NBSIR 88-3719

Research Information Center

^National Bureau of Standards

Gaithersburg, Maryland 20899

An Automated Maintenance

Management Program

Part I: Quantitative Assessment of

the Exterior Condition of Metal

Buildings and Roofing Systems

via Computer Image Processing

Jonathan W. Martin

Dale P. Bentz

Lawrence Kaetzel

Mary E. McKnight

U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards

National Engineering Laboratory

Center for Building Technology

Gaithersburg, MD 20899

OF

March 1988

Stimulating America s Progress

1913-1988

Prepared for: Tri-Services

Building Materials Investigation Program

NBSIR 88-3719

AN AUTOMATED MAINTENANCE

MANAGEMENT PROGRAM

PART I: QUANTITATIVE ASSESSMENT OF

THE EXTERIOR CONDITION OF METAL

BUILDINGS AND ROOFING SYSTEMS

VIA COMPUTER IMAGE PROCESSING

Jonathan W. Martin

Dale P. Bentz

Lawrence Kaetzel

Mary E. McKnight

U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards

National Engineering laboratory

Center for Building Technology

Gaithersburg, MD 20899

March 1988

Prepared for: Tri-Services

Building Materials Investigation Program

U.S. DEPARTMENT OF COMMERCE, C. William Verity, Secretary

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director

TABLE OF CONTENTS

ABSTRACT iii

LIST OF FIGURES ..................... iv

1. INTRODUCTION. 1

2. COMPUTER IMAGE PROCESSING ..... 3

2.1 IMAGE ACQUISITION 3

2.2 IMAGE DIGITALIZATION 4

2.3 COMPUTER PROCESSING OF DIGITAL IMAGES 5

2.3.1 Computer Image Processing Hardware ... 5

2.3.2 Computer Image Processing Software ... 6

2.4 IMAGE DISPLAY ..... 10

3. EXTERIOR CONDITION ASSESSMENT VIA COMPUTER IMAGE. . . 10

PROCESSING

3.1 EXTERIOR CONDITION ASSESSMENT EXAMPLES 12

3.2 SAMPLING CONSIDERATIONS 14

4. INTEGRATION OF IMAGE PROCESSING INTO THE AUTOMATED. . 15

MAINTENANCE PROGRAM

5. SUMMARY 17

6. ACKNOWLEDGEMENTS 17

7. REFERENCES. 18

APPENDIX A — TERMINOLOGY 19

APPENDIX B — IMAGE PROCESSING BIBLIOGRAPHY . 21

APPENDIX C -- SAMPLE IMAGE PROCESSING SYSTEM 2 2

CONFIGURATIONS

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ABSTRACT

Automation of a maintenance management program could result

in considerable benefits in terms of time, money, and aesthetics

to any facility's maintenance program. An integrated system

combining computerized condition assessment, database management,

and expert systems could serve to automate the maintenance

management process. One part of such a system, the use of

computer image processing to quantitatively assess the exterior

condition of buildings, is presented. Computer image processing

hardware and software are reviewed and the special concerns

present in applying image processing to condition assessment are

addressed. Examples of the capability of image processing to

quantify the degradation of coated surfaces and roofing systems

are presented. Finally, the integration of image processing into

an overall automated maintenance management program is discussed.

Keywords: Computer image processing? condition assessment;

database management; degradation; exterior envelope; sampling

procedures

.

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LIST OF FIGURES

Fig. 1. Hypothetical degradation versus time curves for two

buildings

Fig. 2. The effect of greylevel resolution on an image?

8 greylevels versus 256 greylevels.

Fig. 3. Noisy and time-averaged thermograms.

Fig. 4. Corrosion spots (original and binary)

.

Fig. 5. Side of building (original and differentiated)

.

Fig. 6. Steel Structures Painting Council No. 4 rusting standard

(original and processed)

.

Fig. 7. Federation of Societies for Coatings Technology's

chalking standard.

Fig. 8. Peeling paint from side of building (original and

processed)

.

Fig. 9. Thermograms of wet roofs (original and processed)

.

Fig. 10. Aggregate removal from a room (original and processed)

.

Fig. 11. Side of building from three different distances.

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1 . INTRODUCTION

Funds for the maintenance of facilities of military

installations are usually limited. To maximize the return on

these funds , proper maintenance decisions are required. Proper

decisions require knowing the existing condition, rate of

deterioration, and environment of all facilities and cost of

potential repairs. With this knowledge, prioritization of

maintenance actions and proper materials selections can be made.

