6,508 research outputs found
Robust Recovery of Subspace Structures by Low-Rank Representation
In this work we address the subspace recovery problem. Given a set of data
samples (vectors) approximately drawn from a union of multiple subspaces, our
goal is to segment the samples into their respective subspaces and correct the
possible errors as well. To this end, we propose a novel method termed Low-Rank
Representation (LRR), which seeks the lowest-rank representation among all the
candidates that can represent the data samples as linear combinations of the
bases in a given dictionary. It is shown that LRR well solves the subspace
recovery problem: when the data is clean, we prove that LRR exactly captures
the true subspace structures; for the data contaminated by outliers, we prove
that under certain conditions LRR can exactly recover the row space of the
original data and detect the outlier as well; for the data corrupted by
arbitrary errors, LRR can also approximately recover the row space with
theoretical guarantees. Since the subspace membership is provably determined by
the row space, these further imply that LRR can perform robust subspace
segmentation and error correction, in an efficient way.Comment: IEEE Trans. Pattern Analysis and Machine Intelligenc
Perception Imitation: Towards Synthesis-free Simulator for Autonomous Vehicles
We propose a perception imitation method to simulate results of a certain
perception model, and discuss a new heuristic route of autonomous driving
simulator without data synthesis. The motivation is that original sensor data
is not always necessary for tasks such as planning and control when semantic
perception results are ready, so that simulating perception directly is more
economic and efficient. In this work, a series of evaluation methods such as
matching metric and performance of downstream task are exploited to examine the
simulation quality. Experiments show that our method is effective to model the
behavior of learning-based perception model, and can be further applied in the
proposed simulation route smoothly
DEFormer: DCT-driven Enhancement Transformer for Low-light Image and Dark Vision
The goal of low-light image enhancement is to restore the color and details
of the image and is of great significance for high-level visual tasks in
autonomous driving. However, it is difficult to restore the lost details in the
dark area by relying only on the RGB domain. In this paper we introduce
frequency as a new clue into the network and propose a novel DCT-driven
enhancement transformer (DEFormer). First, we propose a learnable frequency
branch (LFB) for frequency enhancement contains DCT processing and
curvature-based frequency enhancement (CFE). CFE calculates the curvature of
each channel to represent the detail richness of different frequency bands,
then we divides the frequency features, which focuses on frequency bands with
richer textures. In addition, we propose a cross domain fusion (CDF) for
reducing the differences between the RGB domain and the frequency domain. We
also adopt DEFormer as a preprocessing in dark detection, DEFormer effectively
improves the performance of the detector, bringing 2.1% and 3.4% improvement in
ExDark and DARK FACE datasets on mAP respectively.Comment: submit to ICRA202
Impact of mechanical stretch on the cell behaviors of bone and surrounding tissues
Mechanical loading is recognized to play an important role in regulating the behaviors of cells in bone and surrounding tissues in vivo. Many in vitro studies have been conducted to determine the effects of mechanical loading on individual cell types of the tissues. In this review, we focus specifically on the use of the Flexercell system as a tool for studying cellular responses to mechanical stretch. We assess the literature describing the impact of mechanical stretch on different cell types from bone, muscle, tendon, ligament, and cartilage, describing individual cell phenotype responses. In addition, we review evidence regarding the mechanotransduction pathways that are activated to potentiate these phenotype responses in different cell populations
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