4 research outputs found
Spatial-Aware Dictionary Learning for Hyperspectral Image Classification
This paper presents a structured dictionary-based model for hyperspectral
data that incorporates both spectral and contextual characteristics of a
spectral sample, with the goal of hyperspectral image classification. The idea
is to partition the pixels of a hyperspectral image into a number of spatial
neighborhoods called contextual groups and to model each pixel with a linear
combination of a few dictionary elements learned from the data. Since pixels
inside a contextual group are often made up of the same materials, their linear
combinations are constrained to use common elements from the dictionary. To
this end, dictionary learning is carried out with a joint sparse regularizer to
induce a common sparsity pattern in the sparse coefficients of each contextual
group. The sparse coefficients are then used for classification using a linear
SVM. Experimental results on a number of real hyperspectral images confirm the
effectiveness of the proposed representation for hyperspectral image
classification. Moreover, experiments with simulated multispectral data show
that the proposed model is capable of finding representations that may
effectively be used for classification of multispectral-resolution samples.Comment: 16 pages, 9 figure
Patchwise Joint Sparse Tracking with Occlusion Detection
This paper presents a robust tracking approach to handle challenges such as
occlusion and appearance change. Here, the target is partitioned into a number
of patches. Then, the appearance of each patch is modeled using a dictionary
composed of corresponding target patches in previous frames. In each frame, the
target is found among a set of candidates generated by a particle filter, via a
likelihood measure that is shown to be proportional to the sum of
patch-reconstruction errors of each candidate. Since the target's appearance
often changes slowly in a video sequence, it is assumed that the target in the
current frame and the best candidates of a small number of previous frames,
belong to a common subspace. This is imposed using joint sparse representation
to enforce the target and previous best candidates to have a common sparsity
pattern. Moreover, an occlusion detection scheme is proposed that uses
patch-reconstruction errors and a prior probability of occlusion, extracted
from an adaptive Markov chain, to calculate the probability of occlusion per
patch. In each frame, occluded patches are excluded when updating the
dictionary. Extensive experimental results on several challenging sequences
shows that the proposed method outperforms state-of-the-art trackers
Application of Compressive Sensing Techniques in Distributed Sensor Networks: A Survey
In this survey paper, our goal is to discuss recent advances of compressive
sensing (CS) based solutions in wireless sensor networks (WSNs) including the
main ongoing/recent research efforts, challenges and research trends in this
area. In WSNs, CS based techniques are well motivated by not only the sparsity
prior observed in different forms but also by the requirement of efficient
in-network processing in terms of transmit power and communication bandwidth
even with nonsparse signals. In order to apply CS in a variety of WSN
applications efficiently, there are several factors to be considered beyond the
standard CS framework. We start the discussion with a brief introduction to the
theory of CS and then describe the motivational factors behind the potential
use of CS in WSN applications. Then, we identify three main areas along which
the standard CS framework is extended so that CS can be efficiently applied to
solve a variety of problems specific to WSNs. In particular, we emphasize on
the significance of extending the CS framework to (i). take communication
constraints into account while designing projection matrices and reconstruction
algorithms for signal reconstruction in centralized as well in decentralized
settings, (ii) solve a variety of inference problems such as detection,
classification and parameter estimation, with compressed data without signal
reconstruction and (iii) take practical communication aspects such as
measurement quantization, physical layer secrecy constraints, and imperfect
channel conditions into account. Finally, open research issues and challenges
are discussed in order to provide perspectives for future research directions