Currently, because of limited personnel, condition assessment

procedures are very qualitative and may not even be carried out

for all facilities. Hence, procedures are needed to obtain

condition assessment quickly using the available personnel and to

archive this information for future referral. Automating the

condition assessment process using computer image processing

could be a way to obtain and preserve this information and

thereby .improve the cost effectiveness of maintenance actions.

With the rapid evolution of computer technology, creation of

an automated maintenance management program has become

economically viable. The core of the system would be a

maintenance database containing cost, material, environmental,

and degradation information. This database could be organized to

rank structures on the basis of their assessed condition,

estimated repair costs, or in other desired ways. The

quantitative assessment of the condition of a structure can be

provided by a computer image processing system. Using

photographs, videotape, or other images of a structure, computer

image processing can quantify the degradation shown in the

images. This information will then be available for automatic

input into a database management system, which can provide a

historical record of the condition of each structure in the

maintenance supervisor's inventory. The final element in the

automated maintenance system would be an expert system to extract

pertinent information from the databases and make recommendations

on materials and procedures for the repair of structures and to

minimize maintenance costs or to meet other management

objectives. These recommendations would be based on knowledge

provided by experts in the maintenance area. The integration of

these three computer tools: image processing, database

management, and expert systems constitutes an automated

maintenance management program.

This paper addresses in detail one part of the automated

system, exterior condition assessment via computer image

processing. Currently, when carried out, the exterior envelope

of structures is assessed by inspectors using subjective

descriptors such as good, poor, or failed (1) . The weaknesses of

this procedure are that assessments may be influenced by the

biases of the inspector. The assessments provided by computer

1

image processing, on the other hand, are fast and quantitative,

and should be unbiased. Since the images can be conveniently

stored in digital format, using an optical disk for example, they

can be sequentially retrieved so as to follow the progressive

degradation of a structure. This will enable the generation of a

degradation versus time curve which can be utilized in optimally

scheduling maintenance of a structure . Figure 1 shows two

hypothetical curves for the first five years of a building's

life. For Building A, the rate of degradation has levelled off,

after a high rate of initial degradation, and therefore repair

can be postponed if necessary. Conversely, Building B could

require immediate repair as it has reached a critical point

beyond which the degradation is increasing exponentially with

time.

Fig. 1. Hypothetical degradation versus time curves for two

buildings

.

Successful application of computer image processing to

exterior condition assessment requires consideration of several

factors. The computer image processing system must be selected

to serve the immediate needs of the user while allowing for

expansion. Standardized sampling procedures must be developed to

accurately assess and quantify the degradation of a structure

while minimizing the effort expended in collecting this

information. Also, different types of degradation must be

weighted differently in determining the exterior condition of a

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structure. For example, the degradation of structural components

should be given more weight than non-structural components. To

effectively execute maintenance strategies, the information

provided by image processing must be transferred to the database

system so that repair need can be prioritized and materials

selected.

This paper provides a plan for making maintenance decisions.

Image processing hardware and software are reviewed and the

special considerations for applying computer image processing to

exterior condition assessment are addressed. Examples of the

ability of computer image processing to quantify the degradation

of various structures are provided. The interfacing of the image

processing system with the rest of the automated maintenance

system is discussed. Appendices include image processing

terminology and typical imaging system configurations.

2. COMPUTER IMAGE PROCESSING

Computer image processing involves the enhancement,

analysis, and classification of images to obtain useful

information. In general, computer image processing can be

divided into four steps (2)

1) obtaining an image, 2) analog to

digital conversion of this image, 3) computer processing of the

image in digital form, and 4) conversion of the processed image

back to analog form for viewing purposes. Each of these steps

places certain requirements on the hardware and/or software

comprising the image processing system.

2.1 IMAGE ACQUISITION

While all of the above four steps are necessary, obtaining

the image is crucial in that only those features which are

present in the image can be characterized. For example, one

should not expect to quantify small cracks in a painted substrate

by photographing the whole side of a building. Here, a smaller

area will have to be photographed such that the cracks are

visible in the photograph. In addition to visual images, images

can be obtained using an infrared thermography camera (to

identify emission differences) or other techniques from which a

photographic print can be obtained. If possible, photographs

should be obtained at an angle normal to the exterior surface so

that the degradation can be accurately quantified. If this is

not done, it will be necessary to mathematically reorient the

image to produce a normal representation.

