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Process Not Product: The ICOMOS Ename Charter (2008) and the Practice of Heritage Stewardship
Courtesy CRM: The Journal of Heritage Stewardship, published by the National Park Service for the heritage community
Evaluation of CNN-based Single-Image Depth Estimation Methods
While an increasing interest in deep models for single-image depth estimation
methods can be observed, established schemes for their evaluation are still
limited. We propose a set of novel quality criteria, allowing for a more
detailed analysis by focusing on specific characteristics of depth maps. In
particular, we address the preservation of edges and planar regions, depth
consistency, and absolute distance accuracy. In order to employ these metrics
to evaluate and compare state-of-the-art single-image depth estimation
approaches, we provide a new high-quality RGB-D dataset. We used a DSLR camera
together with a laser scanner to acquire high-resolution images and highly
accurate depth maps. Experimental results show the validity of our proposed
evaluation protocol
Nonlinear Peculiar-Velocity Analysis and PCA
We allow for nonlinear effects in the likelihood analysis of peculiar
velocities, and obtain ~35%-lower values for the cosmological density parameter
and for the amplitude of mass-density fluctuations. The power spectrum in the
linear regime is assumed to be of the flat LCDM model (h=0.65, n=1) with only
Om_m free. Since the likelihood is driven by the nonlinear regime, we "break"
the power spectrum at k_b=0.2 h/Mpc and fit a two-parameter power-law at k>k_b.
This allows for an unbiased fit in the linear regime. Tests using improved mock
catalogs demonstrate a reduced bias and a better fit. We find for the Mark III
and SFI data Om_m=0.35+-0.09$ with sigma_8*Om_m^0.6=0.55+-0.10 (90% errors).
When allowing deviations from \lcdm, we find an indication for a wiggle in the
power spectrum in the form of an excess near k~0.05 and a deficiency at k~0.1
h/Mpc --- a "cold flow" which may be related to a feature indicated from
redshift surveys and the second peak in the CMB anisotropy. A chi^2 test
applied to principal modes demonstrates that the nonlinear procedure improves
the goodness of fit. The Principal Component Analysis (PCA) helps identifying
spatial features of the data and fine-tuning the theoretical and error models.
We address the potential for optimal data compression using PCA.Comment: 15 pages, LaTex, in Mining the Sky, July 31 - August 4, 2000,
Garching, German
Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network
With more and more household objects built on planned obsolescence and
consumed by a fast-growing population, hazardous waste recycling has become a
critical challenge. Given the large variability of household waste, current
recycling platforms mostly rely on human operators to analyze the scene,
typically composed of many object instances piled up in bulk. Helping them by
robotizing the unitary extraction is a key challenge to speed up this tedious
process. Whereas supervised deep learning has proven very efficient for such
object-level scene understanding, e.g., generic object detection and
segmentation in everyday scenes, it however requires large sets of per-pixel
labeled images, that are hardly available for numerous application contexts,
including industrial robotics. We thus propose a step towards a practical
interactive application for generating an object-oriented robotic grasp,
requiring as inputs only one depth map of the scene and one user click on the
next object to extract. More precisely, we address in this paper the middle
issue of object seg-mentation in top views of piles of bulk objects given a
pixel location, namely seed, provided interactively by a human operator. We
propose a twofold framework for generating edge-driven instance segments.
First, we repurpose a state-of-the-art fully convolutional object contour
detector for seed-based instance segmentation by introducing the notion of
edge-mask duality with a novel patch-free and contour-oriented loss function.
Second, we train one model using only synthetic scenes, instead of manually
labeled training data. Our experimental results show that considering edge-mask
duality for training an encoder-decoder network, as we suggest, outperforms a
state-of-the-art patch-based network in the present application context.Comment: This is a pre-print of an article published in Human Friendly
Robotics, 10th International Workshop, Springer Proceedings in Advanced
Robotics, vol 7. The final authenticated version is available online at:
https://doi.org/10.1007/978-3-319-89327-3\_16, Springer Proceedings in
Advanced Robotics, Siciliano Bruno, Khatib Oussama, In press, Human Friendly
Robotics, 10th International Workshop,
Cosmological Density and Power Spectrum from Peculiar Velocities: Nonlinear Corrections and PCA
We allow for nonlinear effects in the likelihood analysis of galaxy peculiar
velocities, and obtain ~35%-lower values for the cosmological density parameter
Om and the amplitude of mass-density fluctuations. The power spectrum in the
linear regime is assumed to be a flat LCDM model (h=0.65, n=1, COBE) with only
Om as a free parameter. Since the likelihood is driven by the nonlinear regime,
we "break" the power spectrum at k_b=0.2 h/Mpc and fit a power law at k>k_b.
This allows for independent matching of the nonlinear behavior and an unbiased
fit in the linear regime. The analysis assumes Gaussian fluctuations and
errors, and a linear relation between velocity and density. Tests using proper
mock catalogs demonstrate a reduced bias and a better fit. We find for the
Mark3 and SFI data Om_m=0.32+-0.06 and 0.37+-0.09 respectively, with
sigma_8*Om^0.6 = 0.49+-0.06 and 0.63+-0.08, in agreement with constraints from
other data. The quoted 90% errors include cosmic variance. The improvement in
likelihood due to the nonlinear correction is very significant for Mark3 and
moderately so for SFI. When allowing deviations from LCDM, we find an
indication for a wiggle in the power spectrum: an excess near k=0.05 and a
deficiency at k=0.1 (cold flow). This may be related to the wiggle seen in the
power spectrum from redshift surveys and the second peak in the CMB anisotropy.
A chi^2 test applied to modes of a Principal Component Analysis (PCA) shows
that the nonlinear procedure improves the goodness of fit and reduces a spatial
gradient of concern in the linear analysis. The PCA allows addressing spatial
features of the data and fine-tuning the theoretical and error models. It shows
that the models used are appropriate for the cosmological parameter estimation
performed. We address the potential for optimal data compression using PCA.Comment: 18 pages, LaTex, uses emulateapj.sty, ApJ in press (August 10, 2001),
improvements to text and figures, updated reference
Purification, growth, and characterization of Zn(x)Cd(1-x)Se crystals
The purification of starting materials which were used in the growth of Zn(x)Cd(1-x)Se (x = 0.2) single crystals using the traveling solution method (TSM) is reported. Up to 13 cm long single crystals and as grown resistivities of 6 x 10(exp 12) ohm/cm could be achieved. Infrared and Raman spectra of Zn(0.2)Cd(0.8)Se are also presented and discussed
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