11,588 research outputs found
Coplanar Repeats by Energy Minimization
This paper proposes an automated method to detect, group and rectify
arbitrarily-arranged coplanar repeated elements via energy minimization. The
proposed energy functional combines several features that model how planes with
coplanar repeats are projected into images and captures global interactions
between different coplanar repeat groups and scene planes. An inference
framework based on a recent variant of -expansion is described and fast
convergence is demonstrated. We compare the proposed method to two widely-used
geometric multi-model fitting methods using a new dataset of annotated images
containing multiple scene planes with coplanar repeats in varied arrangements.
The evaluation shows a significant improvement in the accuracy of
rectifications computed from coplanar repeats detected with the proposed method
versus those detected with the baseline methods.Comment: 14 pages with supplemental materials attache
Vehicle detection and tracking using homography-based plane rectification and particle filtering
This paper presents a full system for vehicle detection and tracking in non-stationary settings based on computer vision. The method proposed for vehicle detection exploits the geometrical relations between the elements in the scene so that moving objects (i.e., vehicles) can be detected by analyzing motion parallax. Namely, the homography of the road plane between successive images is computed. Most remarkably, a novel probabilistic framework based on Kalman filtering is presented for reliable and accurate homography estimation. The estimated homography is used for image alignment, which in turn allows to detect the moving vehicles in the image. Tracking of vehicles is performed on the basis of a multidimensional particle filter, which also manages the exit and entries of objects. The filter involves a mixture likelihood model that allows a better adaptation of the particles to the observed measurements. The system is specially designed for highway environments, where it has been proven to yield excellent results
Rapid mapping of digital integrated circuit logic gates via multi-spectral backside imaging
Modern semiconductor integrated circuits are increasingly fabricated at
untrusted third party foundries. There now exist myriad security threats of
malicious tampering at the hardware level and hence a clear and pressing need
for new tools that enable rapid, robust and low-cost validation of circuit
layouts. Optical backside imaging offers an attractive platform, but its
limited resolution and throughput cannot cope with the nanoscale sizes of
modern circuitry and the need to image over a large area. We propose and
demonstrate a multi-spectral imaging approach to overcome these obstacles by
identifying key circuit elements on the basis of their spectral response. This
obviates the need to directly image the nanoscale components that define them,
thereby relaxing resolution and spatial sampling requirements by 1 and 2 - 4
orders of magnitude respectively. Our results directly address critical
security needs in the integrated circuit supply chain and highlight the
potential of spectroscopic techniques to address fundamental resolution
obstacles caused by the need to image ever shrinking feature sizes in
semiconductor integrated circuits
Advancing image quantification methods and tools for analysis of nanoparticle electrokinetics
Image processing methods and techniques for high-throughput quantification of dielectrophoretic (DEP) collections onto planar castellated electrode arrays are developed and evaluated. Fluorescence-based dielectrophoretic spectroscopy is an important tool for laboratory investigations of AC electrokinetic properties of nanoparticles. This paper details new, first principle, theoretical and experimental developments of geometric feature recognition techniques that enable quantification of positive dielectrophoretic (pDEP) nanoparticle collections onto castellated arrays. As an alternative to the geometric-based method, novel statistical methods that do not require any information about array features, are also developed using the quantile and standard deviation functions. Data from pDEP collection and release experiments using 200 nm diameter latex nanospheres demonstrates that pDEP quantification using the statistic-based methods yields quantitatively similar results to the geometric-based method. The development of geometric- and statistic-based quantification methods enables high-throughput, supervisor-free image processing tools critical for dielectrophoretic spectroscopy and automated DEP technology development
Automated Visual Fin Identification of Individual Great White Sharks
This paper discusses the automated visual identification of individual great
white sharks from dorsal fin imagery. We propose a computer vision photo ID
system and report recognition results over a database of thousands of
unconstrained fin images. To the best of our knowledge this line of work
establishes the first fully automated contour-based visual ID system in the
field of animal biometrics. The approach put forward appreciates shark fins as
textureless, flexible and partially occluded objects with an individually
characteristic shape. In order to recover animal identities from an image we
first introduce an open contour stroke model, which extends multi-scale region
segmentation to achieve robust fin detection. Secondly, we show that
combinatorial, scale-space selective fingerprinting can successfully encode fin
individuality. We then measure the species-specific distribution of visual
individuality along the fin contour via an embedding into a global `fin space'.
Exploiting this domain, we finally propose a non-linear model for individual
animal recognition and combine all approaches into a fine-grained
multi-instance framework. We provide a system evaluation, compare results to
prior work, and report performance and properties in detail.Comment: 17 pages, 16 figures. To be published in IJCV. Article replaced to
update first author contact details and to correct a Figure reference on page
Plane-extraction from depth-data using a Gaussian mixture regression model
We propose a novel algorithm for unsupervised extraction of piecewise planar
models from depth-data. Among other applications, such models are a good way of
enabling autonomous agents (robots, cars, drones, etc.) to effectively perceive
their surroundings and to navigate in three dimensions. We propose to do this
by fitting the data with a piecewise-linear Gaussian mixture regression model
whose components are skewed over planes, making them flat in appearance rather
than being ellipsoidal, by embedding an outlier-trimming process that is
formally incorporated into the proposed expectation-maximization algorithm, and
by selectively fusing contiguous, coplanar components. Part of our motivation
is an attempt to estimate more accurate plane-extraction by allowing each model
component to make use of all available data through probabilistic clustering.
The algorithm is thoroughly evaluated against a standard benchmark and is shown
to rank among the best of the existing state-of-the-art methods.Comment: 11 pages, 2 figures, 1 tabl
Data-Driven Grasp Synthesis - A Survey
We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
Source extraction and photometry for the far-infrared and sub-millimeter continuum in the presence of complex backgrounds
(Abridged) We present a new method for detecting and measuring compact
sources in conditions of intense, and highly variable, fore/background. While
all most commonly used packages carry out the source detection over the signal
image, our proposed method builds from the measured image a "curvature" image
by double-differentiation in four different directions. In this way point-like
as well as resolved, yet relatively compact, objects are easily revealed while
the slower varying fore/background is greatly diminished. Candidate sources are
then identified by looking for pixels where the curvature exceeds, in absolute
terms, a given threshold; the methodology easily allows us to pinpoint
breakpoints in the source brightness profile and then derive reliable guesses
for the sources extent. Identified peaks are fit with 2D elliptical Gaussians
plus an underlying planar inclined plateau, with mild constraints on size and
orientation. Mutually contaminating sources are fit with multiple Gaussians
simultaneously using flexible constraints. We ran our method on simulated
large-scale fields with 1000 sources of different peak flux overlaid on a
realistic realization of diffuse background. We find detection rates in excess
of 90% for sources with peak fluxes above the 3-sigma signal noise limit; for
about 80% of the sources the recovered peak fluxes are within 30% of their
input values.Comment: Accepted on A&
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