3

2.2 IMAGE DIGITALIZATION

The second step in processing an image involves converting

the image into a digital format. Generally, this is accomplished

by viewing the image source with an imaging camera whose output

is sent in a standard format (such as RS-170) to an image

digitizing board contained in the main computer. The digitized

image can then be permanently stored for later reference.

During the analog to digital conversion, the image is

resolved into a series of picture elements, pixels, whose

assigned integer value depends on the intensity of the incoming

analog signal. The key consideration in selecting an image

digitizer is the resolution, both spatial and greylevel. Spatial

resolution refers to the number of pixels in an image and is a

measure of the systems' ability to accurately isolate objects

(defects) from the background. At present digitizers with

512*512 (columns * rows) are readily available, with 1024*1024

and 4096*4096 systems being available for specialized

applications. Typical greylevel resolutions are 8, 16, 64, and

256. The human eye can resolve about 40 greylevels so that a

digitizer with a minimum greylevel resolution of 64 (6 bit

resolution) is needed to produce images which appear continuous

to a human observer. The effect of greylevel resolution is

illustrated in Figure 2 where the same image is shown with 8

greylevels and 256 greylevels. As the number of greylevels

increase, it is easier to isolate features in an image.

Fig. 2. The effect of greylevel resolution on an image: 8

greylevels versus 256 greyscale levels.

4

Other considerations for an image digitizer include the

noise level and linearity of the digitalization and compatibility

between camera and digitizer. Although software can overcome

some deficiencies in the areas of noise and linearity, it is best

to choose a digitizer with high linearity and a low noise level.

Above all, the digitizer board must be capable of accepting the

analog signal from the camera to which it is attached.

Since the storage of digitized images requires large amounts

of memory (262 Kbytes for a 512*512 image with 256 greylevels)

,

major consideration must be given to the storage capability of

the image processing system. When a large number of images must

be stored, the system should include a removable hard disk or an

optical disk. One 5 1/4 inch optical disk can typically hold up

to a Gigabyte or approximately four thousand images. Current

technology in this field (WORM -Write Once Read Many) does not

allow unwanted files to be deleted and replaced; this may be a

benefit to an automated maintenance plan, since the major

emphasis will be on archiving images for future reference.

2.3 COMPUTER PROCESSING OF DIGITAL IMAGES

While the first two steps in processing an image mainly

depend on obtaining the proper hardware and image capture, the

actual computer processing of the digitized image relies heavily

on both hardware and software. A successful system requires that

hardware and software be developed concurrently and that each

takes full advantage of the other's capabilities.

2.3.1 Computer Image Processing Hardware

The major hardware components for processing digitized

images are image processing boards, the main computer, and

peripheral hardware devices to assist the user of the system.

Image processing boards perform a variety of functions. Framebuffer boards provide the space to hold one image in active

memory where each pixel can be accessed and/or modified. Most

image processing systems contain at least one frame buffer board,

although sometimes this capability is provided within the image

digitizing board. In systems containing multiple frame buffer

boards, specialized image processing boards (containing

arithmetic logic units) provide the capability to quickly combine

two or more images (such as subtracting a background image)

.

Other specialized boards exist to generate a greylevel histogram

(a histogram of the number of pixels occupying each of the

possible greylevels) or perform other image processing functions.

The advantage of hardware-based image processing algorithms, as

opposed to software-based algorithms, is that they reduce

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processing time (some algorithms can actually be performed in

real time) and they relieve the host computer of specialized

tasks. The disadvantage is the general lack of flexibility

provided by the hardware.

The computer is the unifying element in an image processing

system. The imaging boards, peripheral devices, and software

cannot be specified until the computer has been selected. In the

past, only mainframe computers were able to handle the

calculation-intensive tasks required of an image processing

system. Although larger computers are generally faster and

provide more memory for the execution of complicated image

processing algorithms, complete systems are now available which

are centered around the personal computer found on a typical

researcher's desk. Personal computers have many advantages over

dedicated mini-, or even main frame, computer image processing

systems including portability, low cost, and 100 percent

dedication to image processing. Several image processing system

configurations along with estimated costs are provided in the

Appendix C.

Other hardware items involved in the computer processing of

digitized images are the peripheral devices ranging from line

printers, for printing analysis results, to a joystick or mouse

to select areas of an image for further processing. These items

vary from system to system and must be specified so that they are

compatible with the selected computer.

2.3.2 Computer Image Processing Software

Computer processing of digital images consists of three

stages (2)

preprocessing, enhancement, and classification.

