an ROI that 

encompasses the converging lines, as shown in the right image in Figure 3, and calculate 

the contrast transfer function (CTF) at least at each of the index numbers along the two 

sets of converging lines; a process that calculates the CTF at each row in the ROI is also 

acceptable. The CTF is calculated using the following equation: 

max min

max min

I I

I I CTF

+

− = (2) 

where Imax and Imin are the highest and lowest pixel intensity values, respectively, along a 

row of pixels cut through the pattern at least at each index line as indicated by the dotted 

lines in the right image in Figure 3. Retain the original pixel intensity values for later use. 

Pixels that represent symbols, icons, or text are excluded from the analysis. 

5. The CTF values are multiplied by π/4 and paired with the frequencies of each index 

number to construct a Modulation Transfer Function (MTF) curve. The frequencies are 

listed in Table 4 below. 

Table 4. Frequencies corresponding to the indices on the spatial resolution target when the 

thermal imager under test is placed 1 m from the target. 

Index Frequency (cycles/mrad) 

1 (largest end of lines) 0.019 

2 0.038 

3 0.083 

4 0.118 

5 0.143 

6 0.167 

7 0.200 

8 0.250 

9 0.286 

10 (smallest end of lines) 0.300 
7 

6. The MTF curve is normalized to the value obtained at the low-frequency edge of the 

target (index 1). 

7. The average NU value measured at a target temperature of 30 o

C, and listed in Table 3, is 

subtracted from the MTF curve. This adjustment corrects for the effects of high 

frequency noise. 

8. The area under the normalized and adjusted MTF curve is the spatial resolution. 

The accuracy of the temperature measurements described in this section is discussed in Section 5, 

uncertainty analysis. 

 Figure 3. Spatial resolution image on left with a dashed box indicating the inset image on right. 

The ROI is between the solid white lines in the right image and the dotted lines show the position 

and orientation of the rows of pixels used to calculate the CTF.
8 

2.4 Effective Temperature Range 

The Effective Temperature Range (ETR) test measures the ability of a firefighter thermal imager 

to see relatively small temperature differences in cases when large temperature differences exist 

in the field of view [1]. In this test, the thermal imager is positioned such that it views a set of 

contrast bars having constant temperature while the temperature of a surface of equal size in the 

field of view is increased from near ambient to 550 °C. In general, as the hot surface 

temperature increases, the contrast of the bars decreases. This test, as described in Draft NFPA 

1801, also has a color component in which the colorization corresponding to certain surface 

temperature ranges is verified. The colorization employed by the imagers tested for this work 

was not designed to conform with Draft NFPA 1801, therefore the color component of this test is 

not reported here. 

The thermal imagers were placed 1 m from the bar target, which were comprised of four vertical 

1.27 cm diameter copper tubes placed 1.27 cm apart, resulting in a bar frequency of 0.04 

cycles/mrad. A heated siloxane solution flowed through the bars to maintain a temperature of 

37 °C ± 1 °C. The background temperature was 28 °C ± 1 °C. The bars and background were 

coated with black paint having an emissivity of 0.94 ± 0.05. A CI Systems SR80-HT extended 

area blackbody having a 178 mm square surface was positioned such that its radiation impinged 

on a 203 mm diameter off-axis parabolic mirror having a focal length of 1 m and an offset of 10 

degrees and was directed to the thermal imager under test. The layout of the testing equipment is 

shown in Figure 4. It is important that the emitting surface of the blackbody appear in the center 

of the image displayed by the thermal imager, while the heated bars appear at one side. 

The size of the blackbody radiation in the thermal imager’s field of view was at least as large as 

the bars, but varied depending on the field of view of the thermal imager under test. A Nikon D3 

digital visible camera was used to take 16-bit digital photos of the image displayed by the 

thermal imager every 3 seconds. These images were initially stored in flash memory on the 

Nikon, then transferred to a PC for further processing. The processing steps were as follows: 

1. Convert the images from the proprietary Nikon NEF format to 16-bit TIFF files. 

Table 5. Spatial resolution test results. 

Imager SR

1 0.0442 ± 0.0039 

2 0.0389 ± 0.0043 

3 0.0305 ± 0.0008 

4 0.0402 ± 0.0089 

5 0.0445 ± 0.0013 

The uncertainty expressed in these test measurements is a combination of Type A (statistical) 

and Type B (other), with a coverage factor of 2 resulting in a 95 % confidence interval. See 

Section 5 for detailed uncertainty analysis. 
9 

2. Convert the TIFF files to grayscale as defined per International Electrotechnical 

Commission (IEC) 61966-2-1 (1999) [5]. 

3. Filter out the high-frequency noise created by oversampling the thermal imager’s display. 

This was accomplished with an averaging filter which averaged over an area whose width 

and height was determined by the frequency of the noise. 

4. Use a MATLAB program to define an ROI that encompasses the four vertical bars. 

Exclude or remove symbols, icons, and text from this ROI. Retain the original pixel 

intensity values for later use. 

5. Use the MATLAB program to calculate the CTF of the bars within the ROI. The CTF is 

calculated using equation 2. 

