309 research outputs found
Open-Set Object Recognition Using Mechanical Properties During Interaction
while most of the tactile robots are operated in close-set conditions, it is
challenging for them to operate in open-set conditions where test objects are
beyond the robots' knowledge. We proposed an open-set recognition framework
using mechanical properties to recongise known objects and incrementally label
novel objects. The main contribution is a clustering algorithm that exploits
knowledge of known objects to estimate cluster centre and sizes, unlike a
typical algorithm that randomly selects them. The framework is validated with
the mechanical properties estimated from a real object during interaction. The
results show that the framework could recognise objects better than alternative
methods contributed by the novelty detector. Importantly, our clustering
algorithm yields better clustering performance than other methods. Furthermore,
the hyperparameters studies show that cluster size is important to clustering
results and needed to be tuned properly
Convolution theorems associated with quaternion linear canonical transform and applications
Novel types of convolution operators for quaternion linear canonical
transform (QLCT) are proposed. Type one and two are defined in the spatial and
QLCT spectral domains, respectively. They are distinct in the quaternion space
and are consistent once in complex or real space. Various types of convolution
formulas are discussed. Consequently, the QLCT of the convolution of two
quaternionic functions can be implemented by the product of their QLCTs, or the
summation of the products of their QLCTs. As applications, correlation
operators and theorems of the QLCT are derived. The proposed convolution
formulas are used to solve Fredholm integral equations with special kernels.
Some systems of second-order partial differential equations, which can be
transformed into the second-order quaternion partial differential equations,
can be solved by the convolution formulas as well. As a final point, we
demonstrate that the convolution theorem facilitates the design of
multiplicative filters
The Color Clifford Hardy Signal: Application to Color Edge Detection and Optical Flow
This paper introduces the idea of the color Clifford Hardy signal, which can
be used to process color images. As a complex analytic function's
high-dimensional analogue, the color Clifford Hardy signal inherits many
desirable qualities of analyticity. A crucial tool for getting the color and
structural data is the local feature representation of a color image in the
color Clifford Hardy signal. By looking at the extended Cauchy-Riemann
equations in the high-dimensional space, it is possible to see the connection
between the different parts of the color Clifford Hardy signal. Based on the
distinctive and important local amplitude and local phase generated by the
color Clifford Hardy signal, we propose five methods to identify the edges of
color images with relation to a certain color. To prove the superiority of the
offered methodologies, numerous comparative studies employing image quality
assessment criteria are used. Specifically by using the multi-scale structure
of the color Clifford Hardy signal, the proposed approaches are resistant to a
variety of noises. In addition, a color optical flow detection method with
anti-noise ability is provided as an example of application.Comment: 13 page
SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Views
We introduce SparseNeuS, a novel neural rendering based method for the task
of surface reconstruction from multi-view images. This task becomes more
difficult when only sparse images are provided as input, a scenario where
existing neural reconstruction approaches usually produce incomplete or
distorted results. Moreover, their inability of generalizing to unseen new
scenes impedes their application in practice. Contrarily, SparseNeuS can
generalize to new scenes and work well with sparse images (as few as 2 or 3).
SparseNeuS adopts signed distance function (SDF) as the surface representation,
and learns generalizable priors from image features by introducing geometry
encoding volumes for generic surface prediction. Moreover, several strategies
are introduced to effectively leverage sparse views for high-quality
reconstruction, including 1) a multi-level geometry reasoning framework to
recover the surfaces in a coarse-to-fine manner; 2) a multi-scale color
blending scheme for more reliable color prediction; 3) a consistency-aware
fine-tuning scheme to control the inconsistent regions caused by occlusion and
noise. Extensive experiments demonstrate that our approach not only outperforms
the state-of-the-art methods, but also exhibits good efficiency,
generalizability, and flexibility.Comment: Project page: https://www.xxlong.site/SparseNeuS
Advances in nanomaterial-based targeted drug delivery systems
Nanomaterial-based drug delivery systems (NBDDS) are widely used to improve the safety and therapeutic efficacy of encapsulated drugs due to their unique physicochemical and biological properties. By combining therapeutic drugs with nanoparticles using rational targeting pathways, nano-targeted delivery systems were created to overcome the main drawbacks of conventional drug treatment, including insufficient stability and solubility, lack of transmembrane transport, short circulation time, and undesirable toxic effects. Herein, we reviewed the recent developments in different targeting design strategies and therapeutic approaches employing various nanomaterial-based systems. We also discussed the challenges and perspectives of smart systems in precisely targeting different intravascular and extravascular diseases
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