The purpose of preprocessing is to obtain a digital image

which suitably represents the original image source. Noise

removal is one goal of preprocessing. Random noise can often be

eliminated by time-averaging a series of incoming images. If

additive random noise is present, averaging N incoming images

will improve the signal-to-noise ratio (SNR) by the square root

of N. This process is illustrated in Figure 3 which shows a

thermographic image and the improvement obtained by averaging

eight consecutive incoming frames. If the incoming image is

spatially non-uniform (e.g. the image is darker on the edges than

in the center) , image subtraction can be used to eliminate much

of the non-uniformity present in the image.

The second step in digital image processing is image

enhancement. Here, the goal is to produce an image which is

easily interpreted. Image enhancement algorithms can be either

pixel transformations (point operations) , neighborhood

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transformations (local operations) , or geometric transformations.

In point algorithms, the resultant greylevel value for each pixel

in the digitized image is dependent only on the greylevel value

of that pixel in the input image. The most often used point

operation is thresholding. In thresholding, all pixels within a

user-specified range (0-30 for example) are assigned a new

greylevel value of 0 while all other pixels outside this range

are assigned another greylevel value (255 for example) . The

result is a high contrast, so-called binary image, which will

optimally contain dark objects on a light background. These

binary images are much more readily interpretable (i.e. the size

and shape of each object can be easily determined) than the

original images as is shown in Figure 4.

Fig. 3. Noisy and time-average thermograms.

Another example of a point operation is a color lookup

table. In imaging systems capable of color (RGB — red, green,

blue) display, color can be used to highlight objects of

interest. For example, the two greyscale levels in the binary

image in Figure 4 can be depicted in color as opposed to white

and black. In designing a color lookup table, one assigns a

color triple (R,G,B) to each of a number of greylevel ranges

(e.g. 0 to 255)

.

Then, each pixel in turn is assigned a display

color based on that pixel* s original greylevel value.

In neighborhood operations, the resultant greylevel value of

each pixel depends on the original greylevel values of that pixel

and a set of surrounding pixels (such as a 3*3 box of pixels

centered at the pixel of interest) . Filtering is an example of a

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neighborhood operation. Here, the greylevel values of each pixel

in the original neighborhood are weighted by some factor, the

resultant values are summed, and the resulting sum is rescaled

and assigned to be the output greylevel value for the pixel at

the center of the neighborhood box. The time necessary to

perform a filtering operation can be reduced if an arithmetic

logic unit board contained in the image processing system can be

used for both combining images and filtering. Two enhancement

filters are the low-pass filter, used to reduce noise and smooth

the image, and the high-pass filter, used to sharpen edges.

%

Fig. 4. Corrosion spots (original and binary).

A neighborhood operation useful for detecting edges of

objects or separating objects on the basis of texture is

differentiation or gradient filtering. Here, the maximum

difference between each pixel and its surrounding eight pixel

neighborhood is assigned as the output pixel value. This

technique is illustrated in Figure 5 which shows the original and

differentiated images of the side of a building containing

corrosion defects.

In geometric transformations, the orientation, scaling, and

perspective of the original image are modified. The image may be

rotated to reorient an area of interest for further processing.

A warping operation may be useful if the original image is a

distorted representation of the image source. Here, the user

selects certain points in the image and informs the imaging

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system of where these points should actually appear in the image.

The imaging system then applies a geometric transformation to the

image to align these selected points with their desired

locations.

The final step in computer processing of digital images is

the classification or analysis of the enhanced image to obtain

the final quantitative results. Depending on the application,

the desired result may be the percentage of the image occupied by

a particular feature (defects) , the size, shape, and location of

individual features, or another quantitative measure of

degradation. If the enhanced image consists of black features on

a white background (a binary image) , these features can be easily

characterized with respect to size, shape, and location or any

other statistic of interest to the user (Figure 4 and Table 1)

.

Once such characterization is completed, the results should be

placed in a format which allows for easy manipulation and

integration into other parts of the automated maintenance

program.

Fig. 5. Side of a building (original and binary).

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2 . 4 IMAGE DISPLAY

The final step in computer image processing is to convert

the digital image back to analog form for output. Output is

generally classified into two categories: volatile or refresh

(such as a TV screen) and permanent or hardcopy (such as a

videoprinter) . The resolution and linearity of the digital-toanalog converter and display device should be as good as those of

the digitizer so that realistic images may be presented to the

user of the system. In addition, the physical size of the

display should be such that the smallest features of interest in

an image can be easily recognized. If the image processing

system is capable of color output, a color monitor or color

videoprinter would be a useful addition to the system.

