High Resolution Image Reconstruction with Smart Camera Network

Abstract

In this work, a framework of feature distribution scheme is proposed for object matching. In this approach, information is distributed in such a way that each individual node maintains only a small amount of information about the objects seen by the network. Nevertheless, this amount is sufficient to efficiently route queries through the network without any degradation of the matching performance. Digital image processing approaches have been investigated to reconstruct a high resolution image from aliased low resolution images. The accurate registrations between low resolution images are very important to the reconstruction of a high resolution image. The proposed feature distribution scheme results in far lower network traffic load. To achieve the maximum performance as with the full distribution of feature vectors, a set of requirements regarding abstraction, storage space, similarity metric and convergence has been proposed to implement this work in C++ andQT.

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ISSN (ONLINE) : 2395-695X
ISSN (PRINT) : 2395-695X
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International Journal of Advanced Research in Biology, Ecology, Science and Technology (IJARBEST)
Vol. 1, Issue 4, July 2015
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All Rights Reserved © 2015 IJARBEST
High Resolution Image Reconstruction with Smart
Camera Network
R.Nikitha1, C.K.Sankavi2, H.Mehnaz3, N.Rajalakshmi4, Christo Ananth5
U.G.Scholars, Department of ECE, Francis Xavier Engineering College, Tirunelveli1,2,3,4
Associate Professor, Department of ECE, Francis Xavier Engineering College, Tirunelveli 5
Abstract— In this work, a framework of
feature distribution scheme is proposed for object
matching. In this approach, information is distributed in
such a way that each individual node maintains only a
small amount of information about the objects seen by
the network. Nevertheless, this amount is sufficient to
efficiently route queries through the network without
any degradation of the matching performance. Digital
image processing approaches have been investigated to
reconstruct a high resolution image from aliased low
resolution images. The accurate registrations between
low resolution images are very important to the
reconstruction of a high resolution image. The proposed
feature distribution scheme results in far lower network
traffic load. To achieve the maximum performance as
with the full distribution of feature vectors, a set of
requirements regarding abstraction, storage space,
similarity metric and convergence has been proposed to
implement this work in C++ andQT.
Index Terms—Computer vision, Object Reconstruction,
Visual Sensor Networks
I. INTRODUCTION
A Visual Sensor Network is a network of
spatially distributed smart camera devices capable of
processing and fusing images of a scene from a variety of
viewpoints into some form more useful than the individual
images. A visual sensor network may be a type of wireless
sensor network, and much of the theory and application of
the latter applies to the former. The network generally
consists of the cameras themselves, which have some local
image processing, communication and storage capabilities,
and possibly one or more central computers, where image
data from multiple cameras is further processed and fused.
Local processing of the image data reduces the total
amount of data that needs to be communicated through the
network. Local processing can involve simple image
processing algorithms (such as background substraction for
motion/object detection, and edge detection) as well as
more complex image/vision processing algorithms (such as
feature extraction, object classification, scene reasoning).
Thus, depending on the application, the camera nodes may
provide different levels of intelligence, as determined by the
complexity of the processing algorithms they use. The
cameras can collaborate by exchanging the detected object
features, enabling further processing to collectively reason
about the object's appearance or behavior. At this point the
visual sensor network becomes a user-independent,
intelligent system of distributed cameras that provides only
relevant information about the monitored phenomena.
Therefore, the increased complexity of vision processing
algorithms results in highly intelligent camera systems that
are oftentimes called smart camera networks.
The issue of ensuring and preserving coverage of
an area with controlled redundancy using WSNs has been
widely investigated, and efficient algorithms have been
proposed.
The main goals of coverage optimization
algorithms are to preserve coverage in case of sensor failure
and to save energy by putting redundant sensor nodes to
sleep. Choosing which nodes to put in sleeping or active
mode should be done carefully to prolong the network
lifetime, preserve coverage and connectivity, and perform
the task at hand. However, when camera sensors are
involved, three-dimensional coverage of space is required,
which increases the complexity of the coverage issue.