6. The ETR is the temperature at which the CTF of the bars falls below a predetermined 

value. The cut-off value is not specified for this test in lieu of the image quality indicator 

discussed in Section 2.2. 

The ETR data is plotted and discussed in Section 3.4 in the context of the image quality indicator. 

Figure 4. ETR testing configuration. The target consists of the heated bars, which appear on 

the side of the image, and the emitting surface of the blackbody, which appears in the center 

of the image. 
10 

2.5 Thermal Sensitivity 

The thermal sensitivity (TS) test measures the ability of fire fighting thermal imagers to discern 

small temperature differences [1]. Three tests for each thermal imager were conducted at each of 

five target temperatures: 1 °C, 30 °C, 100 °C, 160 °C, and 260 °C, which span the temperature 

range of interest to the fire service [4]. In most cases, a pair of well-characterized extended area 

blackbodies were used as targets. A CI Systems SR800 was used for target temperatures 

between 1 °C and 160 °C, which is the temperature range of this blackbody, a CI Systems SR80-

HT was used for target temperatures above 30 °C since this blackbody cannot be cooled below 

30 °C, and an IRCon BCN-07C cavity blackbody combined with a 20.3 cm diameter spherical 

mirror having a focal length of 30.5 cm was used for the 260 °C target temperature because the 

SR800 could not produce this temperature. A metal 3.8 liter (1 gallon) container coated with 

black paint having an emissivity of 0.94 ± 0.05 filled with ice water was used for the 1 °C target 

temperature because only the SR800 could be cooled below ambient temperatures. The thermal 

imager under test was positioned such that the image of two blackbody surfaces equally filled as 

much of the field of view as possible. The amount of the field of view filled by the blackbody 

surfaces depended on the optical geometry of the thermal imager under test. The thermal imager 

was positioned normal to the plane of the blackbody surfaces. The purpose of having two 

blackbody surfaces in the field of view was to force the automatic gain control (AGC) function 

of the imagers to stabilize during the test. A significant portion of a typical image used to 

calculate TS consists of surfaces at ambient temperature, which also contributed to the 

stabilization of the AGC. 

For each nominal target temperature (Tn) below 260 °C, one blackbody was held at a constant 

temperature. The other blackbody was initially set at Tn – 0.05 °C and ten 16-bit digital photos 

were collected from the thermal imager display with a Nikon D3 digital visible camera. The 

blackbody was then set at Tn and ten more images were collected. The blackbody was then set at 

Tn + 0.05 °C and ten more images were collected. For the 260 °C nominal target temperature, the 

same process was performed except that the temperature differences were Tn ± 0.20 °C. This 

entire process was conducted three times for each imager at each nominal target temperature. 

The images were initially stored in flash memory on the Nikon, then transferred to a PC for 

further processing. The processing steps were as follows: 

1. Convert the images from the proprietary Nikon NEF format to 16-bit TIFF files. 

2. Convert the TIFF files to grayscale as defined per IEC 61966-2-1 (1999) [5]. 

3. Use a MATLAB program to define an ROI that encompasses at least 90 % of the area in 

the field of view representing the blackbody surface that is set at Tn ± 0.05 °C or Tn ± 

0.20 °C. Exclude or remove symbols, icons, and text from this ROI. The three images in 

Figure 5 are examples of ROIs used for the TS test. 

4. Filter out the high-frequency noise created by oversampling the thermal imager’s display. 

This was accomplished with an averaging filter which averaged over an area whose width 

and height was determined by the frequency of the noise. 

5. Calculate the mean pixel intensity (μ) of the ROI. Retain the original pixel intensity 

values for later use. 
11 

6. Rank the ten μ values collected at each target temperature, then discard the lowest μ

value from the data set. This step is performed to mitigate the affect of an ill-timed NUC 

that may occur during the collection of images. 

7. Average the remaining nine μ values for each set of images collected at each nominal 

target temperature, i.e., at Tn and Tn ± 0.05 °C or Tn ± 0.20 °C. 

8. Calculate the contrast between the average pixel intensity at Tn and Tn – 0.05 °C or 

0.20 °C. Record this value. Calculate the contrast between the average pixel intensity at 

Tn and Tn + 0.05 °C or 0.20 °C. Record this value. Average the two contrast values. 

9. Average the contrast values per step 8 above for the three individual tests conducted at 

each target temperature. This is the thermal sensitivity value reported for each target 

temperature in Table 6. 

An example of the contrast obtained from a TS test is presented in Figure 5. Note that, while 

there may be a discernable difference in the average pixel intensity of the three images, there 

may not be enough of a difference to provide useful images for the user of the thermal imager. 

The very low contrasts recorded for the TS tests may not be sufficient for firefighters to locate 

fire hazards or perform other tasks, so additional tests examined the change in contrast as the 

value used for Tn was increased. Three tests were conducted at each Tn value for one imager. 

The results are shown in Figure 6 and will be discussed in Section 3.4 in the context of the image 

quality indicator. 

Table 6. Thermal sensitivity test results.

