3,713 research outputs found
Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization
Camera relocalization plays a vital role in many robotics and computer vision
tasks, such as global localization, recovery from tracking failure and loop
closure detection. Recent random forests based methods exploit randomly sampled
pixel comparison features to predict 3D world locations for 2D image locations
to guide the camera pose optimization. However, these image features are only
sampled randomly in the images, without considering the spatial structures or
geometric information, leading to large errors or failure cases with the
existence of poorly textured areas or in motion blur. Line segment features are
more robust in these environments. In this work, we propose to jointly exploit
points and lines within the framework of uncertainty driven regression forests.
The proposed approach is thoroughly evaluated on three publicly available
datasets against several strong state-of-the-art baselines in terms of several
different error metrics. Experimental results prove the efficacy of our method,
showing superior or on-par state-of-the-art performance.Comment: published as a conference paper at 2018 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS
Stratified Transfer Learning for Cross-domain Activity Recognition
In activity recognition, it is often expensive and time-consuming to acquire
sufficient activity labels. To solve this problem, transfer learning leverages
the labeled samples from the source domain to annotate the target domain which
has few or none labels. Existing approaches typically consider learning a
global domain shift while ignoring the intra-affinity between classes, which
will hinder the performance of the algorithms. In this paper, we propose a
novel and general cross-domain learning framework that can exploit the
intra-affinity of classes to perform intra-class knowledge transfer. The
proposed framework, referred to as Stratified Transfer Learning (STL), can
dramatically improve the classification accuracy for cross-domain activity
recognition. Specifically, STL first obtains pseudo labels for the target
domain via majority voting technique. Then, it performs intra-class knowledge
transfer iteratively to transform both domains into the same subspaces.
Finally, the labels of target domain are obtained via the second annotation. To
evaluate the performance of STL, we conduct comprehensive experiments on three
large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI
DSADS), which demonstrates that STL significantly outperforms other
state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%).
Furthermore, we extensively investigate the performance of STL across different
degrees of similarities and activity levels between domains. And we also
discuss the potential of STL in other pervasive computing applications to
provide empirical experience for future research.Comment: 10 pages; accepted by IEEE PerCom 2018; full paper. (camera-ready
version
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin
頑健な画像間対応付け及び視覚的位置推定のための深層学習手法
Tohoku University博士(情報科学)thesi
Learning Feature Matching via Matchable Keypoint-Assisted Graph Neural Network
Accurately matching local features between a pair of images is a challenging
computer vision task. Previous studies typically use attention based graph
neural networks (GNNs) with fully-connected graphs over keypoints within/across
images for visual and geometric information reasoning. However, in the context
of feature matching, considerable keypoints are non-repeatable due to occlusion
and failure of the detector, and thus irrelevant for message passing. The
connectivity with non-repeatable keypoints not only introduces redundancy,
resulting in limited efficiency, but also interferes with the representation
aggregation process, leading to limited accuracy. Targeting towards high
accuracy and efficiency, we propose MaKeGNN, a sparse attention-based GNN
architecture which bypasses non-repeatable keypoints and leverages matchable
ones to guide compact and meaningful message passing. More specifically, our
Bilateral Context-Aware Sampling Module first dynamically samples two small
sets of well-distributed keypoints with high matchability scores from the image
pair. Then, our Matchable Keypoint-Assisted Context Aggregation Module regards
sampled informative keypoints as message bottlenecks and thus constrains each
keypoint only to retrieve favorable contextual information from intra- and
inter- matchable keypoints, evading the interference of irrelevant and
redundant connectivity with non-repeatable ones. Furthermore, considering the
potential noise in initial keypoints and sampled matchable ones, the MKACA
module adopts a matchability-guided attentional aggregation operation for purer
data-dependent context propagation. By these means, we achieve the
state-of-the-art performance on relative camera estimation, fundamental matrix
estimation, and visual localization, while significantly reducing computational
and memory complexity compared to typical attentional GNNs
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