While the majority of image processing systems operate on

black-and-white images, new systems are emerging which can

extract color information from the image source. Color is

represented by a color triple (red, green, blue) similar to the

color assignment made in creating a color lookup table. The

difference is that instead of each pixel being assigned a

greylevel value between 0 and 255 during digitalization, each

pixel is assigned three values between 0 and 255 (one each for

red, green, and blue) . These values are obtained by viewing the

image source with a color camera which senses polychromatic

radiation or a monochromatic camera which contains

interchangeable red, green, and blue filters.

The advantage of color image processing is in the

segmentation process, that is, separating defects from non-defect

regions. In a black and white image, this is usually

accomplished by a threshold operation, that is, the defects are

uniquely contained and can be isolated in a certain greylevel

range. Using color image processing, this separation can be made

in color space where there is often more contrast between defects

and non-defect regions. For instance, red rust stains on an

orange building are more readily separable in color space where

one can take advantage of the yellow component of the orange

color.

3. EXTERIOR CONDITION ASSESSMENT VIA COMPUTER IMAGE PROCESSING

To properly consider what features of an image processing

system are most important in building condition assessment, one

must first decide what a typical assessment procedure will be.

One possible plan would be to take pictures of the site or

structure to be assessed and return these pictures along with

identification information to a central location which contains

the image processing system. Here, a typical procedure would be

10

to take the picture of the structure from the field, digitize and

enhance the image in the laboratory, and then analyze the

resultant image to determine the size, shape, and location of

individual defects. This degradation information could then be

stored and included in a database for future reference.

The above procedure would place certain requirements on the

image processing equipment. The photographs obtained in the

field must depict accurately the degradation of the structure so

a high quality camera would be desirable. The resolution of the

image digitizer board and size of the area photographed should be

such that the smallest defects occupy several pixels in the

digitized image.

The imaging program should ideally execute both in an

interactive mode (where the operator has complete control over

the imaging process) and also in a batch mode (where a series of

commands are executed with no human intervention) . Such a system

would minimize manpower requirements in the image processing area

while still providing flexibility in the imaging process. Since

Fig. 6. Steel Structures Painting Council No. 4 rusting

standard (original and processed).

photographs often contain structural and * non-structural areas

(e.g. the sky or trees) , the image processing software should be

such that the user can execute algorithms on only parts of

images. As stated earlier, a joystick or mouse provides a

convenient means for rapidly selecting "areas of interest" within

an image.

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Obviously, for an automated maintenance program, computer

storage of images will be a primary concern. Since it is

desirable to store all of the digitized images for the

development of structural histories, the imaging system should

include a high capacity storage system such as a removable hard

disk or an optical disk. Furthermore, the images must be

uniquely and concisely labelled so that relevant images can be

easily identified for future retrieval.

3.1 EXTERIOR CONDITION ASSESSMENT EXAMPLES

Computer image processing has been successfully applied to

evaluating the degradation of painted surfaces (3) and roofing

systems. For painted surfaces, Bentz and Martin (1987)

demonstrated that computer image processing was applicable to the

wide variety of coating defects by quantitatively evaluating the

photographic visual standards for the different defect types

(4,5) (such as blistering, chalking, cracking, mildew, peeling,

and rusting) . The quantification of the ASTM D610, No. 4

photographic standard for rusting is illustrated in Figure 6

which shows the original and processed images. The percent area

rusted determined by the image processing system was 13 percent.

No. 8 No. S No. 4 No. 2

Fig. 7. Federation of Societies for Coatings Technology's

chalking standard.

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Quantification of chalking degradation is different from the

standard measure of degradation in that the percent area degraded

is not a relevant parameter. Here, the discoloration of the

cloth rubbed over the painted surface was quantified by

determining the average greylevel of the discolored area. Figure

7 shows the photographic standard for chalking. The percent

whiteness calculated for the No. 8, 6, 4, and 2 standards in

Figure 7 are 10, 35, 67, and 98 percent, respectively. This

demonstrates the ability of computer image processing to quantify

degradation in an application where degradation area is not a

critical measure.

Fig. 8. Peeling paint from side of building (original and

processed)

.

Computer image processing has also been used to quantify the

degradation of coated structures and roofing systems. Figure 8

shows the original and processed images of the side of a metal

building from which the applied coating has peeled. Via computer

image processing, the peeled area was determined to occupy 48

percent of the area shown. In figure 9, a thermographic image of

a building roof was photographed from a helicopter, the building

had two appurtenances each of which was processed separately to

isolate the wet regions (16 percent of the smaller and 35 percent

of the larger roof are wet) . Aggregate removal from a roofing

system was also quantified using computer image processing as

illustrated in Figure 10 ( 13 percent of the surface area shown

was free of aggregate). In general, computer image processing

has the capability of quantifying any building material defects

which can be observed in the source image.

