This paper discusses a proposed processing technique for combining video imagery with auxiliary sensor information. The latter greatly simplifies image processing by reducing complexity of the transformation model. The mosaics produced by this technique are adequate for many applications, in particular habitat mapping. The algorithm is demonstrated through simulations and hardware configuration is described

Cutter, Randy G, Jr.

Huff, Lloyd C

Rzhanov, Yuri

English

UNH Scholars' Repository

University of New HampshireUniversity of New Hampshire Scholars' RepositoryCenter for Coastal and Ocean Mapping Center for Coastal and Ocean Mapping10-2001Sensor-Assisted Video Mosaicing for SeafloorMappingYuri RzhanovUniversity of New Hampshire, Durham, Yuri.Rzhanov@unh.eduRandy G. Cutter Jr.University of New Hampshire, DurhamLloyd C. HuffUniversity of New Hampshire, DurhamFollow this and additional works at: https://scholars.unh.edu/ccomPart of the Computer Sciences Commons, and the Oceanography and Atmospheric Sciences andMeteorology CommonsThis Conference Proceeding is brought to you for free and open access by the Center for Coastal and Ocean Mapping at University of New HampshireScholars' Repository. It has been accepted for inclusion in Center for Coastal and Ocean Mapping by an authorized administrator of University of NewHampshire Scholars' Repository. For more information, please contact nicole.hentz@unh.edu.Recommended CitationRzhanov, Yuri; Cutter, Randy G. Jr.; and Huff, Lloyd C., "Sensor-Assisted Video Mosaicing for Seafloor Mapping" (2001). InternationalConference on Image Processing (ICIP). 228.https://scholars.unh.edu/ccom/228SENSOR-ASSISTED VIDEO MOSAICING FOR SEAFLOOR MAPPING K Rzhanov, G. R. Cuttel; L. H u f '  Center for Coastal and Ocean Mapping (C-COM) University of New Hampshire Durham 03824, USA email: yuri.rzhanov@unh.edu, gcutter@cisunix.unh.edu, Ihuff@cisunix.unh.edu ABSTRACT This paper discusses a proposed processing technique for combining video imagery with auxiliary sensor informa- tion. The latter greatly simplifies image processing by re- ducing complexity of the transformation model. The mo- saics produced by this technique are adequate for many ap- plications, in particular habitat mapping. The algorithm is demonstrated through simulations and hardware configura- tion is described. 1. INTRODUCTION The ideal product from seafloor imaging efforts would be a complete 3D reconstruction of the scene with the resolution required to resolve essential details, and accurate georefer- encing such that subsequent maps could be compared di- rectly. Essentially, that would provide real seafloor images from any perspective at the finest scales (the basin with the water removed). To accomplish that, accurate bathymetric data must be acquired first, the terrain reconstructed, then optical imagery draped over. The scene would then be ready for virtual reality applications and 3D visualization which could provide additional insights with innovative analyses. This technique would definitely find many applications in marine biology, geology, and underwater archaeology. Im- plementation is the difficulty. 1.1. Constraints and alternatives While some steps already have been taken in that direction [see, for example, [l]], field data collection for the ap- proach requires highly stable platform (submarine, AUV), expensive microbathymetric equipment and precise under- water navigation. 3D reconstruction is in principle best, but is it necessary, especially considering potential costs versus economic value of substrates, habitats and fisheries that are mapped? In the present paper we will concentrate on a technique allowing construction of a map of an underwater site: pro- jection of all scene features onto a two dimensional planar surface [2]. We will show that this can be achieved by inex- pensive means using consumer-available products, such as a hand-held video camera and a set of attitude sensors, and that the product is valuable for seafloor mapping. The goal is alignment of 2D images of the 3D world. 2. SPECIFIC APPLICATIONS Two reasons for collecting bottom video imagery are to char- acterize seafloor habitats and to provide information for ground- truthing acoustic data. Video imagery provides data relat- ing substrate characteristics, organisms (density, spatial pat- terns and variability), and associations of organisms with substrate. This data can reliably characterize many ben- thic, epibenthic and, less accurately, fish populations. Al- though video imagery does not provide direct information about habitat utilization, it can sometimes be inferred since many benthic and fish species tend to associate with and utilize particular bottom types and substrate characteristics for shelter, nutrition, or reproduction during some stage in their lives (e.g. [3]). Because of that, mapping the substrate and biological features over a range of scales is important for habitat surveys, and video mosaics provide the most complete and accurate representation of the substrate from scales of millimeters to hundreds of meters. 