Imager TS 1 °C TS 30 °C TS 100 °C TS 160 °C TS 260 °C 

1 0.0134 ± 

0.0123 

0.0029 ± 

0.0012 

0.0028 ± 

0.0018 

0.0042 ± 

0.0006 

0.0096 ± 

0.0010 

2 0.0316 ± 

0.0213 

0.0070 ± 

0.0029 

0.0030 ± 

0.0015 

0.0021 ± 

0.0011 

0.0011 ± 

0.0003 

3 0.0096 ± 

0.0047 

0.0067 ± 

0.0023 

0.0020 ± 

0.0009 

0.0020 ± 

0.0020 

0.0092 ± 

0.0107 

4 0.0282 ± 

0.0110 

0.0042 ± 

0.0007 

0.0016 ± 

0.0002 

0.0041 ± 

0.0015 

0.0026 ± 

0.0009 

5 0.0237 ± 

0.0183 

0.0104 ± 

0.0039 

0.0039 ± 

0.0027 

0.0025 ± 

0.0007 

0.0063 ± 

0.0012 

The uncertainty expressed in these test measurements is a combination of Type A (statistical) 

and Type B (other), with a coverage factor of 2 resulting in a 95 % confidence interval. See 

Section 5 for detailed uncertainty analysis. 
12 

Figure 6. Contrast response to increasing values of Tn for the TS test. The nominal target 

temperature was 100 oC. The uncertainty expressed in these test measurements is a combination 

of Type A (statistical) and Type B (other), with a coverage factor of 2 resulting in a 95 % 

confidence interval. See Section 5 for detailed uncertainty analysis. 

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

0 500 1000 1500 2000 2500 3000 3500

Contrast

Tn (mK)

 

Figure 5. Example test images for TS at a nominal target temperature of 100 °C. The image on 

the left is of a 100.05 oC target, the image in the center is of a 100.00 oC target, and the image on 

the right is of a 99.95 oC target. Note that these three images may appear to be identical when 

viewed on a printed page.
13 

3. MULTIVARIATE ANALYSIS 

In order to determine the minimum level of image quality that allows users to successfully 

perform meaningful tasks, a set of perception tests were conducted using firefighters that use 

thermal imagers on the job. The test subjects were asked to observe thermal images of scenes in 

which a single potential fire hazard may or may not be present. The test subjects identified the 

hazard by clicking on it or, if no hazard was present, the test subject clicked a “No Hazard” 

button. The contrast (C), brightness (B), spatial resolution (R), and noise (N) in the thermal 

images were degraded in varying degrees such that the effect of each of these primary quality 

factors on the ability of a firefighter to perform a task was quantified. These primary factors 

were chosen based on their relevance to the tests presented in Sections 2.2 to 2.5, although there 

is not a 1-to-1 correspondence. The measurement of contrast is fundamental to all the imaging 

tests conducted for this report. Brightness is used as an indication of the relative temperature of 

surfaces in the field of view. Broadband noise, manifested as nonuniformity, and spatial 

resolution are directly related to the test methods described in Sections 2.2 and 2.3, respectively. 

During the process of analyzing the perception test results, it became apparent that the above 

four primary factors of image quality were too tightly dependent upon one another to adequately 

describe the imaging performance of a thermal imager if used in separate, individual 

performance tests such as those presented in Sections 2.2 to 2.5. For example, the interaction of 

contrast with brightness has a much more profound effect on a firefighter’s ability to successfully 

identify a fire hazard than the additive effect of contrast plus brightness. In order to 

accommodate the interactions between primary image quality factors without disturbing the test 

methods previously discussed, a human perception model was developed that includes the four 

primary factors, as measured by the test methods described in Sections 2.2 to 2.5, along with 

two-way interactions between these factors and squared terms of these factors. The model is 

presented in its most compact form in the following equation: 

X

X

e

e P

+ =1 (3) 

where P is the probability of successfully identifying the fire hazard, or absence of a fire hazard, 

in an image. For the purposes of this report, P is the image quality indicator. The variable X

represents a long set of terms in the form 

X = + C + B + R + N + C + B + R + N + CB + CR + CN + BR + BN + RN 2 2 2 2 0.2563 (4) 
14 

where C, B, R, and N are the contrast, brightness, spatial resolution, and noise that were present 

in the images observed by the firefighters during their perception tests. The coefficients assigned 

to each of the terms in equation 4 are listed in Table 7 below. 

Several methods were used to validate the model represented by this equation, which will be 

discussed in depth in a future publication. One of the methods used is to plot the averaged data 

collected in the perception tests for each of the 25 different image degradation settings against 

values predicted by the model. This is done in Figure 7, which shows a very good correlation 

between actual data and predicted values. 

Table 7. List of coefficients that apply to terms in equation 4. 

Coefficient Term 

0.2563 intercept 

0.8737 C 

0.4595 B 

15.85 R 

-2.567 N 

-8.359 C2

-4.631 B2

-242.2 R2

2.893 N2

3.779 CB 

48.71 CR 

11.60 CN 

34.91 BR 

-8.016 BN 

-5.008 RN 
15 

Finally, a consistency check was performed to observe the predicted values from the model as 

each primary image quality factor was varied across the range of possible values, holding the 

other factors constant at their average value. This is shown in Figure 8. Note that all the factors 

vary from 0 to 1 except R, which is plotted using the uppermost x-axis. Also, note that the model 

is useful for predictions within the range of factor values that were used in the perception tests; 

extrapolation beyond this range of values for each factor is less useful. 

