24,730 research outputs found
Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking
Object-to-camera motion produces a variety of apparent motion patterns that
significantly affect performance of short-term visual trackers. Despite being
crucial for designing robust trackers, their influence is poorly explored in
standard benchmarks due to weakly defined, biased and overlapping attribute
annotations. In this paper we propose to go beyond pre-recorded benchmarks with
post-hoc annotations by presenting an approach that utilizes omnidirectional
videos to generate realistic, consistently annotated, short-term tracking
scenarios with exactly parameterized motion patterns. We have created an
evaluation system, constructed a fully annotated dataset of omnidirectional
videos and the generators for typical motion patterns. We provide an in-depth
analysis of major tracking paradigms which is complementary to the standard
benchmarks and confirms the expressiveness of our evaluation approach
Data Reduction Pipeline for the CHARIS Integral-Field Spectrograph I: Detector Readout Calibration and Data Cube Extraction
We present the data reduction pipeline for CHARIS, a high-contrast
integral-field spectrograph for the Subaru Telescope. The pipeline constructs a
ramp from the raw reads using the measured nonlinear pixel response, and
reconstructs the data cube using one of three extraction algorithms: aperture
photometry, optimal extraction, or fitting. We measure and apply both
a detector flatfield and a lenslet flatfield and reconstruct the wavelength-
and position-dependent lenslet point-spread function (PSF) from images taken
with a tunable laser. We use these measured PSFs to implement a -based
extraction of the data cube, with typical residuals of ~5% due to imperfect
models of the undersampled lenslet PSFs. The full two-dimensional residual of
the extraction allows us to model and remove correlated read noise,
dramatically improving CHARIS' performance. The extraction produces a
data cube that has been deconvolved with the line-spread function, and never
performs any interpolations of either the data or the individual lenslet
spectra. The extracted data cube also includes uncertainties for each spatial
and spectral measurement. CHARIS' software is parallelized, written in Python
and Cython, and freely available on github with a separate documentation page.
Astrometric and spectrophotometric calibrations of the data cubes and PSF
subtraction will be treated in a forthcoming paper.Comment: 18 pages, 15 figures, 3 tables, replaced with JATIS accepted version
(emulateapj formatted here). Software at
https://github.com/PrincetonUniversity/charis-dep and documentation at
http://princetonuniversity.github.io/charis-de
Multi-View Region Adaptive Multi-temporal DMM and RGB Action Recognition
Human action recognition remains an important yet challenging task. This work
proposes a novel action recognition system. It uses a novel Multiple View
Region Adaptive Multi-resolution in time Depth Motion Map (MV-RAMDMM)
formulation combined with appearance information. Multiple stream 3D
Convolutional Neural Networks (CNNs) are trained on the different views and
time resolutions of the region adaptive Depth Motion Maps. Multiple views are
synthesised to enhance the view invariance. The region adaptive weights, based
on localised motion, accentuate and differentiate parts of actions possessing
faster motion. Dedicated 3D CNN streams for multi-time resolution appearance
information (RGB) are also included. These help to identify and differentiate
between small object interactions. A pre-trained 3D-CNN is used here with
fine-tuning for each stream along with multiple class Support Vector Machines
(SVM)s. Average score fusion is used on the output. The developed approach is
capable of recognising both human action and human-object interaction. Three
public domain datasets including: MSR 3D Action,Northwestern UCLA multi-view
actions and MSR 3D daily activity are used to evaluate the proposed solution.
The experimental results demonstrate the robustness of this approach compared
with state-of-the-art algorithms.Comment: 14 pages, 6 figures, 13 tables. Submitte
Pointwise adaptive estimation for robust and quantile regression
A nonparametric procedure for robust regression estimation and for quantile
regression is proposed which is completely data-driven and adapts locally to
the regularity of the regression function. This is achieved by considering in
each point M-estimators over different local neighbourhoods and by a local
model selection procedure based on sequential testing. Non-asymptotic risk
bounds are obtained, which yield rate-optimality for large sample asymptotics
under weak conditions. Simulations for different univariate median regression
models show good finite sample properties, also in comparison to traditional
methods. The approach is extended to image denoising and applied to CT scans in
cancer research
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