2.1. Difficulties with underwater video imaging Habitat mapping is most important in estuaries, coastal, and continental shelf regions because of the reliance of human- exploited fisheries species upon these areas. Unfortunately, conditions in these parts of the oceans are worst for opti- cal imaging because of suspended sediments, particles and planktonic organisms, all of which impede optical paths and scatter or absorb light. In addition, underwater scenes are much less accessible than those on the land - investigators have to wear diving equipment or be restrained by an under- water vehicle, or remotely operated tools must be used. Even when good quality video is acquired, analysis is limited to either review of the video recording or exam- 0-7803-6725- 1/0 1/$10.00 02001 IEEE 41 1 ination of individual still images constructed from video frames. In either case, sample time interval and travel dis- tance must be maximized to avoid duplicating counts, and minimized to avoid missing areas. These problems can be overcome using sequential video frames, like those shown in Fig. 1, to construct a video mosaic map from which multi- scale measurements can be made). Fig. 1. Examples of frames from underwater video. 2.2. Virtues of a video mosaic record Mosaics provide continuous data. Broad areal coverage, by multiple track overlap, and narrow transect coverage can both provide direct information at scales larger than point sample data. By comparison, point sample data can only imply spatial scales, larger than the single frame field of view. Mosaics allow mul.ti-scale analysis without the possi- bility of duplicate counts. Counts from mosaics may suffer from distortions caused during mosaicing, however popula- tion estimates will be more accurate than those made from counts of randomly sekcted frames. Although randomly selected frames may be more reliable for identifying cer- tain small features, population estimates will incur error un- less the population follows an idealized distribution that is known a priori. Many benthic and epibenthic populations have aggregated distributions, such that they are dense in a few areas but sparse for most, and therefore are unlikely to be enumerated accurar.ely using single image selections. That is especially true fcsr the case of larger organisms. In addition to representing the organisms and features, mosaics also allow distances between features to be measured di- rec tl y. 2.3. Video image mosaicing In the scope of this paper we assume that the scene to be mapped is flat. Errors iri the mosaic due to terrain effects may be neglected for our application as seafloor areas mapped using devices employing optical imagery are relatively flat and small local distortions do not affect the biological fea- ture data. The analysis errors incurred because of a flat- bottom assumption are 'less than those related to naviga- tional inaccuracies that must be considered if separate frames were used as opposed to a mosaic. In order to generate the product, we employ either a diver-held or towed video camera in an underwater hous- ing. Both these camera platforms are inexpensive and have 6 degrees of freedom in their motion. Translation, yaw, and heave do not pose problems for map construction. Those motions are described by an affine model and all four pa- rameters of the model (scaling, rotation and translation in two orthogonal directions) can be estimated by automatic featureless Fourier transform-based technique [4, 51. How- ever, any tilt of the camera poses a serious complication. Translation of the camera point of view combined with cam- era tilt (pitch and roll) cause perspective distortions of the acquired images that require more complex transformations such as 8 parameter projective transformation [6]. There are two means of recovering these parameters: (1) manu- ally choosing (at least) four tie points in each image to be co-registered (perhaps by pre-arranged insertion of features into the scene) and solving eight equations for eight un- known parameters [ 7 ] ;  or (2) formulation of a non-quadratic minimization problem for pixel value differences and con- sequent numerical solution using Levenberg-Marquardt [ 11 or other scheme. Both methods have drawbacks: human in- tervention in the first, and in second case inhomogeneous illumination associated with camera and light source mo- tion, which makes the optimization problem be ill-defined. In this paper we propose to use additional information from consumer-grade attitude sensors to effectively reduce the complexity of the transformation model: from projec- tive to affine. The latter can be efficiently solved with robust automatic methods and is not influenced by inhomogeneous lighting. By means of simulating video acquisition with sig- nificant tilt angles, we show that although acquired frames do not appear visually distorted, they cannot be reliably as- sembled in a mosaic. However, if tilt angles can be esti- mated, even with modest accuracy, frames can be corrected for projection distortion and the quality of the mosaic is im- proved dramatically. Simulations have demonstrated a rela- tionship between errors in tilt angle correctors and quality of the produced mosaic. 