Figure 7. A comparison of the observed and model predicted values for the probability of 

successfully completing the fire hazard identification task. The plot character represents the 

image setting (1 to 25). A perfect correlation falls at the 45° diagonal.

0.2 0.3 0.4 0.5 0.6 0.7 0.8

Observed Result

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Model Prediction

1

2

3

4

5

6

8 7

9

10

111213

14

15

16

17

18

19

20

21

22

23

24

25
16 

In order to optimize the perception testing procedure and reduce test subject fatigue, the range of 

values used for the primary image quality factors in the degraded images were chosen to explore 

a particular region of the probability of successfully performing the fire hazard identification task, 

this region was 0.6 < P < 0.9. The reasoning was that collecting data from images in which none 

of the test subjects were able to identify anything did not yield valuable information for the 

purposes of this work. Likewise, using images in which all test subjects were able to identify the 

fire hazard would not provide useful information from which to construct the model. Therefore, 

no images were degraded to “zero” values of any of the primary image quality factors. It should 

be also noted that the upper end of the N primary factor was not within the range of N values 

used to construct the model and values above about 0.6 are extrapolated. 

3.1 Nonuniformity and Spatial Resolution Procedure 

The measurement of nonuniformity at the target temperature of 30 °C is independent of the 

image quality equation and is performed strictly in conformance with the method described in 

Section 2.2, in which the standard deviation of the pixel intensity is divided by the mean pixel 

intensity of a ROI within an image. This is the only test that does not require the use of equation 

3 and is used to establish the primary factor R. The nonuniformity tests conducted at target 

temperatures of 1 °C, 100 °C, 160 °C, and 260 °C make use of equation 3 and therefore 

Figure 8. Plot of each image quality factor as it varies from 0 to 1 (0 to 0.05 for R) while the 

other factors are held constant at their average values. 

0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Spatial Resolution Value

Image Quality Indicator,

P

Primary Factor Value (excluding Spatial Resolution)

P (N) P (C) P (B) P (R)
17 

necessitate a pre-existing value for R. This will become apparent as the procedure is explained 

below. 

1. For the target temperature of 30 °C, follow the NU procedure outlined in Section 2.2. 

2. Follow the procedure outlined in Section 2.3 to calculate the spatial resolution, SR. To 

apply equation 3, use the contrast measured at the low-frequency edge of the spatial 

resolution target as C, use the average pixel intensity within the spatial resolution ROI as 

B, use the measured spatial resolution as R, and use the nonuniformity measured in step 1 

above as N. 

3. Calculate the image quality indicator, P. If P indicates less than the desired image 

quality level, then the imager fails the entire test sequence and no further testing is 

necessary. 

4. If P indicates image quality that is greater than or equal to the desired level at the target 

temperature of 30 °C, apply equation 3 to the nonuniformity data collected at target 

temperatures of 1 °C, 100 °C, 160 °C, and 260 °C, applying equation 3 separately to data 

collected at each target temperature. For these tests, use the same contrast value as in 

step 2 for C, use the average pixel intensity within the nonuniformity ROI as B, use the 

same measured spatial resolution as in step 2 for R, and use the nonuniformity measured 

according to the procedure outlined in Section 2.2 at each of the target temperatures (1 °C, 

100 °C, 160 °C, and 260 °C) as N. 

5. Calculate the image quality indicator, P. If P indicates less than the desired image 

quality level, then the imager fails the entire test sequence and no further testing is 

necessary. 

6. If P indicates image quality that is consistently greater than or equal to the desired level, 

continue to the test sequence for effective temperature range in the following section. 

3.2 Effective Temperature Range Procedure 

The procedure for applying equation 3 to the data collected for the effective temperature range 

test is relatively simple. For this test, the image quality indicator, P, is calculated using pixel 

intensities from the ROI encompassing the bars for each image collected during the test. The 

procedure is explained below. 

1. Follow the ETR procedure outlined in Section 2.4. 

2. To apply equation 3, use the CTF of the bars as the contrast C, use the average pixel 

intensity of the bar ROI as the brightness B, use the same measured spatial resolution as 

in step 2 of Section 3.1 for R, and use the nonuniformity value obtained at a target 

temperature of 30 °C in step 1 of Section 3.1 as N. 

3. Calculate the image quality indicator, P, for each image collected during the test. If any 

of the calculated values of P indicate less than the desired image quality level, the imager 

under test fails the entire test sequence and no further testing is necessary. 

4. If P indicates image quality that is consistently greater than or equal to the desired level, 

continue to the test sequence for thermal sensitivity in the following section. 
18 

3.3 Thermal Sensitivity Procedure 

The procedure for applying equation 3 to the data collected for the thermal sensitivity test is also 

relatively simple. For this test, the image quality indicator, P, is calculated using a contrast 

measurement that depends on subtle changes in pixel intensity resulting from small changes in 

the target surface temperature. The procedure is explained below. 

