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Reconstruction of Objects with VSN

April 2015
SSRN Electronic Journal 1(1):17-20

Authors:

Samarkand State University named after Sharof Rashidov

By this object reconstruction with feature distribution scheme, efficient processing has to be done on the images received from nodes to reconstruct the image and respond to user query. Object matching methods form the foundation of many state-of-the-art algorithms. Therefore, this feature distribution scheme can be directly applied to several state-of-the-art matching methods with little or no adaptation. The future challenge lies in mapping state-of-the-art matching and reconstruction methods to such a distributed framework. The reconstructed scenes can be converted into a video file format to be displayed as a video, when the user submits the query. This work can be brought into real time by implementing the code on the server side/mobile phone and communicate with several nodes to collect images/objects. This work can be tested in real time with user query results.

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Available online at www.ijarbest.com

International Journal of Advanced Research in Biology, Ecology, Science and Technology (IJARBEST)

Vol. 1, Issue 1, April 2015

Reconstruction of Objects with VSN

M.Priscilla1, B.Nandhini2, S.Manju3, S.Shafiqa Shalaysha4, Christo Ananth5

U.G.Scholars, Department of ECE, Francis Xavier Engineering College, Tirunelveli1,2,3,4

Assistant Professor, Department of ECE, Francis Xavier Engineering College, Tirunelveli 5

Abstract— By this object reconstruction with feature

distribution scheme, efficient processing has to be done

on the images received from nodes to reconstruct the

image and respond to user query. Object matching

methods form the foundation of many state- of-the-art

algorithms. Therefore, this feature distribution scheme

can be directly applied to several state-of- the-art

matching methods with little or no adaptation. The

future challenge lies in mapping state-of-the-art

matching and reconstruction methods to such a

distributed framework. The reconstructed scenes can be

converted into a video file format to be displayed as a

video, when the user submits the query. This work can

be brought into real time by implementing the code on

the server side/mobile phone and communicate with

several nodes to collect images/objects. This work can be

tested in real time with user query results.

Index Terms— Computer vision, Object Reconstruction,

Visual SensorNetworks.

I. INTRODUCTION

Visual sensor networks are envisioned as

distributed and autonomous systems, where cameras

collaborate and, based on exchanged information, reason

autonomously about the captured event and decide how to

proceed. Through collaboration, the cameras relate the

events captured in the images, and they enhance their

understanding of the environment. Similar to wireless

sensor networks, visual sensor networks should be data-

centric, where captured events are described by their names

and attributes. Communication between cameras should be

based on some uniform ontology for the description of the

event and interpretation of the scene dynamics. 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.

In redundantly deployed visual sensor networks a

subset of cameras can perform continuous monitoring and

provide information with a desired quality. This subset of

active cameras can be changed over time, which enables

balancing of the cameras' energy consumption, while

spreading the monitoring task among the cameras. In such a

scenario the decision about the camera nodes' activity and

the duration of their activity is based on sensor management

policies. Sensor management policies define the selection

and scheduling of the camera nodes' activity in such a way

that the visual information from selected cameras satisfies

the application-specified requirements while the use of

camera resources is minimized.

In visual sensor networks, sensor management

policies are needed to assure balance between the oftentimes

opposite requirements imposed by the wireless networking

and vision processing tasks. While reducing energy

consumption by limiting data transmissions is the primary

challenge of energy-constrained visual sensor networks, the

quality of the image data and application QoS improve as the

network provides more data. In such an environment, the

optimization methods for sensor management developed

for wireless sensor networks are oftentimes hard to directly

apply to visual sensor networks. Such sensor management

policies usually do not consider the event- driven nature of

visual sensor networks, nor do they consider the

unpredictability of data traffic caused by eventdetection.

The main obje ctive of [ 1 ] is Hierar chical feature

distribution scheme for object recognition. This paper

focuses on the principle that each individual node in the

network hold only a small amount of information about

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International Journal of Advanced Research in Biology, Ecology, Science and Technology (IJARBEST)

Vol. 1, Issue 1, April 2015

objects seen by the network, this small amount is sufficient

to efficiently route queries through network.In computer

vision algorithm, large amount of data has to be processed,

stored or transmitted. It requires enormous amount of

network resources. Due to restrictions in memory resources

of the nodes and high cost of data transmission there is a

need to distribute visual data in a more efficientway.

The methodology used in this paper is hierarchical

feature distribution scheme. This scheme has to fulfill some

additional requirements regarding feature abstraction,

feature storage space, measure of similarity and

convergence. The visual sensor that originally sees the

unknown object retains complete information about the

object. The Hierarchical feature distribution scheme, results

lower total network traffic load and it does not affect

performance of tested computer vision methods and the

cons of this is, computer vision methods operate on large

amount of data, which can overload communication

constrained over distributed network.