3. INSTRUMENTATION The video camera used for this work is the Sony DCR- TRV310 Digital Video Camera. It is mounted in a diver held underwater housing manufactured by Ikalite. The minimum auxiliary data to support the improved mosaicing process is a time series of camera roll and pitch that is synchronized with the video frames. The synchronization must be main- tained at the frame level, even though successive frames in a video are virtually never employed in developing a mo- saic (because not all of them are necessary). Simulations of the proposed rolllpitch corrected mosaicing process indi- cated that the accuracy of the rolllpitch information should 412 be on the order of one degree, or better. The benefit of us- ing roll/pitch information that is better than one degree does not justify the instrumentation cost to achieve an accuracy of 0.1 degree, rather than one degree. This is especially true in light of other factors like pixel resolution and the losses in fidelity of the images, associated with rotating the video frames into a common, north-up frame of reference. The Precision Navigation Model TCiM2-50 was selected for the required rolVpitch information. Its size and power re- quirements are well suited for inclusion within the camera’s underwater housing. The rolypitch accuracy of the unit in a static environment is 0.4 degree. An important consider- ation in the choice of the TCM2-50 is that it also provides an azimuth relative to magnetic north, which is accurate to 0.5 degree in a static environment. The processing algo- rithm is capable of determining rotations between succes- sive frames, although any sequence of rotations is subject to cumulative, random walk errors. Camera orientation rel- ative to magnetic north can be utilized to develop the mo- saic in a north-up reference and to constrain the algorithm’s determinations of frame-to-frame rotation. The TCM2-SO also has the desirable characteristic of being programmable with respect to sample rates, averaging times, and output in- tervals. It is intended to output data twice per second, that are one-second averages of the parameters, which have been sampled at 16 Hz. The RS232 formatting of the TCM2-50 output is an es- sential element for recording the heading, roll, and pitch information in synchronization with the video frames. The TCM2-SO output is reduced in amplitude and ac coupled into the audio input channel of the video recorder. Internal to the Sony DCR-TRV310, the signal input is digitized to 12 bits at a bit sample rate of 32 kHz and recorded onto the tape, in synchronization with the video imagery. The head- ing, roll, and pitch data are automatically retrieved through reconstruction, detection, and decoding the time series wave- form of the RS232 output message. This videotape technique of recordinghetrieving RS232 messages in synchronization with video imagery has been successfully demonstrated. Figure 2 presents part of the reconstructed time series of a navigational position output message from a GPS receiver that had been recorded on videotape. Videotaped RS232 messages are automatically recovered with less than one bit error per million. 4. RESULTS Figure 3,a shows square frames generated from a raster im- age with inclined pinhole camera model (pitch equals 10 degrees). Distortions are not visually noticeable, but the mosaic constructed from these frames has significant curl (Fig. 3,b). (Reasons for this curl and ways to correct the mosaic are discussed in [SI). Correction of frames for pitch Fig. 2. Time series of the GPS message. (Fig. 3,c) allows for creation of an almost ideal mosaic with- out the unrealistic curl (Fig. 3,d). Fig. 3. Separate frames used in simulation (a, c) and result- ing mosaics (b, d). Controlled use of moderate camera inclinations may sig- nificantly increase the area covered by a single-pass video survey. Fig. 4 shows schematically a towed body with a video camera attached. The camera has rolled periodically which results in variation in the imaged areas, as shown in the figure by several white outlines. A larger area is imaged with finer pixel resolution that could be provided by a fixed camera orientation and either a wide angle lens or a greater distance to target. Application of the technique to the mosaicing of video imagery collected underwater (even with modest accuracy) produces mosaics of high quality, as the one shown in Fig. 5. 413 Fig. 4. Schematic view of video frames acquisition process. 