32,336 research outputs found
ASPECT: A spectra clustering tool for exploration of large spectral surveys
We present the novel, semi-automated clustering tool ASPECT for analysing
voluminous archives of spectra. The heart of the program is a neural network in
form of Kohonen's self-organizing map. The resulting map is designed as an icon
map suitable for the inspection by eye. The visual analysis is supported by the
option to blend in individual object properties such as redshift, apparent
magnitude, or signal-to-noise ratio. In addition, the package provides several
tools for the selection of special spectral types, e.g. local difference maps
which reflect the deviations of all spectra from one given input spectrum (real
or artificial). ASPECT is able to produce a two-dimensional topological map of
a huge number of spectra. The software package enables the user to browse and
navigate through a huge data pool and helps him to gain an insight into
underlying relationships between the spectra and other physical properties and
to get the big picture of the entire data set. We demonstrate the capability of
ASPECT by clustering the entire data pool of 0.6 million spectra from the Data
Release 4 of the Sloan Digital Sky Survey (SDSS). To illustrate the results
regarding quality and completeness we track objects from existing catalogues of
quasars and carbon stars, respectively, and connect the SDSS spectra with
morphological information from the GalaxyZoo project.Comment: 15 pages, 14 figures; accepted for publication in Astronomy and
Astrophysic
Boosting Deep Open World Recognition by Clustering
While convolutional neural networks have brought significant advances in
robot vision, their ability is often limited to closed world scenarios, where
the number of semantic concepts to be recognized is determined by the available
training set. Since it is practically impossible to capture all possible
semantic concepts present in the real world in a single training set, we need
to break the closed world assumption, equipping our robot with the capability
to act in an open world. To provide such ability, a robot vision system should
be able to (i) identify whether an instance does not belong to the set of known
categories (i.e. open set recognition), and (ii) extend its knowledge to learn
new classes over time (i.e. incremental learning). In this work, we show how we
can boost the performance of deep open world recognition algorithms by means of
a new loss formulation enforcing a global to local clustering of class-specific
features. In particular, a first loss term, i.e. global clustering, forces the
network to map samples closer to the class centroid they belong to while the
second one, local clustering, shapes the representation space in such a way
that samples of the same class get closer in the representation space while
pushing away neighbours belonging to other classes. Moreover, we propose a
strategy to learn class-specific rejection thresholds, instead of heuristically
estimating a single global threshold, as in previous works. Experiments on
RGB-D Object and Core50 datasets show the effectiveness of our approach.Comment: IROS/RAL 202
FPGA-based Anomalous trajectory detection using SOFM
A system for automatically classifying the trajectory of a moving object in a scene as usual or suspicious is presented. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a reconfigurable hardware architecture (Field Programmable Gate Array) to cluster trajectories acquired over a period, in order to detect novel ones. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 15\% classification error, showing the robustness of our approach over others in literature and the speed-up over the use of conventional microprocessor as compared to the use of an off-the-shelf FPGA prototyping board
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