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NIST Technical Note 1630

Evaluation of Image Quality of

Thermal Imagers used by

the Fire Service

Francine Amon

Andrew Lock

I 00 I %l WiMii# I National Institute of Standards and Technology • U.S. Department of Commerce

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 ofStandards and Technology

February, 2009

/ *^4rES o*

'^

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
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 (AO 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.
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.

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 80x60 VOx

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 discemable
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

Table 2. Horizontal and vertical FOF results.

Imager Vertical FOF (degrees) Horizontal FOK(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.

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) FOK 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
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 lEC 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 fi-om 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]

NU ^ (1)

Where a is the standard deviation and /u 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 A^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. vVL^ values for a range of set point temperatures.

Imager NU\ °C iV;730°C NU\00°C NU \60°C NU260°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 uncert

and Type I Secfion 5 f

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

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

or detailed uncertainty analysis.
Imager 1 Imager 3 Imager 4 Imager 5

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

temperature is 100 °C.

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.

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.
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 1 6-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 1 6-bit TIFF files.

2. Convert the TIFF files to grayscale as defined per lEC 6 1 966-2- 1 ( 1 999) [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:

f^'TfP _ max min

max min (2)

where I^ax and In,i„ 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 7r/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
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 °C, and listed in Table 3, is subtracted from the KfTF 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.

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.

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

uncertainty analysis.
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.

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 1 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.
2. Convert the TIFF files to grayscale as defined per International Electrotechnical

Commission (lEC) 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 fi-equency 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 Secfion 2.2.

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

Parabolic Mirror:

Focal length 1.0 m

Off axis distance 0.176 m

Off axis angle 10°

Clear aperture 0.2 m

Heated bars

O O O O

178 nnm square

extended area

~ blackbody"

3-

CD—

3

9L3

0} CD

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

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

of the image.

9
2.5 Thermal Sensitivity

The thermal sensitivity (TS) test measures the abihty 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 (T„) below 260 °C, one blackbody was held at a constant

temperature. The other blackbody was initially set at T„ - 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 T„ and ten more images were collected. The blackbody was then set at T„ + 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 T„ ± 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 lEC 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 T„ ± 0.05 °C or T„ ±

0.20 °C. Exclude or remove symbols, icons, and text fi-om 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 (ju) of the ROI. Retain the original pixel intensity

values for later use.

10
Rank the ten // values collected at each target temperature, then discard the lowest fj. 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.

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

target temperature, i.e., at r„ and 7; ± 0.05 °C or T„ ± 0.20 °C.

Calculate the contrast between the average pixel intensity at r„ and Tn - 0.05 °C or 0.20 °C. Record this value. Calculate the contrast between the average pixel intensity at Tn and r„ + 0.05 °C or 0.20 °C. Record this value. Average the two contrast values.

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. Table 6. T lermal sensitivity test results.

Imager TS\ °C r^30°C r^-ioo^c r^l60°C r>S260°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.

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

there may be a discemable 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 r„ was increased. Three tests were conducted at each r„ 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.

11
>!iSjfT!i?;ffrrr'!5»T»r5jj^t5t^t;tt!r*t?t*{»i^^

^':

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 °C target, the image in the center is of a 100.00 °C target, and the image on

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

viewed on a printed page.

0.10

0.09

0.08

0.07

0.06

tt 0.05 mw4-> C

o 0.04

u

0.03

0.02

0.01

0.00

N r X

•

u

^

m 1

1 '1 1 1 1 — —

I

500 1000 1500 2000 2500 3000 3500

Tn (mK)

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

temperature was 100 °C. 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
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 (Q, 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-l correspondence. The measurement of contrast is fiindamental 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:

(3)

where P is the probability of successfijUy 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 = 0.2563 + C + B + R + N + C^ +B^ +R' +N' +CB + CR + CN + BR + BN + RN (4)

13
where C, B, R, and A'^ 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.

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

Coefficient Teiin

0.2563 intercept

0.8737 C

0.4595 B

15.85 R

-2.567 N

-8.359 d

-4.631 B'

-242.2 R'

2.893 N'

3.779 CB

48.71 CR

11.60 CN

34.91 BR

-8.016 BN

-5.008 RN

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.

14
0.8-

iy"

0.7

-

Model

Prediction

o

O

cn

o)

1

1

2gl'

.'-'22

y'iA

0.4

- y' 5

0.3- ,'

0.2

-

1J9'''

1 1

' 1' 1' I ' 1 0.2 0.3 0.4 0.5 0.6 Observed Result

0.7 0.8

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.

