37,811 research outputs found
Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos
We present a semi-supervised approach that localizes multiple unknown object
instances in long videos. We start with a handful of labeled boxes and
iteratively learn and label hundreds of thousands of object instances. We
propose criteria for reliable object detection and tracking for constraining
the semi-supervised learning process and minimizing semantic drift. Our
approach does not assume exhaustive labeling of each object instance in any
single frame, or any explicit annotation of negative data. Working in such a
generic setting allow us to tackle multiple object instances in video, many of
which are static. In contrast, existing approaches either do not consider
multiple object instances per video, or rely heavily on the motion of the
objects present. The experiments demonstrate the effectiveness of our approach
by evaluating the automatically labeled data on a variety of metrics like
quality, coverage (recall), diversity, and relevance to training an object
detector.Comment: To appear in CVPR 201
Precise 3D track reconstruction algorithm for the ICARUS T600 liquid argon time projection chamber detector
Liquid Argon Time Projection Chamber (LAr TPC) detectors offer charged
particle imaging capability with remarkable spatial resolution. Precise event
reconstruction procedures are critical in order to fully exploit the potential
of this technology. In this paper we present a new, general approach of
three-dimensional reconstruction for the LAr TPC with a practical application
to track reconstruction. The efficiency of the method is evaluated on a sample
of simulated tracks. We present also the application of the method to the
analysis of real data tracks collected during the ICARUS T600 detector
operation with the CNGS neutrino beam.Comment: Submitted to Advances in High Energy Physic
The HADES Tracking System
The tracking system of the dielectron spectrometer HADES at GSI Darmstadt is
formed out of 24 low-mass, trapezoidal multi-layer drift chambers providing in
total about 30 square meter of active area. Low multiple scattering in the in
total four planes of drift chambers before and after the magnetic field is
ensured by using helium-based gas mixtures and aluminum cathode and field
wires. First in-beam performance results are contrasted with expectations from
simulations. Emphasis is placed on the energy loss information, exploring its
relevance regarding track recognition.Comment: 6 pages, 4 figures, presented at the 10th Vienna Conference on
Instrumentation, Vienna, February 2004, to be published in NIM A (special
issue
A steerable UV laser system for the calibration of liquid argon time projection chambers
A number of liquid argon time projection chambers (LAr TPC's) are being build
or are proposed for neutrino experiments on long- and short baseline beams. For
these detectors a distortion in the drift field due to geometrical or physics
reasons can affect the reconstruction of the events. Depending on the TPC
geometry and electric drift field intensity this distortion could be of the
same magnitude as the drift field itself. Recently, we presented a method to
calibrate the drift field and correct for these possible distortions. While
straight cosmic ray muon tracks could be used for calibration, multiple coulomb
scattering and momentum uncertainties allow only a limited resolution. A UV
laser instead can create straight ionization tracks in liquid argon, and allows
one to map the drift field along different paths in the TPC inner volume. Here
we present a UV laser feed-through design with a steerable UV mirror immersed
in liquid argon that can point the laser beam at many locations through the
TPC. The straight ionization paths are sensitive to drift field distortions, a
fit of these distortion to the linear optical path allows to extract the drift
field, by using these laser tracks along the whole TPC volume one can obtain a
3D drift field map. The UV laser feed-through assembly is a prototype of the
system that will be used for the MicroBooNE experiment at the Fermi National
Accelerator Laboratory (FNAL)
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A Clustering System for Dynamic Data Streams Based on Metaheuristic Optimisation
open access articleThis article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses “microclusters” and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream’s robustness to noise and resiliency to parameter changes
Finding and tracking multi-density clusters in an online dynamic data stream
The file attached to this record is the author's final peer reviewed version.Change is one of the biggest challenges in dynamic stream mining. From a data-mining perspective, adapting and tracking change is desirable in order to understand how and why change has occurred. Clustering, a form of unsupervised learning, can be used to identify the underlying patterns in a stream. Density-based clustering identifies clusters as areas of high density separated by areas of low density. This paper proposes a Multi-Density Stream Clustering (MDSC) algorithm to address these two problems; the multi-density problem and the problem of discovering and tracking changes in a dynamic stream. MDSC consists of two on-line components; discovered, labelled clusters and an outlier buffer. Incoming points are assigned to a live cluster or passed to the outlier buffer. New clusters are discovered in the buffer using an ant-inspired swarm intelligence approach. The newly discovered cluster is uniquely labelled and added to the set of live clusters. Processed data is subject to an ageing function and will disappear when it is no longer relevant. MDSC is shown to perform favourably to state-of-the-art peer stream-clustering algorithms on a range of real and synthetic data-streams. Experimental results suggest that MDSC can discover qualitatively useful patterns while being scalable and robust to noise
Combining similarity in time and space for training set formation under concept drift
Concept drift is a challenge in supervised learning for sequential data. It describes a phenomenon when the data distributions change over time. In such a case accuracy of a classifier benefits from the selective sampling for training. We develop a method for training set selection, particularly relevant when the expected drift is gradual. Training set selection at each time step is based on the distance to the target instance. The distance function combines similarity in space and in time. The method determines an optimal training set size online at every time step using cross validation. It is a wrapper approach, it can be used plugging in different base classifiers. The proposed method shows the best accuracy in the peer group on the real and artificial drifting data. The method complexity is reasonable for the field applications
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