5. C’ONCLUSIONS This paper has presented a technique for video mosaicing of underwater images. Auxiliary information from attitude sensors has been incorporated into the algorithm to sim- plify image processing and to assist in creation of mosaics. The method has been applied to marine biological measure- ments and provides a cost-effective alternative to previously reported methods. The technique shows much promise for gathering new types of information from the seabed by us- ing video imaging as opposed to traditional acoustic imag- 6. REFERENCES H. Singh, L. Whitcomb, D. Yoerger, 0. Pizarro, ”Mi- crobathymetric mapping from underwater vehicles in the deep ocean.” Computer Vision and Image Under- standing, vol. 79, pp. 143-161,2000. Y. Rzhanov, L. M. Linnett, R. Forbes, ”Underwater mosaicing for seabed mapping.” 2000 International conference on Image Processing, ICIP2000, Vancou- ver, Canada, vol. 1, pp. 224-227,2000. D. B.Packer, T. Hoff, ”Life history, habitat parameters, and essential habitat of mid-Atlantic summer floun- der.” AFS Symposium 22, pp. 134-147, 1999. B. S. Reddy and B. N. Chatterji, ”An FFT-based tech- nique for translation, rotation and scale-invariant im- age registration.” IEEE Transactions on Image Pro- cessing, vol. 5 ,  pp. 1266-1271, 1996. H. S. Sawhney and R.Kumar, ”True multi-image alignment and its application to mosaicing and lens distortion correction.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, pp. 235- 243,1999. Fig. 5. Mosaic of approximately 40 frames. (Courtesy of R.J.Diaz, Virginia Institute of Marine Science.) [6] S. Mann and R. W. Picard, ”Video orbits of the projec- tive group: a simple approach to featureless estimation of parameters.” IEEE Transactions of Image Process- ing, vol. 6, pp. 1281-1295, 1997. [7] R. Radke, P. Ramadge, ”Efficiently estimating projec- tive transformations.” 2000 International conference on Image Processing, ICIP2000, Vancouver, Canada, vol. 1, pp. 232-235,2000. [8] A. Zomet, S. Peleg, C. Arora, ”Rectified mosaicing: Mosaics without the curl.” IEEE Conference on Com- puter Vision and Pattern Recognition, CVPR2000, Los Alamitos, USA, vol. 2, pp. 459-465,2000. 414 

Sensor-Assisted Video Mosaicing for Seafloor Mapping

University of New Hampshire

University of New Hampshire
University of New Hampshire Scholars' Repository
Center for Coastal and Ocean Mapping Center for Coastal and Ocean Mapping
10-2001
Sensor-Assisted Video Mosaicing for Seafloor
Mapping
Yuri Rzhanov
University of New Hampshire, Durham, Yuri.Rzhanov@unh.edu
Randy G. Cutter Jr.
University of New Hampshire, Durham
Lloyd C. Huff
University of New Hampshire, Durham
Follow this and additional works at: https://scholars.unh.edu/ccom
Part of the Computer Sciences Commons, and the Oceanography and Atmospheric Sciences and
Meteorology Commons
This Conference Proceeding is brought to you for free and open access by the Center for Coastal and Ocean Mapping at University of New Hampshire
Scholars' Repository. It has been accepted for inclusion in Center for Coastal and Ocean Mapping by an authorized administrator of University of New
Hampshire Scholars' Repository. For more information, please contact nicole.hentz@unh.edu.
Recommended Citation
Rzhanov, Yuri; Cutter, Randy G. Jr.; and Huff, Lloyd C., "Sensor-Assisted Video Mosaicing for Seafloor Mapping" (2001). International
Conference on Image Processing (ICIP). 228.
https://scholars.unh.edu/ccom/228
SENSOR-ASSISTED VIDEO MOSAICING FOR SEAFLOOR MAPPING 
K Rzhanov, G. R. Cuttel; L. H u f '  
Center for Coastal and Ocean Mapping (C-COM) 
University of New Hampshire 
Durham 03824, USA 
email: yuri.rzhanov@unh.edu, gcutter@cisunix.unh.edu, Ihuff@cisunix.unh.edu 
ABSTRACT 
This paper discusses a proposed processing technique for 
combining video imagery with auxiliary sensor informa- 
tion. The latter greatly simplifies image processing by re- 
ducing complexity of the transformation model. The mo- 
saics produced by this technique are adequate for many ap- 
plications, in particular habitat mapping. The algorithm is 
demonstrated through simulations and hardware configura- 
tion is described. 
1. INTRODUCTION 
The ideal product from seafloor imaging efforts would be a 
complete 3D reconstruction of the scene with the resolution 
required to resolve essential details, and accurate georefer- 
encing such that subsequent maps could be compared di- 
rectly. Essentially, that would provide real seafloor images 
from any perspective at the finest scales (the basin with the 
water removed). To accomplish that, accurate bathymetric 
data must be acquired first, the terrain reconstructed, then 
optical imagery draped over. The scene would then be ready 
for virtual reality applications and 3D visualization which 
could provide additional insights with innovative analyses. 
This technique would definitely find many applications in 
marine biology, geology, and underwater archaeology. Im- 
plementation is the difficulty. 