Coverage of networked cameras can be simplified by
assuming that the cameras have a fixed focal length lens, are
mounted on the same plane, and are monitoring a parallel
plane.
Visual data collected by camera nodes should be
processed and all or relevant data streamed to the BS. It is
largely agreed that streaming all the data is impractical due
to the severe energy and bandwidth constraints of WSNs.
And since processing costs are significantly lower than
communication costs, it makes sense to reduce the size of
data before sending it to the BS. However, visual data
processing can be computationally expensive. Reliable data
transmission is an issue that is more crucial for VSNs than
for conventional scalar sensor networks. While scalar sensor
networks can rely on redundant sensor readings through
spatial redundancy in the deployment of sensor nodes to
compensate for occasional losses of sensor measurements,
this solution is impractical for VSNs, which are
characterized by higher cost and larger data traffic.
Moreover, most reliable transmission protocols proposed for
conventional scalar data WSNs are based on link layer
acknowledgment messages and retransmissions. They are
therefore not suitable for visual data transmission due to
their stringent bandwidth and delayrequirements. The initial
phase of visual data processing usually involves object
detection. Object detection may trigger a camera's
processing activity and data communication. Object

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detection is mostly based on light-weight background
substraction algorithms and presents the first step toward
collective reasoning by the camera nodes about the objects
that occupy the monitored space.
Since detection of objects on the scene is usually the first
step in image analysis, it is important to minimize the
chances of objects` fault detection. Thus, reliability and
light-weight operations will continue to be the main
concerns of image processing algorithms for object
detection and occupancyreasoning.
The main objective of [1] is to provide
reconstruction theory and techniques for image
reconstruction and creating enhanced resolution images
from irregularly sampled data. The relationship between the
aperture function, the measurement sampling, and the
reconstruction has been examined in this paper. The
methodology used in this paper is image reconstruction and
resolution enhancement algorithm. This algorithm provide
improved resolution images by taking advantage of
oversampling and the response characteristics of the
aperture function to reconstruct the underlying surface
function sampled by the sensor. This algorithm can generate
images from the observations at a resolution better than the
mainlobe aperture resolution of the sensor.
The algebraic reconstruction technique (ART) and
scatterometer image reconstruction (SIR) algorithms can be
termed resolution enhancement algorithms because of their
ability to fully reconstruct attenuated signal components.
SIR is more robust than Multiplicative ART and Additive
ART in the presence of noise. Both AART and MART
produce slightly different results based on the different
regularizations. The results show that the image
reconstruction and resolution enhancement algorithms such
as AART, MART, and SIR provide an effective way to
increase the effective resolution of remotely sensed
imagery. The advantage of this paper is that the sampling
and aperture function considerations in the design of the
sensor system provide better resolution and the high-pass
nature of the reconstruction filter increases the noise power.
The main drawback of this paper is that it will limit the
number of iterations before noise overtakes the
reconstruction.
The main objective of [2] is to develop a new
algorithm for density estimation using the EM algorithm
with a ME constraint. The proposed Maximum- Entropy
Expectation-Maximization (MEEM) algorithm provides a
recursive method to compute a smooth estimate of the
maximum likelihood estimate. The MEEM algorithm is
particularly suitable for tasks that require the estimation of
a smooth function from limited or partial data, such as image
reconstruction and sensor fieldestimation. The methodology
used in this paper is Maximum-Entropy Expectation –
Maximization algorithm. The MEEM algorithm is used to
provide the optimal estimates of the weight, mean,
covariance for kernel density estimation. The basic EM
algorithm estimates a complete set from partial data sets and
therefore we propose to use the EM and MEEM algorithms
in these image reconstruction and sensor network
applications. The EM algorithm relies on a simple extension
of the lower-bound maximization method to prove that our
algorithm converges to a local maximum on the local
generated by the Cauchy-Schwartz inequality, which serves
as a lower bound on the augmented likelihood function.