1. Follow the TS procedure in Section 2.5. 

2. To apply equation 3, for each nominal target temperature, use the contrast obtained in 

step 9 of Section 2.5 as C, use the average pixel intensity of the bar ROI as the brightness 

B, use the same measured spatial resolution as in step 2 of Section 3.1 for R, and use the 

nonuniformity value obtained at the corresponding target temperature as N. 

3. Calculate the image quality indicator, P, at each nominal target temperature. If any of the 

calculated values of P indicate less than the desired image quality level, the imager under 

test fails the entire test sequence and no further testing is necessary. 

4. If P indicates image quality that is consistently greater than or equal to the desired level, 

the imager under test has successfully completed the sequence of image quality tests. 

3.4 Multivariate Image Quality Results 

The results shown in this section consider the same test conditions and data that were collected 

using the test methods presented in Sections 2.2 to 2.5 but offer the added benefit of 

incorporating the effects of interactions between the primary image quality factors. Using 

equation 3 in this way allows the pass/fail criteria for the image quality test methods to be based 

on human perception of images that relate to tasks performed in the user’s line of duty. 

3.4.1 Nonuniformity 

The nonuniformity (NU) results, expressed in terms of the image quality indicator, P, are shown 

below in Figure 9. The mid-range target temperatures of 100 o

C and 160 oC, at which some of 

the imagers show a larger uncertainty, are in the vicinity of the mode-shift trigger for those 

imagers. An automatic mode shift is a method used in thermal imagers that employ a 

microbolometer type detector to extend the dynamic range of the imager by decreasing the 

integration time that the thermal scene is exposed to the detector array. A side effect of 

operating at the shorter integration time is reduced thermal sensitivity; this will be explained in 

more detail in the thermal sensitivity discussion. The effect on the nonuniformity results 

presented here is that the imager may automatically shift for one of the three tests conducted at 

each target temperature, and not shift for the other two tests if the mode shift algorithm isn’t 

triggered. The mode shift algorithm is a proprietary process and can depend on many conditions 

in the thermal scene, including but not limited to target temperature. This phenomenon can make 

testing the imagers problematic if the target temperature and other conditions are very close to 

the mode shift conditions. 
19 

3.4.2 Spatial Resolution 

The spatial resolution (SR) results, expressed in terms of the image quality indicator, P, are 

shown below in Figure 10. With the exception of imager 1, the imagers produced similar SR

results in spite of having different values of C, B, R, and N in equation 3. 

Figure 9. Nonuniformity (NU) plotted in terms of the image quality indicator, P. The 

uncertainty expressed in these test measurements is a combination of Type A (statistical) and 

Type B (other), with a coverage factor of 2 resulting in a 95 % confidence interval. See Section 5 

for detailed uncertainty analysis. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 50 100 150 200 250 300

Image Quality Indicator,

P

Target Temperature (oC)

Imager 1 Imager 2 Imager 3 Imager 4 Imager 5
20 

3.4.3 Effective Temperature Range 

The effective temperature range (ETR) results for Imagers 3, 4, and 5, expressed in terms of the 

image quality indicator, P, are shown below in Figures 11, 12, and 13, respectively. Three tests 

were conducted for each imager. Technical difficulties with the blackbody used as the hot 

surface prevented the collection of data for all imagers. Mode shifts can be observed as jumps in 

the data near the same hot surface temperature for all three tests. Imagers 3 and 4 produced 

higher P values after the mode shifts. Imager 5 did not display a pronounced mode shift. 

Figure 10. Spatial resolution (SR) plotted in terms of the image quality indicator, P. The 

uncertainty expressed in these test measurements is a combination of Type A (statistical) and 

Type B (other), with a coverage factor of 2 resulting in a 95 % confidence interval. See Section 5 

for detailed uncertainty analysis. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0123456

Image Quality Indicator,

P

Imager ID Number
Figure 1

quality in

1. Effectiv

dicator, P. 

e Temperat

Each colore

ure Range 

d line repres

21 

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sents the resu

mager 3 pl

lts of one te

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st. 

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Figure 1

quality in

2. Effectiv

dicator, P. 

e Temperat

Each colore

ure Range 

d line repres

22 

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sents the resu

mager 4 pl

lts of one te

otted in ter

st. 

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3.4.4 Th

The therm

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temperatu

temperatu

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Figure 1

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hermal Sens

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below in Fig

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ures above 

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13. Effectiv

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sitivity

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C. There 

1 o

C. The

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he imager sh

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ve Temperat

Each colore

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d line repres

23 

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Theoreticall

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Imager 5 pl

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24 

There are several factors to consider when attempting to measure very fine differences in 

temperature. First, the target temperature must be well characterized and measured with 

sufficient accuracy. Second, the thermal sensitivity of the temperature sensing device under test 

must be capable of resolving the temperature difference. Third, the displayed image must be 

resolved with enough shades of gray to provide a contrast that can be perceived by the user. 

