3,360 research outputs found
A Detail Based Method for Linear Full Reference Image Quality Prediction
In this paper, a novel Full Reference method is proposed for image quality
assessment, using the combination of two separate metrics to measure the
perceptually distinct impact of detail losses and of spurious details. To this
purpose, the gradient of the impaired image is locally decomposed as a
predicted version of the original gradient, plus a gradient residual. It is
assumed that the detail attenuation identifies the detail loss, whereas the
gradient residuals describe the spurious details. It turns out that the
perceptual impact of detail losses is roughly linear with the loss of the
positional Fisher information, while the perceptual impact of the spurious
details is roughly proportional to a logarithmic measure of the signal to
residual ratio. The affine combination of these two metrics forms a new index
strongly correlated with the empirical Differential Mean Opinion Score (DMOS)
for a significant class of image impairments, as verified for three independent
popular databases. The method allowed alignment and merging of DMOS data coming
from these different databases to a common DMOS scale by affine
transformations. Unexpectedly, the DMOS scale setting is possible by the
analysis of a single image affected by additive noise.Comment: 15 pages, 9 figures. Copyright notice: The paper has been accepted
for publication on the IEEE Trans. on Image Processing on 19/09/2017 and the
copyright has been transferred to the IEE
Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction pipeline
In this paper, we show how absolute orientation measurements provided by
low-cost but high-fidelity IMU sensors can be integrated into the KinectFusion
pipeline. We show that integration improves both runtime, robustness and
quality of the 3D reconstruction. In particular, we use this orientation data
to seed and regularize the ICP registration technique. We also present a
technique to filter the pairs of 3D matched points based on the distribution of
their distances. This filter is implemented efficiently on the GPU. Estimating
the distribution of the distances helps control the number of iterations
necessary for the convergence of the ICP algorithm. Finally, we show
experimental results that highlight improvements in robustness, a speed-up of
almost 12%, and a gain in tracking quality of 53% for the ATE metric on the
Freiburg benchmark.Comment: CVPR Workshop on Visual Odometry and Computer Vision Applications
Based on Location Clues 201
Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomography
Total variation (TV) is a powerful regularization method that has been widely
applied in different imaging applications, but is difficult to apply to diffuse
optical tomography (DOT) image reconstruction (inverse problem) due to complex
and unstructured geometries, non-linearity of the data fitting and
regularization terms, and non-differentiability of the regularization term. We
develop several approaches to overcome these difficulties by: i) defining
discrete differential operators for unstructured geometries using both finite
element and graph representations; ii) developing an optimization algorithm
based on the alternating direction method of multipliers (ADMM) for the
non-differentiable and non-linear minimization problem; iii) investigating
isotropic and anisotropic variants of TV regularization, and comparing their
finite element- and graph-based implementations. These approaches are evaluated
on experiments on simulated data and real data acquired from a tissue phantom.
Our results show that both FEM and graph-based TV regularization is able to
accurately reconstruct both sparse and non-sparse distributions without the
over-smoothing effect of Tikhonov regularization and the over-sparsifying
effect of L regularization. The graph representation was found to
out-perform the FEM method for low-resolution meshes, and the FEM method was
found to be more accurate for high-resolution meshes.Comment: 24 pages, 11 figures. Reviced version includes revised figures and
improved clarit
Attention and Anticipation in Fast Visual-Inertial Navigation
We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to
estimate its state using an on-board camera and an inertial sensor, without any
prior knowledge of the external environment. We consider the case in which the
robot can allocate limited resources to VIN, due to tight computational
constraints. Therefore, we answer the following question: under limited
resources, what are the most relevant visual cues to maximize the performance
of visual-inertial navigation? Our approach has four key ingredients. First, it
is task-driven, in that the selection of the visual cues is guided by a metric
quantifying the VIN performance. Second, it exploits the notion of
anticipation, since it uses a simplified model for forward-simulation of robot
dynamics, predicting the utility of a set of visual cues over a future time
horizon. Third, it is efficient and easy to implement, since it leads to a
greedy algorithm for the selection of the most relevant visual cues. Fourth, it
provides formal performance guarantees: we leverage submodularity to prove that
the greedy selection cannot be far from the optimal (combinatorial) selection.
Simulations and real experiments on agile drones show that our approach ensures
state-of-the-art VIN performance while maintaining a lean processing time. In
the easy scenarios, our approach outperforms appearance-based feature selection
in terms of localization errors. In the most challenging scenarios, it enables
accurate visual-inertial navigation while appearance-based feature selection
fails to track robot's motion during aggressive maneuvers.Comment: 20 pages, 7 figures, 2 table
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