2,143 research outputs found
Discrete denoising of heterogenous two-dimensional data
We consider discrete denoising of two-dimensional data with characteristics
that may be varying abruptly between regions.
Using a quadtree decomposition technique and space-filling curves, we extend
the recently developed S-DUDE (Shifting Discrete Universal DEnoiser), which was
tailored to one-dimensional data, to the two-dimensional case. Our scheme
competes with a genie that has access, in addition to the noisy data, also to
the underlying noiseless data, and can employ different two-dimensional
sliding window denoisers along distinct regions obtained by a quadtree
decomposition with leaves, in a way that minimizes the overall loss. We
show that, regardless of what the underlying noiseless data may be, the
two-dimensional S-DUDE performs essentially as well as this genie, provided
that the number of distinct regions satisfies , where is the total
size of the data. The resulting algorithm complexity is still linear in both
and , as in the one-dimensional case. Our experimental results show that
the two-dimensional S-DUDE can be effective when the characteristics of the
underlying clean image vary across different regions in the data.Comment: 16 pages, submitted to IEEE Transactions on Information Theor
Multi-Sensor Event Detection using Shape Histograms
Vehicular sensor data consists of multiple time-series arising from a number
of sensors. Using such multi-sensor data we would like to detect occurrences of
specific events that vehicles encounter, e.g., corresponding to particular
maneuvers that a vehicle makes or conditions that it encounters. Events are
characterized by similar waveform patterns re-appearing within one or more
sensors. Further such patterns can be of variable duration. In this work, we
propose a method for detecting such events in time-series data using a novel
feature descriptor motivated by similar ideas in image processing. We define
the shape histogram: a constant dimension descriptor that nevertheless captures
patterns of variable duration. We demonstrate the efficacy of using shape
histograms as features to detect events in an SVM-based, multi-sensor,
supervised learning scenario, i.e., multiple time-series are used to detect an
event. We present results on real-life vehicular sensor data and show that our
technique performs better than available pattern detection implementations on
our data, and that it can also be used to combine features from multiple
sensors resulting in better accuracy than using any single sensor. Since
previous work on pattern detection in time-series has been in the single series
context, we also present results using our technique on multiple standard
time-series datasets and show that it is the most versatile in terms of how it
ranks compared to other published results
GASP : Geometric Association with Surface Patches
A fundamental challenge to sensory processing tasks in perception and
robotics is the problem of obtaining data associations across views. We present
a robust solution for ascertaining potentially dense surface patch (superpixel)
associations, requiring just range information. Our approach involves
decomposition of a view into regularized surface patches. We represent them as
sequences expressing geometry invariantly over their superpixel neighborhoods,
as uniquely consistent partial orderings. We match these representations
through an optimal sequence comparison metric based on the Damerau-Levenshtein
distance - enabling robust association with quadratic complexity (in contrast
to hitherto employed joint matching formulations which are NP-complete). The
approach is able to perform under wide baselines, heavy rotations, partial
overlaps, significant occlusions and sensor noise.
The technique does not require any priors -- motion or otherwise, and does
not make restrictive assumptions on scene structure and sensor movement. It
does not require appearance -- is hence more widely applicable than appearance
reliant methods, and invulnerable to related ambiguities such as textureless or
aliased content. We present promising qualitative and quantitative results
under diverse settings, along with comparatives with popular approaches based
on range as well as RGB-D data.Comment: International Conference on 3D Vision, 201
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