The blackbody used to produce Tn in this test (CI Systems SR-800) has a temperature accuracy 

of 8 mK for temperatures below 50 o

C and 15 mK for temperatures above 50 oC, however, its 

temperature range is restricted to 0 oC to 175 oC. For the 260 oC test point, the CI Systems SR80-7HT has a temperature accuracy of 0.5 o

C, although there are other commercially available 

blackbodies that are more accurate at this temperature. If this test method was modified to 

require thermocouple temperature measurements, the accuracy of these measurements falls to 1.1 o

C or 0.4 %, whichever is greater. A differential blackbody could be utilized, however, these 

instruments are restricted to temperature differences near ambient temperature. 

The thermal sensitivity of the imager’s detector comes into play when the dominant temperature 

difference in the imager’s field of view is relatively small. For the nominal target temperature of 

Figure 14. Thermal Sensitivity (TS) plotted in terms of the image quality indicator, P. The 

uncertainty expressed in these test measurements is a combination of Type A (statistical) and 

Type B (other), with a coverage factor of 2 resulting in a 95 % confidence interval. See Section 5 

for detailed uncertainty analysis. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 50 100 150 200 250 300

Image Quality Indicator,

P

Nominal Target Temperature (oC)

Imager 1 Imager 2 Imager 3 Imager 4 Imager 5
25 

30 o

C, the limiting component would be the detector if the user could discern individual levels of 

grayscale. 

The displays used for fire service thermal imagers have 8-bit grayscale resolution; therefore, 

scenes in which temperature differences exist on the order of 250 oC could not be expected to be 

resolved to less than in 1 oC increments. Following this logic, the smallest value of Tn that can 

be resolved on the imager’s display (one increment of grayscale) for each of the nominal target 

temperatures, assuming an ambient temperature of 25 o

C and a 50 mK detector thermal 

sensitivity, is listed in Table 8. 

Given the minimum Tn values needed to produce one increment of grayscale on an 8-bit display 

at the nominal target temperatures required in this test sequence, it is useful to examine the 

response of the imager to increasing values of Tn. This is done for one imager in Figure 15. The 

data presented in Figure 15 shows that, when plotted in terms of P, holding all the other image 

quality factors constant and increasing the Tn from 50 mK to 3000 mK, P remains quite low 

while the uncertainty in the measurement increases substantially. 

Table 8. Minimum resolvable Tn due to imager component limitations. 

Nominal Target Temperature (o

C) Minimum Tn Limitation 

1 94 mK display 

30 50 mK detector 

100 293 mK display 

160 527 mK display 

260 918 mK display 
26 

In order to improve the application of this test to fire service thermal imagers, several options 

may be considered. Possible changes to the test method are listed below, note that this is not an 

exhaustive list. 

1. Leave the test method as is. 

2. Make this measurement only at one (ambient) nominal target temperature, using a 

differential blackbody to measure the temperature difference. 

3. Use the imager’s detector thermal sensitivity specification, which is measured as the 

NETD, as the imager’s thermal sensitivity. 

4. Adjust the Tn values as necessary to account for resolution limitations imposed by system 

components. 

5. Lower the acceptable image quality indicator value, P, to a value that does not eliminate 

all the thermal imagers in the commercial fire service market. 

6. As a last resort, remove this test method from the suite of image quality tests until a more 

suitable test method is developed. 

Figure 15. Thermal sensitivity as a function of increasing delta T (Tn). The uncertainty 

expressed in these test measurements is a combination of Type A (statistical) and Type B (other), 

with a coverage factor of 2 resulting in a 95 % confidence interval. See Section 5 for detailed 

uncertainty analysis. 

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0 500 1000 1500 2000 2500 3000 3500

Image Quality Indicator,

P

Delta Temperature (mK)
27 

4. UNCERTAINTY ANALYSIS 

There are different components of uncertainty in the measurements made with the equipment 

used in the tests discussed in this report. Type A uncertainties are those which are evaluated 

using statistical methods and Type B uncertainties are those which are estimated using other 

means, such as experience with a particular type of equipment. Type B uncertainties are 

evaluated by estimating the upper and lower limits for the quantity in question such that the 

probability that the quantity would fall within the upper and lower limits is essentially 100 %. 

After estimating the uncertainties by Type A and Type B analysis, the results are combined in 

quadrature to yield the combined standard uncertainty. When the combined standard uncertainty 

is multiplied by a coverage factor of two, the result is the expanded uncertainty which 

correspond to a 95 % confidence interval (2σ). 

The components of uncertainty are listed in Table 9. Some of these components, such as the 

blackbody temperature measurements, are derived from instrument specifications. Other 

components, such as the angular FOV measurements, are estimated based on estimates of 

uncertainty in reading the angular markings on the protractor or rotating platform. 

The error bars and measurement accuracies shown in the figures and tables, respectively, in this 

report reflect values calculated using the uncertainties listed in Table 8 above. 

5. CONCLUSIONS 

Five thermal imagers were provided by fire service thermal imager manufacturers for evaluation 

using test methods designed specifically to assess their image quality performance for fire 

Table 9. Components used in uncertainty analysis. 