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3.2 SAMPLING CONSIDERATIONS

In addition to image processing concerns, exterior condition

assessment will require the development of sampling plans so that

only a portion of a building has to be photographed to quantify

its degradation state. One aspect of the sampling procedure is

the size of the sampling area. Obviously, fewer photographs will

be required if large areas are photographed. At the same time, a

photograph is worthless if it does not depict the degradation

state of a structure (i.e. if defects present are not visible in

the photograph). Thus, the optimum sampling area is the largest

in which individual defects can be readily discerned. If the

defects are large, e.g. due to peeling, large sampling areas can

be used; whereas, if the defects are small, e.g. pinpoint

corrosion or small blisters, then small sampling areas must be

photographed. This approach has been supported by photographing

a building from three different distances and analyzing the same

Fig. 9. Thermogram of wet roofs (original and processed).

area in each photograph for percent degradation. Figure 11

illustrates this procedure for one area, showing the area

analyzed in each of the three images in binary (black-or-white)

form. The degradation percentages determined in each case (15,

14, and 16 percent) are similar enough that any of the

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photographs could be used for accurately evaluating the

degradation of this structure. In each photograph, the cracks

present in the coating cladded to the building are readily

distinguishable

.

Fig. 10. Aggregate removal from a room (original and

processed)

.

The other aspect of the sampling problem is how many

photographs of various areas of a structure are necessary to

quantify the degradation state of that structure. This will

depend on whether the defects are clustered in various areas or

randomly located over the structure. This is the type of

information which should become available as the automated

maintenance program is implemented and degraded spots of various

structures are identified. These trouble spots can then be

photographed along with a random sampling of the rest of the

structure to accurately estimate the degradation.

4. INTEGRATION OF IMAGE PROCESSING INTO THE AUTOMATED

MAINTENANCE PROGRAM

As stated in the introduction, image processing capabilities

are only part of an automated maintenance management program. It

is therefore important that the image processing system interface

with database management and expert system modules contained

within the automated program. The quantitative degradation

information provided by image processing should be included in

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Fig. 11. Side of building from three different distances.

the database and the knowledge base so that maintenance decisions

can be made on the basis of extent of degradation. By crossreferencing the degradation data with geographical and

environmental data, it should be possible to qualitatively

determine the effects of these factors on the degradation of

various types of structures. The database should also contain an

index of the images which have been stored so that the user of

the system can recall images showing the degradation history of a

structure.

The expert system could also benefit from the data provided

by the image processing system. As the degradation is monitored

over time, the success of various maintenance strategies can be

evaluated and the knowledge base updated to reflect the new

knowledge. The ability of the automated maintenance system to

evaluate material performance, for example, may assist in the

selection of materials for new structures. The stored images

could also be accessed by the expert system for demonstrating

various degradation modes and for training new personnel.

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5 . SUMMARY

The application of computer image processing to the exterior

condition assessment of metal buildings and roofing systems has

been discussed. A summary of computer image processing,

including discussions of hardware and software, has been provided

as background information for maintenance personnel. The special

concerns in applying computer image processing to condition

assessment have been addressed and the ability of computer image

processing to successfully quantify the degradation of coated

surfaces and roofs has been demonstrated. Lastly, the potential

of an integrated system combining image processing with database

management and expert systems to automate a maintenance

management program was discussed.

As with any new endeavor, initially a large amount of

resources will need to be allocated to set up an image processing

system for exterior condition assessment. Since there are a

large number of image processing systems (hardware and software)

on the market, however, it should be possible to initiate the

program fairly quickly. Once standard procedures are developed

the condition assessment process should become straightforward.

6 . ACKNOWLEDGEMENTS

This investigation was conducted under the Tri-Services

Building Materials Investigational Program and was jointly

sponsored by the Headquarters, US Army Corps of Engineers; US

Navy, Naval Facilities Engineering Command, and US Air Force, Air

Force Engineering and Services Center. The authors also

acknowledge the diligent efforts of their colleagues, Mr. Edward

Embree and Mr. Mike Hocker, for obtaining and processing the

images used in this paper.