1.1. Constraints and alternatives 
While some steps already have been taken in that direction 
[see, for example, [l]], field data collection for the ap- 
proach requires highly stable platform (submarine, AUV), 
expensive microbathymetric equipment and precise under- 
water navigation. 3D reconstruction is in principle best, but 
is it necessary, especially considering potential costs versus 
economic value of substrates, habitats and fisheries that are 
mapped? 
In the present paper we will concentrate on a technique 
allowing construction of a map of an underwater site: pro- 
jection of all scene features onto a two dimensional planar 
surface [2]. We will show that this can be achieved by inex- 
pensive means using consumer-available products, such as 
a hand-held video camera and a set of attitude sensors, and 
that the product is valuable for seafloor mapping. The goal 
is alignment of 2D images of the 3D world. 
2. SPECIFIC APPLICATIONS 
Two reasons for collecting bottom video imagery are to char- 
acterize seafloor habitats and to provide information for ground- 
truthing acoustic data. Video imagery provides data relat- 
ing substrate characteristics, organisms (density, spatial pat- 
terns and variability), and associations of organisms with 
substrate. This data can reliably characterize many ben- 
thic, epibenthic and, less accurately, fish populations. Al- 
though video imagery does not provide direct information 
about habitat utilization, it can sometimes be inferred since 
many benthic and fish species tend to associate with and 
utilize particular bottom types and substrate characteristics 
for shelter, nutrition, or reproduction during some stage in 
their lives (e.g. [3]). Because of that, mapping the substrate 
and biological features over a range of scales is important 
for habitat surveys, and video mosaics provide the most 
complete and accurate representation of the substrate from 
scales of millimeters to hundreds of meters. 
2.1. Difficulties with underwater video imaging 
Habitat mapping is most important in estuaries, coastal, and 
continental shelf regions because of the reliance of human- 
exploited fisheries species upon these areas. Unfortunately, 
conditions in these parts of the oceans are worst for opti- 
cal imaging because of suspended sediments, particles and 
planktonic organisms, all of which impede optical paths and 
scatter or absorb light. In addition, underwater scenes are 
much less accessible than those on the land - investigators 
have to wear diving equipment or be restrained by an under- 
water vehicle, or remotely operated tools must be used. 
Even when good quality video is acquired, analysis is 
limited to either review of the video recording or exam- 
0-7803-6725- 1/0 1/$10.00 02001 IEEE 41 1 
ination of individual still images constructed from video 
frames. In either case, sample time interval and travel dis- 
tance must be maximized to avoid duplicating counts, and 
minimized to avoid missing areas. These problems can be 
overcome using sequential video frames, like those shown 
in Fig. 1, to construct a video mosaic map from which multi- 
scale measurements can be made). 
Fig. 1. Examples of frames from underwater video. 
2.2. Virtues of a video mosaic record 
Mosaics provide continuous data. Broad areal coverage, by 
multiple track overlap, and narrow transect coverage can 
both provide direct information at scales larger than point 
sample data. By comparison, point sample data can only 
imply spatial scales, larger than the single frame field of 
view. Mosaics allow mul.ti-scale analysis without the possi- 
bility of duplicate counts. Counts from mosaics may suffer 
from distortions caused during mosaicing, however popula- 
tion estimates will be more accurate than those made from 
counts of randomly sekcted frames. Although randomly 
selected frames may be more reliable for identifying cer- 
tain small features, population estimates will incur error un- 
less the population follows an idealized distribution that is 
known a priori. Many benthic and epibenthic populations 
have aggregated distributions, such that they are dense in 
a few areas but sparse for most, and therefore are unlikely 
to be enumerated accurar.ely using single image selections. 
That is especially true fcsr the case of larger organisms. In 
addition to representing the organisms and features, mosaics 
also allow distances between features to be measured di- 
rec tl y. 
2.3. Video image mosaicing 
In the scope of this paper we assume that the scene to be 
mapped is flat. Errors iri the mosaic due to terrain effects 
may be neglected for our application as seafloor areas mapped 
using devices employing optical imagery are relatively flat 
and small local distortions do not affect the biological fea- 
ture data. The analysis errors incurred because of a flat- 
bottom assumption are 'less than those related to naviga- 
tional inaccuracies that must be considered if separate frames 
were used as opposed to a mosaic. 