Component Type A 

Uncertainty Type B Uncertainty Combined Uncertainty 

Total 

Expanded 

Uncertainty 

FOV angle σ 0.25 degrees σ + 0.25 degrees 0.6 degrees 

SR-800 

Blackbody 

σ, pixel 

intensities in 

ROI

T < 50 o

C: ± 8 mK 

T > 50 oC: ± 15 mK

σ + 8 mK 

σ + 15 mK 

2(σ + 8 mK) 

2(σ + 15 mK) 

SR-80-7HT 

Blackbody 

σ, pixel 

intensities in 

ROI 

± 0.5 oC σ + 0.5 o

C 2(σ + 0.5 oC) 

IRCon 

Blackbody 

σ, pixel 

intensities in 

ROI 

T < 316 oC: 2 oC 

T > 316 o

C: 0.5 % 

σ + 2 o

C 

σ + 0.5 % 

2(σ + 2 oC) 

2(σ + 0.5 %) 

Type J 

thermocouples σ

Greater of 1.1 oC or 

0.4 % 

σ + 1.1 oC or 

σ + 0.4 % 

2(σ + 1.1 oC or 

σ + 0.4 %) 

Type K 

Thermocouples σ

Greater of 1.1 oC or 

0.4 % 

σ + 1.1 oC or 

σ + 0.4 % 

2(σ + 1.1 oC or 

σ + 0.4 %) 
28 

service applications. These tests were Field of View (FOV), Nonuniformity (NU), Spatial 

Resolution (SR), Effective Temperature Range (ETR), and Thermal Sensitivity (TS). 

The FOV test results show that the design of thermal imager optical systems varies significantly, 

even in cases where the same sensing material and detector array size are used. The combined 

horizontal and vertical FOVs differed by approximately 75 % from the narrowest to the widest 

FOV values. 

The NU test results, when considered in the context of human perception using the image quality 

indicator (P), were generally reproducible and showed that all the imagers performed in the 

range of about 0.6 to 0.95 across the set of five target temperatures. The uncertainty in these 

measurements was larger for the 100 o

C and 160 oC target temperatures, which is consistent with 

observations that some of the imagers shifted sensitivity mode for one or two of the three tests 

performed at these intermediate target temperatures. 

The ETR tests show that, after an initial increase from ambient temperature, the three imagers 

tested gave three distinctly different responses. Imager 3 has a higher value after the mode shift, 

imager 4’s performance didn’t change noticeably due to mode shifts and was consistently higher 

than the other two imagers, and the performance of imager 5 slowly decreased and then reached 

a plateau with no indication of a mode shift. 

The results of the TS tests are problematic. As the nominal target temperature increases above 1 o

C, the performance of the imagers drops drastically and then becomes erratic at target 

temperature above 100 oC. Part of the erratic nature of the data collected at higher temperatures 

is due to the mode shift, for the same reason noted in the NU discussion above. This test does 

not appear to fully capture the actual thermal sensitivity of the imager. A closer examination of 

the response of two imagers to increasing the temperature difference (Tn) used in this test showed 

that the imagers don’t discriminate significantly between a Tn of 50 mK and a Tn of 2000 mk. 
29 

References 

[1] Amon, F., et al., Performance Metrics for Fire Fighting Thermal Imaging Cameras – 

Small- and Full-Scale Experiments. 2008 

[2] Holst, G.C., Testing and Evaluation of Infrared Imaging Systems. 1998, Winter Park, FL: 

JCD Publishing. 

[3] Lock, A. and F. Amon. Measurement of the Nonuniformity of First Responder Thermal 

Imaging Cameras. in SPIE Defense + Security. 2008. Orlando, FL: SPIE. 

[4] Donnelly, M.K., et al., Thermal Environment for Electronic Equipment used by First 

Responders. 2006(NIST Technical Note 1474). 

[5] IEC, Multimedia systems and equipment - Colour measurement and management 

International Electrotechnical Commission, 1999. IEC 61966-2-1. 

[6] Lock, A. and F. Amon. Application of Spatial Frequency Response as a Criterion for 

Evaluating Thermal Imaging Camera Performance. in SPIE Defense and Security 

Conference. 2008. Orlando, Florida: SPIE. 

[7] ISO, Photography - Electronic still-picture cameras - Resolution measurements.

International Standards Organization 2000. ISO 12233. 

 
30 

6. APPENDIX - EQUIPMENT LIST 

Certain commercial entities, equipment, or materials may be identified in this document in order 

to describe an experimental procedure or concept adequately. Such identification is not intended 

to imply recommendation or endorsement by the National Institute of Standards and Technology, 

nor is it intended to imply that the entities, materials, or equipment are necessarily the best 

available for the purpose. When possible, readily available equipment that had already been 

purchased for different tasks was utilized rather than purchasing new equipment that might be 

better suited for the specific tasks in this project. 