The results indicate that, in most cases the results
under maximum entropy show better results than the
conventional EM algorithm. When we use a small number
of centers, the result of minimum entropy penalty shows
better results than the results of the conventional EM
algorithm and maximum entropy penalty. This is due to the
characteristics of maximum and minimum entropy. The
advantages of this paper are that the maximum entropy
solution provides smooth solution and the minimum entropy
solution provides the least smooth distribution. It provides a
very high performance than various othermethods.
The objective of [3] is to develop a theory of phase
singularities (PSs) for image representation. PSs is
calculated by the Laguerre-Gauss filters which contain
important information of an image and provide an efficient
and effective tool for image analysis and presentation. PSs
are invariant to translation and rotation and the positions of
PSs contain nearly complete information for reconstructing
the original image up to a scale. To examine the usefulness
of PSs, we develop two applications: object tracking and
image matching. In object tracking, the iterative closest
point (ICP) algorithm is used to determine the
correspondences of PSs between two adjacent frames. The
use of PSs allows us to precisely determine the motions of
tracked objects. In image matching, we combine PSs and
scale-invariant feature transform (SIFT) descriptor to deal
with the variations between two images and examine the
proposed method on a benchmark database. The ICP
algorithm is used for aligning two groups of points based on
geometrical information. The ICP starts with a rough initial
estimation on the transformation between the two groups of
points, and then iteratively refines the transformation by
identifying the matching points and minimizing an error
metric.
The result shows that PSs are generally stable to
real noise and image deformation and the proposed method
is used to find a large number of matching points for each
pair, which distribute over the whole images. The advantage
of this paper is that this method is more robust and we can
find correct matchingpairs.
The main objective of [4] is to collect considerably
less data than conventional systems, and display only what is
relevant for the task at hand. The proposed method is not an
alternative when the perfect reconstruction of arbitrary
images is required, but nevertheless operates within the
same framework by extracting information from
compressive measurements. Compressed sensing holds the
promise for radically novel sensors that can perfectly
reconstruct images using comparatively simple hardware
and considerably fewer samples of data. In surveillance
applications vast regions of the image may not contains
object of interest, and may therefore not be of significance to
the operator.

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Reconstruction Algorithms is the methodology
used in this paper. In this algorithm reconstruction using
compressed sensing will always require more samples than
if it were possible to directly measure projections on an
underlying basis in which the object is sparse. This paper is
not concerned with perfect reconstruction of the full image
from a relatively few samples, but with the reconstructions
of specific objects that are present in the image. The results
of simulation shown that the proposed approach can be
realized assuming different basis sets to represent the object
and irrespective of the choice of basis set, the weighting
process always yields a better result. The advantage of the
paper is to achieve the greatest possible compression and
reconstruction fidelity and the weights can be optimized to
emphasize greater discrimination between the objects and
background which should lead to enhanced visualization of
interested objects in the image.
The objective of [5] is to present a novel approach
for the study of signal reconstruction from randomly
scattered sensors in a multidimensional space. The random
sampling using constant-mean point processes yields an
unbiased estimate of the signal. Iterative reconstruction
scheme is the methodology used in this paper. The classical
iterative reconstruction forms a sequence of unbiased
estimates of band-limited signals, which converges to the
true function in the mean-square sense. The use of an ideal
band-limited operator in the iterative reconstruction method
improves the reconstruction substantially and removes
many of the artifacts. The iterative estimation method
performs efficiently even when the sensors are sparse. The
performance of the iterative estimation method for 2-D
image reconstruction and field estimation from Poisson and
uniformly distributed sensors are also demonstrated in this
method. The field estimation problem is formulated as
signal reconstruction from scattered sensors. This approach
is an extension of the problem of image reconstruction from
limited samples. The solution to these problems is based on
classical methods for function estimation from irregular
samples. When the samples are distributed according to a
homogeneous Poisson process in the plane, the point
process is constant mean and corresponds to the density of
the process in the limit as the number of samples approaches
infinity.
