155,224 research outputs found
Intelligent multi-sensor integrations
Growth in the intelligence of space systems requires the use and integration of data from multiple sensors. Generic tools are being developed for extracting and integrating information obtained from multiple sources. The full spectrum is addressed for issues ranging from data acquisition, to characterization of sensor data, to adaptive systems for utilizing the data. In particular, there are three major aspects to the project, multisensor processing, an adaptive approach to object recognition, and distributed sensor system integration
Improving Online Source-free Domain Adaptation for Object Detection by Unsupervised Data Acquisition
Effective object detection in mobile robots is challenged by deployment in
diverse and unfamiliar environments. Online Source-Free Domain Adaptation
(O-SFDA) offers real-time model adaptation using a stream of unlabeled data
from a target domain. However, not all captured frames in mobile robotics
contain information that is beneficial for adaptation, particularly when there
is a strong domain shift. This paper introduces a novel approach to enhance
O-SFDA for adaptive object detection in mobile robots via unsupervised data
acquisition. Our methodology prioritizes the most informative unlabeled samples
for inclusion in the online training process. Empirical evaluation on a
real-world dataset reveals that our method outperforms existing
state-of-the-art O-SFDA techniques, demonstrating the viability of unsupervised
data acquisition for improving adaptive object detection in mobile robots
Adaptive Training of Video Sets for Image Recognition on Mobile Phones
We present an enhancement towards adaptive video training for PhoneGuide, a digital museum guidance system for ordinary camera–equipped mobile phones. It enables museum visitors to identify exhibits by capturing photos of them. In this article, a combined solution of object recognition and pervasive tracking is extended to a client–server–system for improving data acquisition and for supporting scale–invariant object recognition
WxBS: Wide Baseline Stereo Generalizations
We have presented a new problem -- the wide multiple baseline stereo (WxBS)
-- which considers matching of images that simultaneously differ in more than
one image acquisition factor such as viewpoint, illumination, sensor type or
where object appearance changes significantly, e.g. over time. A new dataset
with the ground truth for evaluation of matching algorithms has been introduced
and will be made public.
We have extensively tested a large set of popular and recent detectors and
descriptors and show than the combination of RootSIFT and HalfRootSIFT as
descriptors with MSER and Hessian-Affine detectors works best for many
different nuisance factors. We show that simple adaptive thresholding improves
Hessian-Affine, DoG, MSER (and possibly other) detectors and allows to use them
on infrared and low contrast images.
A novel matching algorithm for addressing the WxBS problem has been
introduced. We have shown experimentally that the WxBS-M matcher dominantes the
state-of-the-art methods both on both the new and existing datasets.Comment: Descriptor and detector evaluation expande
Multiform Adaptive Robot Skill Learning from Humans
Object manipulation is a basic element in everyday human lives. Robotic
manipulation has progressed from maneuvering single-rigid-body objects with
firm grasping to maneuvering soft objects and handling contact-rich actions.
Meanwhile, technologies such as robot learning from demonstration have enabled
humans to intuitively train robots. This paper discusses a new level of robotic
learning-based manipulation. In contrast to the single form of learning from
demonstration, we propose a multiform learning approach that integrates
additional forms of skill acquisition, including adaptive learning from
definition and evaluation. Moreover, going beyond state-of-the-art technologies
of handling purely rigid or soft objects in a pseudo-static manner, our work
allows robots to learn to handle partly rigid partly soft objects with
time-critical skills and sophisticated contact control. Such capability of
robotic manipulation offers a variety of new possibilities in human-robot
interaction.Comment: Accepted to 2017 Dynamic Systems and Control Conference (DSCC),
Tysons Corner, VA, October 11-1
How nouns and verbs differentially affect the behavior of artificial organisms
This paper presents an Artificial Life and Neural Network (ALNN) model for the evolution of syntax. The simulation methodology provides a unifying approach for the study of the evolution of language and its interaction with other behavioral and neural factors. The model uses an object manipulation task to simulate the evolution of language based on a simple verb-noun rule. The analyses of results focus on the interaction between language and other non-linguistic abilities, and on the neural control of linguistic abilities. The model shows that the beneficial effects of language on non-linguistic behavior are explained by the emergence of distinct internal representation patterns for the processing of verbs and nouns
High dynamic range imaging with a single-mode pupil remapping system : a self-calibration algorithm for redundant interferometric arrays
The correction of the influence of phase corrugation in the pupil plane is a
fundamental issue in achieving high dynamic range imaging. In this paper, we
investigate an instrumental setup which consists in applying interferometric
techniques on a single telescope, by filtering and dividing the pupil with an
array of single-mode fibers. We developed a new algorithm, which makes use of
the fact that we have a redundant interferometric array, to completely
disentangle the astronomical object from the atmospheric perturbations (phase
and scintillation). This self-calibrating algorithm can also be applied to any
- diluted or not - redundant interferometric setup. On an 8 meter telescope
observing at a wavelength of 630 nm, our simulations show that a single mode
pupil remapping system could achieve, at a few resolution elements from the
central star, a raw dynamic range up to 10^6; depending on the brightness of
the source. The self calibration algorithm proved to be very efficient,
allowing image reconstruction of faint sources (mag = 15) even though the
signal-to-noise ratio of individual spatial frequencies are of the order of
0.1. We finally note that the instrument could be more sensitive by combining
this setup with an adaptive optics system. The dynamic range would however be
limited by the noise of the small, high frequency, displacements of the
deformable mirror.Comment: 11 pages, 7 figures. Accepted for publication in MNRA
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