In order to generate the product, we employ either a 
diver-held or towed video camera in an underwater hous- 
ing. Both these camera platforms are inexpensive and have 
6 degrees of freedom in their motion. Translation, yaw, and 
heave do not pose problems for map construction. Those 
motions are described by an affine model and all four pa- 
rameters of the model (scaling, rotation and translation in 
two orthogonal directions) can be estimated by automatic 
featureless Fourier transform-based technique [4, 51. How- 
ever, any tilt of the camera poses a serious complication. 
Translation of the camera point of view combined with cam- 
era tilt (pitch and roll) cause perspective distortions of the 
acquired images that require more complex transformations 
such as 8 parameter projective transformation [6]. There 
are two means of recovering these parameters: (1) manu- 
ally choosing (at least) four tie points in each image to be 
co-registered (perhaps by pre-arranged insertion of features 
into the scene) and solving eight equations for eight un- 
known parameters [ 7 ] ;  or (2) formulation of a non-quadratic 
minimization problem for pixel value differences and con- 
sequent numerical solution using Levenberg-Marquardt [ 11 
or other scheme. Both methods have drawbacks: human in- 
tervention in the first, and in second case inhomogeneous 
illumination associated with camera and light source mo- 
tion, which makes the optimization problem be ill-defined. 
In this paper we propose to use additional information 
from consumer-grade attitude sensors to effectively reduce 
the complexity of the transformation model: from projec- 
tive to affine. The latter can be efficiently solved with robust 
automatic methods and is not influenced by inhomogeneous 
lighting. By means of simulating video acquisition with sig- 
nificant tilt angles, we show that although acquired frames 
do not appear visually distorted, they cannot be reliably as- 
sembled in a mosaic. However, if tilt angles can be esti- 
mated, even with modest accuracy, frames can be corrected 
for projection distortion and the quality of the mosaic is im- 
proved dramatically. Simulations have demonstrated a rela- 
tionship between errors in tilt angle correctors and quality 
of the produced mosaic. 
3. INSTRUMENTATION 
The video camera used for this work is the Sony DCR- 
TRV310 Digital Video Camera. It is mounted in a diver held 
underwater housing manufactured by Ikalite. The minimum 
auxiliary data to support the improved mosaicing process is 
a time series of camera roll and pitch that is synchronized 
with the video frames. The synchronization must be main- 
tained at the frame level, even though successive frames in 
a video are virtually never employed in developing a mo- 
saic (because not all of them are necessary). Simulations 
of the proposed rolllpitch corrected mosaicing process indi- 
cated that the accuracy of the rolllpitch information should 
412 
be on the order of one degree, or better. The benefit of us- 
ing roll/pitch information that is better than one degree does 
not justify the instrumentation cost to achieve an accuracy 
of 0.1 degree, rather than one degree. This is especially true 
in light of other factors like pixel resolution and the losses 
in fidelity of the images, associated with rotating the video 
frames into a common, north-up frame of reference. 
The Precision Navigation Model TCiM2-50 was selected 
for the required rolVpitch information. Its size and power re- 
quirements are well suited for inclusion within the camera’s 
underwater housing. The rolypitch accuracy of the unit in 
a static environment is 0.4 degree. An important consider- 
ation in the choice of the TCM2-50 is that it also provides 
an azimuth relative to magnetic north, which is accurate to 
0.5 degree in a static environment. The processing algo- 
rithm is capable of determining rotations between succes- 
sive frames, although any sequence of rotations is subject 
to cumulative, random walk errors. Camera orientation rel- 
ative to magnetic north can be utilized to develop the mo- 
saic in a north-up reference and to constrain the algorithm’s 
determinations of frame-to-frame rotation. The TCM2-SO 
also has the desirable characteristic of being programmable 
with respect to sample rates, averaging times, and output in- 
tervals. It is intended to output data twice per second, that 
are one-second averages of the parameters, which have been 
sampled at 16 Hz. 
The RS232 formatting of the TCM2-50 output is an es- 
sential element for recording the heading, roll, and pitch 
information in synchronization with the video frames. The 
TCM2-SO output is reduced in amplitude and ac coupled 
into the audio input channel of the video recorder. Internal 
to the Sony DCR-TRV310, the signal input is digitized to 
12 bits at a bit sample rate of 32 kHz and recorded onto the 
tape, in synchronization with the video imagery. The head- 
ing, roll, and pitch data are automatically retrieved through 
reconstruction, detection, and decoding the time series wave- 
form of the RS232 output message. 