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7 . REFERENCES

(1) McKnight, M.E., Mathey, R.G., and Drisko, R.W. , "Preliminary

Recommendations for Maintenance of Factory Coated Metal

Siding and Roofing," NBSIR 85-3193, National Bureau of

Standards, June 1985.

(2) Schowengerdt , R.A. , "Techniques for Image Processing and

Classification in Remote Sensing," Academic Press, Inc., New

York (1983)

.

(3) Bentz, D.P., and Martin, J.W. , "Using the Computer to

Analyze Coating Defects," Journal of Protective Coatings and

Linings 4(5): 38 (1987).

(4) Federation of Societies for Coatings Technology, "Pictorial

Standards for Coatings Defects", Federation of Societies

for Coatings Technology, 1315 Walnut Street, Suite 832,

Philadelphia, PA (1979).

(5) SSPC-Vis 2-68T, "Standard Method of Evaluating Degree of

Rusting on Painted Steel Surfaces," Steel Structures

Painting Council, 4400 Fifth Av.

,

Pittsburgh, PA (1970).

18
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APPENDIX A- TERMINOLOGY

BATCH IMAGE PROCESSING- Image processing involving the execution

of a series of commands to access, preprocess, enhance, and

classify and image.

BINARY IMAGE - A high-contrast image in which each pixel is

assigned one of two values (e.g. O-black and 255-white)

.

DIFFERENTIATION - An image processing algorithm in which each

pixel is assigned the absolute value of the maximum

difference between the original greylevel values of that

pixel and each of its neighbors.

EXPERT SYSTEM - An interactive computerized system that uses

KNOWLEDGE and INFERENCE procedures to solve specialized

problems that require the knowledge of an expert.

FILTERING - An image processing neighborhood operation in which

the output value for each pixel depends on the greylevel

values of a neighborhood of surrounding pixels and a

discrete weighting function (the filter) . Examples include

low and high pass filters.

FRAME BUFFER - The memory allocated for the storage of the

greylevel values of all the pixels in a digitized image.

GREYLEVEL HISTOGRAM - A histogram of the number of pixels in

an area of interest assigned to each of the available

greylevel values. Peaks in such a histogram often

correspond to the greylevel values occupied by defects

of interest.

GREYLEVEL RESOLUTION - The number of different greylevel values

an image processing system is capable of producing. Typical

values are 8, 16, 64, and 256.

IMAGE CLASSIFICATION - The analysis of an image to obtain the

desired final result such as the number, size, and type

of defects.

IMAGE ENHANCEMENT - The modification of an image to produce a

resultant image in which features of interest are easily

detectable.

INFERENCE - The process of matching or linking knowledge (facts

or data) to rules to form conclusions specific to the

problem.

19
-

,

'

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INTERACTIVE IMAGE PROCESSING - Image processing in which an

operator subjectively performs sequential operations to

access, preprocess, enhance, and classify an image.

KNOWLEDGE BASE - Facts and rules contained in an expert system

specific to the problem domain. This information serves as

the basis for decision making.

LOOKUP TABLE - An input/output system where the output value (s)

are dependent on the input entry (or entries) . IRS tax

tables are an example of a lookup table (LUT) where the

output (the tax you owe) is dependent on the inputs (your

income and number of dependents)

.

NEIGHBORHOOD OPERATIONS (Local operations) - Image processing

algorithms in which the output value of each pixel is a

function of the original greylevel values of that pixel

and those pixels in its neighborhood. Filtering

is an example of a neighborhood operation.

PIXEL - A pixel is an individual display unit. In image

digitalization, each pixel is assigned a greylevel value

(e.g. 0-255) based on the intensity of the incoming signal.

POINT OPERATIONS (Pixel operations) - Image processing algorithms

in which the output greylevel value for each pixel is a

function only of that pixel's original greylevel value.

An example of a point operation is thresholding.

PREPROCESSING - The execution of image processing program, such

as time averaging and background subtraction, in advance of

another program which will perform most of the programming.

SPATIAL RESOLUTION - The ability of a digitized image to separate

two close objects in an image; that is, it is a measure of

the image processing system's ability to define the details

of an image.

THRESHOLDING - An image processing point operation in which each

pixel is assigned one output value (e.g. 0) if its original

greylevel value is below a specified threshold value and a

second value (e.g. 255) if its original output value is

above this threshold. The result is a BINARY IMAGE.

VOLATILE DISPLAY (Refresh display) - An output display in which

the image is continuously updated such as a TV or CRT

screen.

20
-

v'.

'
APPENDIX B - IMAGE PROCESSING BIBLIOGRAPHY

Bole, L. ; Kulpa, Z., "Digital Image Processing Systems,"

Springer- Verlag, New York (1981)

.