The simulation results rely on a finite number of
Poisson distributed random samples on a bounded region
.We interpret these random samples as an extraction of a
bounded region from an unbounded plane with an infinite
number of Poisson samples. The advantage of this paper is
that the energy is confined within a certain bandwidth and
improves the reconstruction of images.
II. EXISTING SYSTEM
Computer Vision Algorithm is used in this existing
system. By using this algorithm large amount of digitized
visual data is processed. High end hardware is required for
processing. It leads to the formation of star network
structure, a powerful processing unit. In this system, only
one sensor node is used, so the processing of large amount
of digitized data is difficult. The advantage of this method
is that it allows simplicity of routing. A conceptual problem
of this centralized approach is that it is not scalable, i.e., it
does not scale with the number of sensors used. When
additional nodes are added to such a configuration, the
central processor becomes a major bottleneck. In some
cases, the number of visual sensors may go into the hundreds
.It is obvious that the requirements for transmitting and
processing the data in such a large system are
correspondingly large.
III. PROPOSED SYSTEM
In this project, a framework of feature distribution
scheme is proposed for object matching. Each individual
node maintains only a small amount of information about
the objects seen by the network. Nevertheless, this amount
is sufficient to efficiently route queries through the network
without any degradation of the matching performance.
Efficient processing has to be done on the images received
from nodes to reconstruct the image and respond to user
query. The proposed feature distribution scheme results in
far lower network traffic load. To achieve the maximum
performance as with the full distribution of feature
vectors, a set of requirements regarding abstraction, storage
space, similarity metric and convergence has to be proposed
to implement this work in C++.
The SQL database package is used for database
connectivity. The SQL database performs functions such
as insert, delete and update. The insert function is used to
insert the data into the database. The delete function is
used to delete the entire row in a database. The update
function updates all the data in thedatabase.
IV. RESULTS AND DISCUSSION
In this the user first sets the path of the folder
where the background images are present. After selecting
the path click the start button to process the filename of
the background image and find out the node number of that
background and stored it in the database. Click close to exit
from the window. Fig.1. shows the storage of background
images in the database.
Fig.1. Receive background image

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A. RECEIVE FOREGROUND OBJECT
Fig.2. Receive foregroundobject
In this the user first sets the path of the folder
where the foreground objects are present. After selecting
the path click the start button to process the filename of
the foreground object and find out the node number and
frame number of that foreground and stored it in the
database. Click close to exit from the window. Fig.2. shows
the storage of processed foreground object in the database.
C. IMAGE STITCHING
Fig.3. Image Stitching
In image stitching the node number, frame number
and the co-ordinates of the foreground objects in the
background image have to be found in the database. The
objects which are not stitched with the background in the
database are taken first and then find out the corresponding
node number of that object. Then the node’s corresponding
background image is taken and stitches it with the
foreground object and is stored in the database. After
stitching the process gets completed and this message will
be shown to the user. Click close to exit from the window.
Fig.3. shows the process of image stitching.
D. USER QUERY PROCESSING
Fig.4.Object Reconstruction
When a user wants to know about the foreground
objects that is present during a time, then the user enters the
starting date that is from date and to date and also he enters
the node number that is from which node, the user wants
the foreground object to be seen. The server retrieves the
correct background and foreground objects from the
database and displays it to the user. Fig.4. shows the
reconstructedobject.
V. CONCLUSION
In this work, a framework of feature distribution
scheme is proposed for object matching. In this approach,
information is distributed in such a way that each individual
node maintains only a small amount of information about the
objects seen by the network. Nevertheless, this amount is
sufficient to efficiently route queries through the network
without any degradation of the matching performance.
Digital image processing approaches have been investigated
to reconstruct a high resolution image from aliased low
resolution images. The accurate registrations between low
resolution images are very important to the reconstruction
of a high resolution image. The proposed feature
distribution scheme results in far lower network traffic load.
To achieve the maximum performance as with the full
distribution of feature vectors, a set of requirements
regarding abstraction, storage space, similarity metric and
convergence has been proposed to implement this work in
C++ andQT.
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[7] Lowe, D.G. 2001. Local feature view clustering for 3D object
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