This videotape technique of recordinghetrieving RS232 
messages in synchronization with video imagery has been 
successfully demonstrated. Figure 2 presents part of the 
reconstructed time series of a navigational position output 
message from a GPS receiver that had been recorded on 
videotape. Videotaped RS232 messages are automatically 
recovered with less than one bit error per million. 
4. RESULTS 
Figure 3,a shows square frames generated from a raster im- 
age with inclined pinhole camera model (pitch equals 10 
degrees). Distortions are not visually noticeable, but the 
mosaic constructed from these frames has significant curl 
(Fig. 3,b). (Reasons for this curl and ways to correct the 
mosaic are discussed in [SI). Correction of frames for pitch 
Fig. 2. Time series of the GPS message. 
(Fig. 3,c) allows for creation of an almost ideal mosaic with- 
out the unrealistic curl (Fig. 3,d). 
Fig. 3. Separate frames used in simulation (a, c) and result- 
ing mosaics (b, d). 
Controlled use of moderate camera inclinations may sig- 
nificantly increase the area covered by a single-pass video 
survey. Fig. 4 shows schematically a towed body with a 
video camera attached. The camera has rolled periodically 
which results in variation in the imaged areas, as shown in 
the figure by several white outlines. A larger area is imaged 
with finer pixel resolution that could be provided by a fixed 
camera orientation and either a wide angle lens or a greater 
distance to target. 
Application of the technique to the mosaicing of video 
imagery collected underwater (even with modest accuracy) 
produces mosaics of high quality, as the one shown in Fig. 5. 
413 
Fig. 4. Schematic view of video frames acquisition process. 
5. C’ONCLUSIONS 
This paper has presented a technique for video mosaicing 
of underwater images. Auxiliary information from attitude 
sensors has been incorporated into the algorithm to sim- 
plify image processing and to assist in creation of mosaics. 
The method has been applied to marine biological measure- 
ments and provides a cost-effective alternative to previously 
reported methods. The technique shows much promise for 
gathering new types of information from the seabed by us- 
ing video imaging as opposed to traditional acoustic imag- 
6. REFERENCES 
H. Singh, L. Whitcomb, D. Yoerger, 0. Pizarro, ”Mi- 
crobathymetric mapping from underwater vehicles in 
the deep ocean.” Computer Vision and Image Under- 
standing, vol. 79, pp. 143-161,2000. 
Y. Rzhanov, L. M. Linnett, R. Forbes, ”Underwater 
mosaicing for seabed mapping.” 2000 International 
conference on Image Processing, ICIP2000, Vancou- 
ver, Canada, vol. 1, pp. 224-227,2000. 
D. B.Packer, T. Hoff, ”Life history, habitat parameters, 
and essential habitat of mid-Atlantic summer floun- 
der.” AFS Symposium 22, pp. 134-147, 1999. 
B. S. Reddy and B. N. Chatterji, ”An FFT-based tech- 
nique for translation, rotation and scale-invariant im- 
age registration.” IEEE Transactions on Image Pro- 
cessing, vol. 5 ,  pp. 1266-1271, 1996. 
H. S. Sawhney and R.Kumar, ”True multi-image 
alignment and its application to mosaicing and lens 
distortion correction.” IEEE Transactions on Pattern 
Analysis and Machine Intelligence, vol. 21, pp. 235- 
243,1999. 
Fig. 5. Mosaic of approximately 40 frames. (Courtesy of 
R.J.Diaz, Virginia Institute of Marine Science.) 
[6] S. Mann and R. W. Picard, ”Video orbits of the projec- 
tive group: a simple approach to featureless estimation 
of parameters.” IEEE Transactions of Image Process- 
ing, vol. 6, pp. 1281-1295, 1997. 
[7] R. Radke, P. Ramadge, ”Efficiently estimating projec- 
tive transformations.” 2000 International conference 
on Image Processing, ICIP2000, Vancouver, Canada, 
vol. 1, pp. 232-235,2000. 
[8] A. Zomet, S. Peleg, C. Arora, ”Rectified mosaicing: 
Mosaics without the curl.” IEEE Conference on Com- 
puter Vision and Pattern Recognition, CVPR2000, Los 
Alamitos, USA, vol. 2, pp. 459-465,2000. 
414 


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Sensor-Assisted Video Mosaicing for Seafloor Mapping

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