Castleman, K. R. , "Digital Image Processing," Prentice-Hall,

Inc., Englewood Cliffs, NJ (1979)

.

Chang, N., "Image Analysis and Image Database Management,"

UMI Research Press, Ann Arbor, MI (1981)

.

Duff, M.J.B.; Levaldi, S., "Languages and Architectures for Image

Processing," Academic Press, Inc., New York (1981).

Ekstrom, M. P. [ed. ] , "Digital Image Processing Techniques,"

Academic Press, Inc., New York (1984).

Gonzalez, R. and Wintz, P. , "Digital Image Processing," AddisonWesley Publishing Co., Inc., Reading Mass. (1977).

Green, W. B. , "Digital Image Processing- A Systems Approach," Van

Nostrand Reinhold Co., New York (1983).

Hord, R. M. , "Digital Image Processing of Remotely Sensed

Data," Academic Press, Inc., New York (1982).

Huang, T.S., "Advances in Computer Vision and Image Processing,"

Jai Press, Inc. (1986)

.

Offen, P. J. , "VLSI Image Processing," McGraw-Hill Book Co., New

York (1985).

Oosterlink, A.? Danielsson, P. [eds.], "Architecture and

Algorithms for Digital Image Processing," Society for Photooptical Instrumentation Engineers, Bellingham, WA (1983)

.

Schowengerdt , R.A. , "Techniques for Image Processing and

Classification in Remote Sensing," Academic Press, Inc., New

York (1983).

Young, T.S.; Fu, K. , "Handbook of Pattern Recognition and Image

Processing," Academic Press, Inc., New York (1986).

21
;
APPENDIX C- SAMPLE IMAGE PROCESSING SYSTEM CONFIGURATIONS

PC -BASED IMAGE PROCESSING SYSTEM

Fig. Cl. Sample PC-based computer image processing system.

MINICOMPUTER-BASED IMAGE PROCESSING SYSTEM

Fig. C2 . Sample Minicomputer-based computer image

processing system.

22
‘ '' 1
U.S. DEPT. OF COMM. 1. PUBLICATION OR 2. Performing Organ. . . port No. 3. Publication Date

BIBLIOGRAPHIC DATA

REPORT NO.

SHEET (See instructions) NBS IR 88-3719 MARCH .1988

4. TITLE AND SUBTITLE

An Automated Maintenance Management Program

Part I: Quantitative Assessment of the Exterior Condition of Metal Buildings and

Roofing Systems Via Computer Image Processing

5o AUTHOR(S) , , T, ..... Uj. Jonathan W. Martin, Dale P. Bentz, Lawrence Kaetzel and Mary E. McKnight

6. PERFORMING ORGANIZATION (If joint or other than NBS. see instructions)

NATIONAL BUREAU OF STANDARDS

U.S. DEPARTMENT OF COMMERCE

GAITHERSBURG, MD 20899

7. Contract/Grant No.

8 . Type of Report & Period Covered

9. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street. City. State. ZIP)

Tri-Services Building Materials Investigational Program

Department of Defense

10. SUPPLEMENTARY NOTES

J Document describes a computer program; SF-185, FIPS Software Summary, is attached.

11. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a si gn i fi cant

bi bliography or literature survey, mention it here)

Automation of a maintenance management program could result in considerable benefits

in terms of time, money, and aesthetics to any facilities' maintenance program. An

integrated system combining computerized condition assessment, database management, and

expert systems could serve to automate the maintenance management process. One part

of such a system, the use of computer image processing to quantitatively assess the

exterior condition of buildings, is presented. Computer image processing hardware and

software are reviewed and the special concerns present in applying image processing to

condition assessment are addressed. Examples of the capability of image processing to

quantify the degradation of coated surfaces and roofing systems are presented. Finally

the integration of image processing into an overall automated maintenance management

program is discussed.

12. KEY WORDS (S/x to twelve entries; alphabetical order; capitalize only proper names; and separate key words by semicolon s) automated maintenance management program; computer image processing; condition

assessment; database management; degradation; exterior envelope; sampling procedures

13. AVAILABILITY

Uni i mi ted n For Official Distribution. Do Not Release to NTIS

I Order From Superintendent of Documents, U.S. Government Printing Office, Washington, D.C.

20402.

Order From National Technical Information Service (NTIS), Springfield, VA. 22161

14. NO. OF

PRINTED PAGES

28

15. Price

$11.95

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