70,766 research outputs found
DeMoN: Depth and Motion Network for Learning Monocular Stereo
In this paper we formulate structure from motion as a learning problem. We
train a convolutional network end-to-end to compute depth and camera motion
from successive, unconstrained image pairs. The architecture is composed of
multiple stacked encoder-decoder networks, the core part being an iterative
network that is able to improve its own predictions. The network estimates not
only depth and motion, but additionally surface normals, optical flow between
the images and confidence of the matching. A crucial component of the approach
is a training loss based on spatial relative differences. Compared to
traditional two-frame structure from motion methods, results are more accurate
and more robust. In contrast to the popular depth-from-single-image networks,
DeMoN learns the concept of matching and, thus, better generalizes to
structures not seen during training.Comment: Camera ready version for CVPR 2017. Supplementary material included.
Project page:
http://lmb.informatik.uni-freiburg.de/people/ummenhof/depthmotionnet
Using Self-Contradiction to Learn Confidence Measures in Stereo Vision
Learned confidence measures gain increasing importance for outlier removal
and quality improvement in stereo vision. However, acquiring the necessary
training data is typically a tedious and time consuming task that involves
manual interaction, active sensing devices and/or synthetic scenes. To overcome
this problem, we propose a new, flexible, and scalable way for generating
training data that only requires a set of stereo images as input. The key idea
of our approach is to use different view points for reasoning about
contradictions and consistencies between multiple depth maps generated with the
same stereo algorithm. This enables us to generate a huge amount of training
data in a fully automated manner. Among other experiments, we demonstrate the
potential of our approach by boosting the performance of three learned
confidence measures on the KITTI2012 dataset by simply training them on a vast
amount of automatically generated training data rather than a limited amount of
laser ground truth data.Comment: This paper was accepted to the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), 2016. The copyright was transfered to IEEE
(https://www.ieee.org). The official version of the paper will be made
available on IEEE Xplore (R) (http://ieeexplore.ieee.org). This version of
the paper also contains the supplementary material, which will not appear
IEEE Xplore (R
Optical Coherence Tomography Angiography Vessel Density in Healthy, Glaucoma Suspect, and Glaucoma Eyes.
PurposeThe purpose of this study was to compare retinal nerve fiber layer (RNFL) thickness and optical coherence tomography angiography (OCT-A) retinal vasculature measurements in healthy, glaucoma suspect, and glaucoma patients.MethodsTwo hundred sixty-one eyes of 164 healthy, glaucoma suspect, and open-angle glaucoma (OAG) participants from the Diagnostic Innovations in Glaucoma Study with good quality OCT-A images were included. Retinal vasculature information was summarized as a vessel density map and as vessel density (%), which is the proportion of flowing vessel area over the total area evaluated. Two vessel density measurements extracted from the RNFL were analyzed: (1) circumpapillary vessel density (cpVD) measured in a 750-μm-wide elliptical annulus around the disc and (2) whole image vessel density (wiVD) measured over the entire image. Areas under the receiver operating characteristic curves (AUROC) were used to evaluate diagnostic accuracy.ResultsAge-adjusted mean vessel density was significantly lower in OAG eyes compared with glaucoma suspects and healthy eyes. (cpVD: 55.1 ± 7%, 60.3 ± 5%, and 64.2 ± 3%, respectively; P < 0.001; and wiVD: 46.2 ± 6%, 51.3 ± 5%, and 56.6 ± 3%, respectively; P < 0.001). For differentiating between glaucoma and healthy eyes, the age-adjusted AUROC was highest for wiVD (0.94), followed by RNFL thickness (0.92) and cpVD (0.83). The AUROCs for differentiating between healthy and glaucoma suspect eyes were highest for wiVD (0.70), followed by cpVD (0.65) and RNFL thickness (0.65).ConclusionsOptical coherence tomography angiography vessel density had similar diagnostic accuracy to RNFL thickness measurements for differentiating between healthy and glaucoma eyes. These results suggest that OCT-A measurements reflect damage to tissues relevant to the pathophysiology of OAG
Subjective Annotation for a Frame Interpolation Benchmark using Artefact Amplification
Current benchmarks for optical flow algorithms evaluate the estimation either
directly by comparing the predicted flow fields with the ground truth or
indirectly by using the predicted flow fields for frame interpolation and then
comparing the interpolated frames with the actual frames. In the latter case,
objective quality measures such as the mean squared error are typically
employed. However, it is well known that for image quality assessment, the
actual quality experienced by the user cannot be fully deduced from such simple
measures. Hence, we conducted a subjective quality assessment crowdscouring
study for the interpolated frames provided by one of the optical flow
benchmarks, the Middlebury benchmark. We collected forced-choice paired
comparisons between interpolated images and corresponding ground truth. To
increase the sensitivity of observers when judging minute difference in paired
comparisons we introduced a new method to the field of full-reference quality
assessment, called artefact amplification. From the crowdsourcing data, we
reconstructed absolute quality scale values according to Thurstone's model. As
a result, we obtained a re-ranking of the 155 participating algorithms w.r.t.
the visual quality of the interpolated frames. This re-ranking not only shows
the necessity of visual quality assessment as another evaluation metric for
optical flow and frame interpolation benchmarks, the results also provide the
ground truth for designing novel image quality assessment (IQA) methods
dedicated to perceptual quality of interpolated images. As a first step, we
proposed such a new full-reference method, called WAE-IQA. By weighing the
local differences between an interpolated image and its ground truth WAE-IQA
performed slightly better than the currently best FR-IQA approach from the
literature.Comment: arXiv admin note: text overlap with arXiv:1901.0536
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