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 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.

15
0.005

Spatial Resolution Value

0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05

m

c

re a

reE

0.9

0.8

0.7

0.6

0.5

0.4

0.3 ± 0.2

0.1

1 1 1 1 1 ^ 1 i 1 ^

^I^^^^==*""^5:<^^'''^'^^-'—^

^^"^"'''^^

/^ —-^"^ ^

\v

^—^^^^^

^\v

P{N) P(C) ---riu; KiK)

r 1 1 1 1 1 1i 1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Primary Factor Value (excluding Spatial Resolution)

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

other factors are held constant at their average values.

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

16
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 A/^L'^ 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 A^.

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 A^.

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.

17
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 A^.

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 °C and 160 °C, 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.
1

0.9

0.8

0.7 i 3 I

I "-5 •a i T

c

> 0.5

fO3

a 0.4

QibO

reE 0.3

X

0.2 -j

0.1

i

&

I

¥

Imager 1 X Imager 2 ^Imagers irimager 4 ©Imagers

50 100 150 200

Target Temperature (°C)

250 300

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.

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.

19
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.

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.

20
1

0.9

0.8

s

M 0.6 1

f 0.5 T3

§ 0.4

wu

I

0.3

0.2

0.1

1 TC3

!

1

!

1 d

"

f

!

i

!i 3

' ! i ; 1 1 ! 1 50 100 150 200 250 300

Hot Surface Temperature TO

350 4CK» 450

Figure 11. Effective Temperature Range (£"77?) for Imager 3 plotted in terms of the image

quality indicator, P. Each colored line represents the results of one test.

21
1

0.9

0.8

0.7

rou 0.6 XJ

•"C

>•

4-' 0.5

TO3

a 0.4

(U00

nj£ U.3

0.2

0.1

TIC4

50 100 150 200 250 300

Hot Surface Temperature {°C)

350 400 450

Figure 12. Effective Temperature Range (ETR) for Imager 4 plotted in terms of the image

quality indicator, P. Each colored line represents the results of one test.

22
Figure 13. Effective Temperature Range {ETR) for Imager 5 plotted in terms of the image

quality indicator, P. Each colored line represents the results of one test.

3.4.4 Thermal Sensitivity

The thermal sensitivity {TS) results, expressed in terms of the image quality indicator, P, are shown below in Figure 14. All the imagers appeared to perform best at the cool nominal

temperature of 1 °C. There is no significant trend in the test results at nominal target

temperatures above 1 °C. These chaotic results indicate the need to look more closely at the

temperature difference {T„) used in the test. Theoretically, if the imager's detector is the limiting

system component, the imager should show a decrease in P when operating in the low sensitivity

mode (shorter integration time), which is not apparent in Figure 14.

23
0.9

0.8

^- 0.7 O

n

.y 0.6 a

c

"(5 a 0.4 -

<u

ns£ 0.3

0.2

0.1

# Imager 1 X Imager 2 9 imager 3 _^_ Imager 4 ® Imager 5

«

1

m

<A

i

X

f

^

50 100 150 200

Nominal Target Temperature (°C)

250 300

'. 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.

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 T„ in this test (CI Systems SR-800) has a temperature accuracy

of 8 mK for temperatures below 50 °C and 15 mK for temperatures above 50 °C, however, its temperature range is restricted to °C to 175 °C. For the 260 °C test point, the CI Systems SR80-7HT has a temperature accuracy of 0.5 °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 l.l °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

24
30 °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 °C could not be expected to be

resolved to less than in 1 °C increments. Following this logic, the smallest value of T„ 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 °C and a 50 mK detector thermal

sensitivity, is listed in Table 8.

'able 8. Minimum resolvable T„ due to imager component limitations.

Nominal Target Temperature (°C) Minimum T„ Limitation

1 94 mK display

30 50 mK detector

100 293 mK display

160 527 mK display

260 918 mK display

Given the minimum T„ 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 T„. This is done for one imager in Figure 15. The

data presented in Figure 1 5 shows that, when plotted in terms of P, holding all the other image

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

while the uncertainty in the measurement increases substantially.

25
Figure 15. Thermal sensitivity as a function of increasing delta T {T„). 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.

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 T„ 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.

26
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 (2a).

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.