The objective of [2] is to study the classical

problem of object recognition in low-power, low-bandwidth

distributed camera networks. In this paper an effective

framework is proposed to perform distributed object

recognition using smart cameras and computer at the base

station. Base station uses multiple decoding schemes to

recover multiple view object features based on distributed

compressive sensing theory. Object recognition has been a

well studied problem in computer vision. Distributed object

recognition has been mainly focused on two directions.

First, when multiple images share a set of common visual

features, correspondence can be established across camera

views. Second, when the camera sensors do not have

sufficient communication resources to streamline the

high-dimensional visual features among camera views and

perform feature matching, distributed data compression can

be utilized to encode and transmit the features.

The methodology used here is random projection,

in which a projection function is used to encode histogram

vectors in a lower-dimensional space. The multiple

decoding schemes are used to recover multiple view object

features based on distributed compressive sensing theory. A

distributed object recognition system is suitable for band-

limited camera sensor networks. Random projection has

gained much publicity in application where the prior

information of the source data and the computational power

of the sensor modalities.In this paper reduction function is

used to compress high dimensional histogram. The main

disadvantage is that, it will recognize the nearest object and

it does not recognize distant objects.

In [3], the authors had proposed a high-resolution

image reconstruction algorithm considering inaccurate

subpixel registration. Use multichannel image

reconstruction algorithm for application with multiframe

environments. The proposed algorithm is robust against

registration error noise and do not require any prior

information about original image. An iterative

reconstruction algorithm is adopted to determine the

regularization parameter and to reconstruct the image.

Ill-posedness problem will occur from inaccurate pixel

registration. The quality of image resolution is limited by

physical constraints. Subpixel level motion estimation is a

very difficult problem. Multichannel image convolution

approaches are particularly well suited for application with

multiframe environments. The problem of estimating a

high-resolution image from low resolution images is

ill-posed, since a number of solutions satisfy the constraints

of the observation model. A well-posed problem will be

formulated using the regularized multichannel image

deconvolution technique. In this a set of theoretical approach

is used to obtain solution. A prior knowledge about the

image is assumed which restricts the solution. The

significant increase in the high-frequency detailed

information of the reconstructed image, especially when

compared with the results of the bicubic interpolation and

conventional approach.

The proposed algorithm is robust and insensitive

to registration error noise, and they do not require any prior

information about the original image or the registrationerror

process.

The objective of [4] is to capture a short video

sequence, scanning a certain object and utilize information

from multiple frames to improve the chance of a successful

match in the database. Object instance matching is a

cornerstone component in application such as image search,

augmented reality and unsupervised tagging. The common

flow in this application is to take an input image and match

it against a datab ase of previously enrolled image of objects

of interest. Capturing image corresponding to an object

already present in the database especially in case of 3D

object is difficult.The methodology used in this paper is

object matching algorithm. In this algorithm two types of

matching techniques have been used. They are (1) Object

Instance Matching (2) Keypoint Filtering. Object instance

matching is used to incorporate previous frame keypoints

upto the maximum time window, for matching. In keypoint

filtering two filtering schemes are used. The first is to select

and to propagate the keypoint from the previous frame and

the next is to compare the matching image with respect to

highest matching image according to threshold on the

database matchingscore.

By using this object matching algorithm object

instance identification for exploiting time sequence

information in matching objects that are captured in a video

and stored to a database of images can be improved. The

drawbacks of this paper are performance is affected with

larger sets and, space and computational complexity will

occur.

The main objective of [5] is to reconstruct the

complete object trajectories across the field-of-views of

multiple semi-overlapping cameras. The reconstruction of

global trajectories across the multiple fields of view requires

the fusion of multiple simultaneous views of the same

object as well as the linkage of trajectory fragments

captured by individual sensors.

In this paper global trajectory reconstruction

algorithm is used. This approach uses segments of

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International Journal of Advanced Research in Biology, Ecology, Science and Technology (IJARBEST)

Vol. 1, Issue 1, April 2015

trajectories generated by individual cameras and performs

trajectory association and fusion on a common ground

plane. The association and fusion identifies fragments of

transformed trajectories generated by each object. These

fragments are fused and connected across the fields of view

using temporal consistency and object identity. The

proposed approach does not require any inter-camera

handoff procedure when objects enter or exit the

fields-of-view of each individual sensor. Local trajectory

segments are extracted from each sensor and those

corresponding to the same object are first associated and

then fused. Then, a spatiotemporal linkage procedure is

applied to connect the fused segments in order to obtain the

global complete trajectories across the distributed setup.

The results of trajectory association shows that the

matching performance is related to the segment length

which in turn is related to tracking performance and ground

plane transformation as it can affect the accuracy of objects’

attributes. The main drawbacks of this paper are tracking

failure, transformation errors and the trajectory metadata

from individual sensors may be corrupted by errors and

inaccuracies caused by noise, objects re-entrances and

occlusions.

