96,923 research outputs found
DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers
In this paper, a new method for generating object and action proposals in
images and videos is proposed. It builds on activations of different
convolutional layers of a pretrained CNN, combining the localization accuracy
of the early layers with the high informative-ness (and hence recall) of the
later layers. To this end, we build an inverse cascade that, going backward
from the later to the earlier convolutional layers of the CNN, selects the most
promising locations and refines them in a coarse-to-fine manner. The method is
efficient, because i) it re-uses the same features extracted for detection, ii)
it aggregates features using integral images, and iii) it avoids a dense
evaluation of the proposals thanks to the use of the inverse coarse-to-fine
cascade. The method is also accurate. We show that our DeepProposals outperform
most of the previously proposed object proposal and action proposal approaches
and, when plugged into a CNN-based object detector, produce state-of-the-art
detection performance.Comment: 15 page
Expanded Parts Model for Semantic Description of Humans in Still Images
We introduce an Expanded Parts Model (EPM) for recognizing human attributes
(e.g. young, short hair, wearing suit) and actions (e.g. running, jumping) in
still images. An EPM is a collection of part templates which are learnt
discriminatively to explain specific scale-space regions in the images (in
human centric coordinates). This is in contrast to current models which consist
of a relatively few (i.e. a mixture of) 'average' templates. EPM uses only a
subset of the parts to score an image and scores the image sparsely in space,
i.e. it ignores redundant and random background in an image. To learn our
model, we propose an algorithm which automatically mines parts and learns
corresponding discriminative templates together with their respective locations
from a large number of candidate parts. We validate our method on three recent
challenging datasets of human attributes and actions. We obtain convincing
qualitative and state-of-the-art quantitative results on the three datasets.Comment: Accepted for publication in IEEE Transactions on Pattern Analysis and
Machine Intelligence (TPAMI
Visual object tracking performance measures revisited
The problem of visual tracking evaluation is sporting a large variety of
performance measures, and largely suffers from lack of consensus about which
measures should be used in experiments. This makes the cross-paper tracker
comparison difficult. Furthermore, as some measures may be less effective than
others, the tracking results may be skewed or biased towards particular
tracking aspects. In this paper we revisit the popular performance measures and
tracker performance visualizations and analyze them theoretically and
experimentally. We show that several measures are equivalent from the point of
information they provide for tracker comparison and, crucially, that some are
more brittle than the others. Based on our analysis we narrow down the set of
potential measures to only two complementary ones, describing accuracy and
robustness, thus pushing towards homogenization of the tracker evaluation
methodology. These two measures can be intuitively interpreted and visualized
and have been employed by the recent Visual Object Tracking (VOT) challenges as
the foundation for the evaluation methodology
py4DSTEM: a software package for multimodal analysis of four-dimensional scanning transmission electron microscopy datasets
Scanning transmission electron microscopy (STEM) allows for imaging,
diffraction, and spectroscopy of materials on length scales ranging from
microns to atoms. By using a high-speed, direct electron detector, it is now
possible to record a full 2D image of the diffracted electron beam at each
probe position, typically a 2D grid of probe positions. These 4D-STEM datasets
are rich in information, including signatures of the local structure,
orientation, deformation, electromagnetic fields and other sample-dependent
properties. However, extracting this information requires complex analysis
pipelines, from data wrangling to calibration to analysis to visualization, all
while maintaining robustness against imaging distortions and artifacts. In this
paper, we present py4DSTEM, an analysis toolkit for measuring material
properties from 4D-STEM datasets, written in the Python language and released
with an open source license. We describe the algorithmic steps for dataset
calibration and various 4D-STEM property measurements in detail, and present
results from several experimental datasets. We have also implemented a simple
and universal file format appropriate for electron microscopy data in py4DSTEM,
which uses the open source HDF5 standard. We hope this tool will benefit the
research community, helps to move the developing standards for data and
computational methods in electron microscopy, and invite the community to
contribute to this ongoing, fully open-source project
Fast Graph-Based Object Segmentation for RGB-D Images
Object segmentation is an important capability for robotic systems, in
particular for grasping. We present a graph- based approach for the
segmentation of simple objects from RGB-D images. We are interested in
segmenting objects with large variety in appearance, from lack of texture to
strong textures, for the task of robotic grasping. The algorithm does not rely
on image features or machine learning. We propose a modified Canny edge
detector for extracting robust edges by using depth information and two simple
cost functions for combining color and depth cues. The cost functions are used
to build an undirected graph, which is partitioned using the concept of
internal and external differences between graph regions. The partitioning is
fast with O(NlogN) complexity. We also discuss ways to deal with missing depth
information. We test the approach on different publicly available RGB-D object
datasets, such as the Rutgers APC RGB-D dataset and the RGB-D Object Dataset,
and compare the results with other existing methods
An INTEGRAL overview of High Mass X-ray Binaries: classes or transitions?
We analyzed in a systematic way the public INTEGRAL observations spanning
from December 2002 to September 2016, to investigate the hard X-ray properties
of about 60 High Mass X-ray Binaries (HMXBs). We considered both persistent and
transient sources, hosting either a Be star (Be/XRBs) or a blue supergiant
companion (SgHMXBs, including Supergiant Fast X-ray Transients, SFXTs), a
neutron star or a black hole. INTEGRAL X-ray light curves (18-50 keV), sampled
at a bin time of about 2 ks, were extracted for all HMXBs to derive the
cumulative distribution of their hard X-ray luminosity, their duty cycle, the
range of variability of their hard X-ray luminosity. This allowed us to obtain
an overall and quantitative characterization of the long-term hard X-ray
activity of the HMXBs in our sample. Putting the phenomenology observed with
INTEGRAL into context with other known source properties (e.g. orbital
parameters, pulsar spin periods) together with observational constraints coming
from softer X-rays (1-10 keV), enabled the investigation of the way the
different HMXB sub-classes behave (and sometimes overlap). For given source
properties, the different sub-classes of massive binaries seem to cluster in a
suggestive way. However, for what concerns supergiant systems (SgHMXBs versus
SFXTs), several sources with intermediate properties exist, suggesting a smooth
transition between the two sub-classes.Comment: 27 pages, 17 figures, 3 tables; accepted for publication in Monthly
Notices of the Royal Astronomical Society (accepted 2018 August 30. Received
2018 August 22; in original form 2018 May 16
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Models of incremental concept formation
Given a set of observations, humans acquire concepts that organize those observations and use them in classifying future experiences. This type of concept formation can occur in the absence of a tutor and it can take place despite irrelevant and incomplete information. A reasonable model of such human concept learning should be both incremental and capable of handling this type of complex experiences that people encounter in the real world. In this paper, we review three previous models of incremental concept formation and then present CLASSIT, a model that extends these earlier systems. All of the models integrate the process of recognition and learning, and all can be viewed as carrying out search through the space of possible concept hierarchies. In an attempt to show that CLASSIT is a robust concept formation system, we also present some empirical studies of its behavior under a variety of conditions
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