Table 9. Components used in uncertainty analysis.

Component

Type A

Uncertainty Type B Uncertainty

Combined

Uncertainty

Total

Expanded

Uncertainty

FOWangle a 0.25 degrees a + 0.25 degrees 0.6 degrees

SR-800

Blackbody

G, pixel

intensities in

ROI

T < 50 °C: ± 8 mK

T>50°C:±15mK

a + 8mK

a+15mK

2(a + 8 mK)

2(a+15mK)

SR-80-7HT

Blackbody

a, pixel

intensities in

ROI

± 0.5 °C a + 0.5 °C 2(a + 0.5 °C)

IRCon

Blackbody

a, pixel

intensities in

ROI

T<316°C:2°C

T>316T:0.5%

a + 2°C

a + 0.5 %

2(a + 2 °C)

2(a + 0.5 %)

Type J thermocouples a

Greater of 1.1 °C or

0.4 %

a+l.l°Cor

a + 0.4 %

2(a+I.l°Cor

a + 0.4 %)

TypeK

Thermocouples a

Greater of 1.1 °C or

0.4 %

a+l.l°Cor

a + 0.4 %

2(a+l.l°Cor

a + 0.4 %)

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

27
service applications. These tests were Field of View (FOV), Nonuniformity (NU), Spatial

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

The FOVtQSt 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

FOF 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 °C and 160 °C 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 °C, the performance of the imagers drops drastically and then becomes erratic at target

temperature above 100 °C. 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 {T„) used in this test showed

that the imagers don't discriminate significantly between a T„ of 50 mK and a T„ of 2000 mk.

28
References

[1] Amon, F., et al., Performance Metricsfor Fire Fighting Thermal Imaging Cameras -

Small- and Full-Scale Experiments. 2008

[2] Hoist, G.C., Testing and Evaluation ofInfrared Imaging Systems. 1998, Winter Park, FL:

JCD Publishing.

[3] Lock, A. and F. Amon. Measurement ofthe Nonuniformity ofFirst Responder Thermal

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

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

Responders. 2006(NIST Technical Note 1474).

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

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

[6] Lock, A. and F. Amon. Application ofSpatial 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.

29
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.page7pid-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/Hova_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^N82E 168202 11244

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=l&sq=desc&InitialSearch=yes&O=jsp%2Fcatalog.isp&A^search

&Q=*&bhs=t&Go.x=0«S:Go.v-0&Go-submit

Articulated camera positioning arm. http://www.bhphotovideo.com/bnh/controller/home?0=search&A~search&Q=&shs=ma

nfrotto+by+bogen+imaging+articulated+ami+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+C 1 575B%2FD200B%2FE600%2FD520B%2FBAG+-

+AVD800KIT&sb=ps&pn=l&sq=desc&InitialSearch=ves&O=isp%2Fproductlist.isp&A

=search&Q=*&bhs=t&Go.x=24&Go.v= 1 1 &Go=submit

2. Data Acquisition System.

Controller card.

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

30
Chassis.

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

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

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

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

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

http://sine.ni.eom/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/SAl .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" siHcone rubber heating blanket for the background comes from:

http://www.briskheat.com/p-347-srl-srp-silicone-mbber-heating-blankets.aspx (use a variac to control the temperature)

Metal target stencil.

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

6. Blackbodies.

One extended area blackbody that can go as low as °C, such as one of these:

http://www.ci-svstems.com/htmls/article.aspx7C2004- 12724&BSP= 12547 (4", extended

temperature range).

http://www.sbir.conVblackbodv 1 3 1 32.htm (4", -30 to 100 °C temperature range)

http://www.mikroninfrared.com/Catalog.aspx7id-l 164&ekmensel-c580fa7b 8 28 1150

(4", -5 to 170 °C temperature range)

http://vyww.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-svstems.com/Htmls/article.aspx7C2004=12726«feBSP=12547

(Temperature range 30 °C to 550 °C)

http://vyw^.sbir.com/blackbodv 4000.htm

31
(Temperature range 30 °C to 550 °C)

http://www.mikroninfrared.corn/Catalog.aspx?id=2392&ekriiensel=c580fa7b_8_28_1150

_2

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

(Temperature range 30 °C to 550 °C)

Effective Temperature Range Bars.

4 bars: 1.3 cm (V2 in) thick and set 1.3 cm (Yi in) apart; they are 15.2 cm (6 in) long.

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

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

32