473 research outputs found
Real-time Tracking Based on Neuromrophic Vision
Real-time tracking is an important problem in computer vision in which most
methods are based on the conventional cameras. Neuromorphic vision is a concept
defined by incorporating neuromorphic vision sensors such as silicon retinas in
vision processing system. With the development of the silicon technology,
asynchronous event-based silicon retinas that mimic neuro-biological
architectures has been developed in recent years. In this work, we combine the
vision tracking algorithm of computer vision with the information encoding
mechanism of event-based sensors which is inspired from the neural rate coding
mechanism. The real-time tracking of single object with the advantage of high
speed of 100 time bins per second is successfully realized. Our method
demonstrates that the computer vision methods could be used for the
neuromorphic vision processing and we can realize fast real-time tracking using
neuromorphic vision sensors compare to the conventional camera
Sufficient Control of Complex Networks
In this paper, we propose to study on sufficient control of complex networks
which is to control a sufficiently large portion of the network, where only the
quantity of controllable nodes matters. To the best of our knowledge, this is
the first time that such a problem is investigated. We prove that the
sufficient controllability problem can be converted into a minimum cost flow
problem, for which an algorithm can be easily devised with polynomial
complexity. Further, we study the problem of minimum-cost sufficient control,
which is to drive a sufficiently large subset of the network nodes to any
predefined state with the minimum cost using a given number of controllers. It
is proved that the problem is NP-hard. We propose an ``extended
-norm-constraint-based Projected Gradient Method" (eLPGM)
algorithm which may achieve suboptimal solutions for the problems at small or
medium sizes. To tackle the large-scale problems, we propose to convert the
control problem into a graph algorithm problem, and devise an efficient
low-complexity ``Evenly Divided Control Paths" (EDCP) algorithm to tackle the
graph problem. Simulation results on both synthetic and real-life networks are
provided, demonstrating the satisfactory performance of the proposed methods
Boosting Zero-shot Learning via Contrastive Optimization of Attribute Representations
Zero-shot learning (ZSL) aims to recognize classes that do not have samples
in the training set. One representative solution is to directly learn an
embedding function associating visual features with corresponding class
semantics for recognizing new classes. Many methods extend upon this solution,
and recent ones are especially keen on extracting rich features from images,
e.g. attribute features. These attribute features are normally extracted within
each individual image; however, the common traits for features across images
yet belonging to the same attribute are not emphasized. In this paper, we
propose a new framework to boost ZSL by explicitly learning attribute
prototypes beyond images and contrastively optimizing them with attribute-level
features within images. Besides the novel architecture, two elements are
highlighted for attribute representations: a new prototype generation module is
designed to generate attribute prototypes from attribute semantics; a hard
example-based contrastive optimization scheme is introduced to reinforce
attribute-level features in the embedding space. We explore two alternative
backbones, CNN-based and transformer-based, to build our framework and conduct
experiments on three standard benchmarks, CUB, SUN, AwA2. Results on these
benchmarks demonstrate that our method improves the state of the art by a
considerable margin. Our codes will be available at
https://github.com/dyabel/CoAR-ZSL.gitComment: Accepted to TNNL
Nonlinear Spectral Mixture Modeling of Lunar Multispectral: Implications for Lateral Transport
Linear and nonlinear spectral mixture models applied to Clementine multispectral images of the Moon result in roughly similar spatial distributions of endmember abundances. However, there are important differences in the absolute values of the predicted abundances. The magnitude of these differences and the implications for understanding geological processes are investigated across a geologic contact between mare and highland in the Grimaldi Basin on the western nearside of the Moon. Vertical and lateral mass transport due to impact cratering has redistributed mare and highland materials across the contact, creating a gradient in composition. Solutions to linear and nonlinear spectral mixture models for identical spectral endmembers of mare, highland, and fresh crater materials are compared across this simple geologic contact in the Grimaldi Basin. Profiles of mare abundance across the contact are extracted and compared quantitatively. Profiles from the linear mixture models indicate that the geologic contact has an average mare abundance of 60%, and the compositional boundary is asymmetric with more mare transported onto the highland side of the contact than highland onto the mare side of the contact. In contrast the nonlinear abundance profiles indicate that the geologic contact has an average mare abundance of 50%, and the compositional boundary is remarkably symmetric. Given the expectation that materials will be intimately mixed on the surface of the Moon, and that the asymmetries implied by the linear model are not consistent with our understanding of lunar surface processes, the nonlinear spectral mixture model is preferred and should be applied whenever quantitative abundance information is required. The remarkable symmetry in the compositional gradients across this contact indicate that lateral mass transport dominates over vertical transport at this boundary
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