1. Nikon D3 digital SLR camera. 

http://www.nikonusa.com/Find-Your-Nikon/ProductDetail.page?pid=25434

Remote control cord. 

http://www.nikonusa.com/Find-Your-Nikon/Product/Remote-Cords/4652/MC-22-

Remote-Cord-with-Banana-Plugs.html

Nikkor 28 mm lens. 

http://www.nikonusa.com/Find-Your-Nikon/Product/Camera-Lenses/1922/AF-NIKKOR28mm-f%252F2.8D.html

+10 close-up filter. 

http://www.bhphotovideo.com/c/product/94232-

REG/Hoya_S52MCU_52mm_Macro_Close_up_10.html

Image conversion software (apparently the newest Photoshop CS4 includes the plug-in 

necessary for use with Nikon D3 files). 

http://bibblelabs.com/ or http://www.adobe.com/products/photoshop/cameraraw.html

Memory. 

http://www.newegg.com/Product/Product.aspx?Item=N82E16820211244

Camera stand. 

http://www.bhphotovideo.com/bnh/controller/home?ci=0&shs=Manfrotto+by+Bogen+I

maging+SALON+230+CAMERA+STAND+(7%27)+-

+BO809&sb=ps&pn=1&sq=desc&InitialSearch=yes&O=jsp%2Fcatalog.jsp&A=search

&Q=*&bhs=t&Go.x=0&Go.y=0&Go=submit

Articulated camera positioning arm. 

http://www.bhphotovideo.com/bnh/controller/home?O=search&A=search&Q=&shs=ma

nfrotto+by+bogen+imaging+articulated+arm+bracket+bo396b3&ci=0

Grip kit for the articulating arm. 

http://www.bhphotovideo.com/bnh/controller/home?ci=0&shs=Avenger+GRIP+KIT%3

A+C1575B%2FD200B%2FE600%2FD520B%2FBAG+-

+AVD800KIT&sb=ps&pn=1&sq=desc&InitialSearch=yes&O=jsp%2Fproductlist.jsp&A

=search&Q=*&bhs=t&Go.x=24&Go.y=11&Go=submit

2. Data Acquisition System. 

Controller card. 

http://sine.ni.com/nips/cds/view/p/lang/en/nid/14235
31 

Chassis. 

http://sine.ni.com/nips/cds/view/p/lang/en/nid/10676

Analog In module, terminal block, and cable (use TC-2095 terminal). 

http://sine.ni.com/nips/cds/view/p/lang/en/nid/1654

http://sine.ni.com/nips/cds/view/p/lang/en/nid/1676

http://sine.ni.com/nips/cds/view/p/lang/en/nid/1834

Analog Out module and terminal block. This is to control relays that control the Nikon 

D3 camera. 

http://sine.ni.com/nips/cds/view/p/lang/en/nid/1870

http://sine.ni.com/nips/cds/view/p/lang/en/nid/1682

3. Emissivity paint. 

Optical black coating, P/N 20825, Medtherm Corp., P. O. Box 412, Huntsville, AL. 

Average emissivity = 0.95, ± 0.03. 

4. Thermocouples. 

Use Type-J thermocouples. 

http://www.omega.com/pptst/SA1.html

5. Spatial resolution target. 

See files: ImRecTarget.svg, ImRecTarget.eps, ImRecTarget.ai. Note- these are all the 

same image but with different file formats. 

The 24” x 36” silicone rubber heating blanket for the background comes from: 

http://www.briskheat.com/p-347-srl-srp-silicone-rubber-heating-blankets.aspx (use a 

variac to control the temperature) 

Metal target stencil. 

Frederick Sign and Banner, 18 East 6th St., Frederick, MD 21701, 301-663-9122. 

6. Blackbodies. 

One extended area blackbody that can go as low as 0 oC, such as one of these: 

http://www.ci-systems.com/htmls/article.aspx?C2004=12724&BSP=12547 (4”, extended 

temperature range). 

http://www.sbir.com/blackbody_13132.htm (4”, -30 to 100 o

C temperature range) 

http://www.mikroninfrared.com/Catalog.aspx?id=1164&ekmensel=c580fa7b_8_28_1150

_2

(4”, -5 to 170 o

C temperature range) 

http://www.electro-optical.com/html/datashts/extended/pdf/eo710ces200.pdf

(4”, note: temperature accuracy might be out of specification for NFPA 1801) 

In all the above cases, the nonuniformity of the blackbody as measured in paragraph 

8.1.5.13 needs to be verified. 

A high temperature extended area blackbody. 

http://www.ci-systems.com/Htmls/article.aspx?C2004=12726&BSP=12547

(Temperature range 30 o

C to 550 oC) 

http://www.sbir.com/blackbody_4000.htm
32 

(Temperature range 30 o

C to 550 oC) 

http://www.mikroninfrared.com/Catalog.aspx?id=2392&ekmensel=c580fa7b_8_28_1150

_2

http://www.electro-optical.com/html/datashts/extended/pdf/CES600800RevisedC.pdf

(Temperature range 30 o

C to 550 oC) 

7. Effective Temperature Range Bars. 

4 bars: 1.3 cm (1⁄2 in) thick and set 1.3 cm (1⁄2 in) apart; they are 15.2 cm (6 in) long. 

They must maintain a constant temperature of 30 oC ± 0.5 oC. Suggestion: use 

thermoelectric (peltier) heaters with a variac to control them.