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depth camera. The measuring distance of the depth camera was from 1.75m to
2.15m and the baseline distance between HD left and right cameras was 20cm. For
test images, we captured BEAR and ACTOR images using the hybrid camera sys-
tem. Especially, since the BEAR images included a scene that a big yellow bear
doll embraced a small yellow bear doll, they were good to evaluate depth accuracy
of ROI enhanced depth image generated by hybrid camera system. Figure 9 shows
the test images.
Color image
Depth image
Left image
Right image
(a) BEAR images
Color image
Depth image
Left image
Right image
(b) ACTOR images
Fig. 9 Test images
For comparison with the previous depth estimation methods, we estimated ROI
depth images of left images by applying the state-of-the-arts stereo matching
methods, which are belief propagation [18], graph cuts [22], dynamic program-
ming [23], and scan-line optimization [3]. For background depths, we only used
SAD method based on color segmentation. We have also made a ROI ground truth
depth image of BEAR images by projecting the depth data acquired by a 3D laser
range finder [24] onto the camera plane of the left camera. Figure 10 shows the
ground truth depth image and the results of depth estimation for BEAR images.
As objective evaluation methodology, we used two quality measures based on
known ground truth data [3]: the root-mean squared error R E and the percentage of
bad matching pixels B A . Here, bad matching means that the depth value is differ-
ent from the corresponding ground truth depth value by more than one pixel value.
Table 3 shows the result of R E , B A , and the B A difference between the stereo
matching algorithms and the hybird camera method, B Diff .
When we compared the accuracy of ROI depths generated by the hybrid cam-
era system with belief propagation, which was the best among previous methods,
our method was more accurate by approximately 2.1 for R E and 11.2 % for B A than
belief propagation for BEAR images.
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