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Fishery Bulletin 108(3) 
Figure 4 
Computerized display of the stereo processing options for the drop stereo-video with a custom-built application written 
in the Matlab computing language. Synchronous images were extracted from videos taken by two cameras and used to 
estimate fish length. Images were taken in Zhemchug Ridges in the eastern Bering Sea in July 2008. 
the percent difference in multiple measurements of the 
same fish was 0.076. 
Analysis of still-frame stereo images 
Ten deployments were made with the still-frame system, 
and -200 fish were measured per deployment. Catches 
consisted almost exclusively of walleye pollock (>99%). A 
comparison between the length frequencies derived from 
the stereo analysis (/z = 360) and physical measurements 
of fish captured in the codend (n=1260, Fig. 9) showed 
that optical sampling approximates the length-frequency 
distribution of fish caught, despite the smaller sample 
size for optical sampling. 
In addition to length measurements, the stereo analy- 
sis provided data on walleye pollock orientation and 
their relative position within the trawl. Quantitative 
descriptions of the distribution of tilt and yaw angles 
were easily calculated by using the same points in im- 
ages (head and tail) derived for fish lengths (Fig. 10). 
To calculate the position of fish within the trawl addi- 
tional corresponding points along the trawl panel were 
identified and their three-dimensional coordinates were 
determined by the triangulation process outlined above 
(Fig. 5). 
Discussion 
The potential of stereo cameras for measuring marine 
organisms has been shown in many studies (i.e., Shortis 
et al., 2000; Flarvey et al., 2003), but here we present 
a description of the complete implementation of stereo 
cameras, including equipment costs (Table 1), image 
analysis process, and expected precision in data from 
these systems. The two stereo-camera systems described 
here were studied for their potential to provide infor- 
mation to augment fisheries assessment surveys in 
Alaska. Specifically, the stereo-camera systems in our 
study provided species and length data for untrawlable 
regions located within bottom-trawl survey boundaries 
and provide a new method for studying the behavior 
of fish in a midwater trawl. Our main goal was to 
present field-tested methods to provide quantifiable 
image-based data for fisheries surveys and our results 
may help similar research with stereo-camera-based 
sampling systems. 
The video-drop system was useful for estimating 
rockfish size and species composition in field tests in 
Alaska. Error rates for size were on the order of 8.2% 
or less, which equates to about 2.5 cm for a 30-cm fish. 
Compared with other studies with error rates of -0.1% 
to 0.7% in stereo-video systems (Harvey et ah, 2002; 
Harvey et al., 2003; Shortis et al., 2009), the measure- 
ment error rate in our study was high. This rate repre- 
sents systematic error most likely caused by the need 
to remove cameras from the housing after each deploy- 
ment because a slight misalignment of the cameras 
in relation to the position at calibration would reduce 
the precision of the measurements. Ruff et al. (1995) 
report an achievable level of precision in measuring 
fish of 3.5%, based on repeat measurements of indi- 
viduals, which is also better than the 5.9% observed 
in our study. The error rates also compare well to the 
rates of 1-5% for measuring rigid items with parallel 
lasers (Rochet et al., 2006). However, only fish on or 
near which the parallel laser beams are projected can 
be measured. This restriction limits the measurement 
sample size. In contrast, any fish simultaneously viewed 
by both cameras in a stereo-camera system can be mea- 