II. PROPOSED SYSTEM

So inorder to overcome the inefficient behaviour

of the existing method, in this paper a method has been

proposed which uses hierarchical feature distribution

scheme for object matching. In this, the information is

distributed hierarchically in such a way that each individual

node maintains only a small amount of information about the

objects seen by the network. This amount is sufficient to

efficiently route queries through the network without any

degradation of the matching performance. A set of

requirements that have to be fulfilled by the

object-matching method to be used in such a framework is

defined. Four requirements (abstraction, storage, existence

of a metric, convergence) are defined for the matching

method and thus provide an algorithm for the efficient

routing of the network queries during the matching. The

object matching and reconstruction is performed in the base

station. The proposed method is implemented in C++ and

QT and it works on linux environment. Object

Reconstruction can be used in mobile applications and is

open source. In mobile applications, this method is used to

view the reconstructed object through mobile at any time.

electronically for review.

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++.

III. RESULTS AND DISCUSSION

At first all the background images are collected

and stored in a folder. The co- ordinates (X1,X2,Y1,Y2)

are noted for all the background images stored in that

folder. During file processing the filename of the

background image is processed and find out the node

number of that background and stored it in the database.

Fig.1. shows the sample background image.

Fig.1.Background Image

A. RECEIVE FOREGROUND OBJECT

The separated foreground objects from the

background images are collected and stored it in a folder

which does not have the background images. The

co-ordinates (X1,X2,Y1,Y2) are noted for all the foreground

objects stored in that folder. During file processing the

filename of the foreground object is processed and find

out the node number and frame number of that foreground

and stored it in the database. F i g. 2 . shows the processed

foreground object.

Fig.2. Foreground Object

B. 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

Available online at www.ijarbest.com

International Journal of Advanced Research in Biology, Ecology, Science and Technology (IJARBEST)

Vol. 1, Issue 1, April 2015

image is taken and stitches it with the foreground object and

is stored in the database. Fig.3. shows the stitched foreground

object in the backgroundimage.

Fig.3. Image Stitching

C. USER QUERY PROCESSING

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 result

of user query which retrieves the stitched image from the

database and shown it to theuser.

Fig.4.User Query

IV. CONCLUSION

By this object reconstruction with feature distribution

scheme, efficient processing has to be done on the images

received from nodes to reconstruct the image and respond to

user query. Object matching methods form the foundation of

many state- of-the-art algorithms. Therefore, this feature

distribution scheme can be directly applied to several

state-of- the-art matching methods with little or no

adaptation. The future challenge lies in mapping

state-of-the-art matching and reconstruction methods to such

a distributed framework. The reconstructed scenes can be

converted into a video file format to be displayed as a video,

when the user submits the query. This work can be brought

into real time by implementing the code on the server

side/mobile phone and communicate with several nodes to

collect images/objects. This work can be tested in real time

with user queryresults.

REFERENCES

[1] Baumberg, "Reliable feature matching across widely separated

views," in IEEE Intl. Conf Comp. Vision, and Patter Recog., 2000.

[2] Byrne, “Iterative image reconstruction algorithms based on

cross-entropy minimization,” IEEE Trans. Image Process., vol. 2, no. 1,

pp.96–103, Jan. 1993.

[3] Cand`es and Tao. Near optimal signal recovery from random

projections: Universal encoding strategies? IEEE Trans. Information

Theory, 52(12):5406– 5425, 200 6.

[4] Can d`es, Romberg, and Tao, T. Robust uncertainty principles: Exact

signal reconstruction from highly incomplete frequency information. IEEE

Transactions on Information Theory, 52, 2 (Feb.2006), 489—509.

[5] Chen, Defrise, and Deconinck, “Symmetric phase-only matched

filtering of Fourier-Mellin transforms for image registration and

recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 16, no.12, pp.

1156–1168, Dec. 1994.

[6] Chen, Varshney, and Arora, “Performance of mutual information

similarity measure for registration of multitemporal remote sensing

images,” IEEE Trans. Geosci. Remote Sens., vol. 41, no. 11, pp.

2445–2454, Nov. 2003.

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Article

Mar 2006

This paper considers the model problem of reconstructing an object from incomplete frequency samples. Consider a discrete-time signal f∈C^N and a randomly chosen set of frequencies Ω. Is it possible to reconstruct f from the partial knowledge of its Fourier coefficients on the set Ω? A typical result of this paper is as follows. Suppose that f is a superposition of |T| spikes f(t)=σ_τ∈Tf(τ)δ(t-τ) obeying |T|≤C_M·(log N)^-1 · |Ω| for some constant C_M>0. We do not know the locations of the spikes nor their amplitudes. Then with probability at least 1-O(N^-M), f can be reconstructed exactly as the solution to the ℓ₁ minimization problem. In short, exact recovery may be obtained by solving a convex optimization problem. We give numerical values for C_M which depend on the desired probability of success. Our result may be interpreted as a novel kind of nonlinear sampling theorem. In effect, it says that any signal made out of |T| spikes may be recovered by convex programming from almost every set of frequencies of size O(|T|·logN). Moreover, this is nearly optimal in the sense that any method succeeding with probability 1-O(N^-M) would in general require a number of frequency samples at least proportional to |T|·logN. The methodology extends to a variety of other situations and higher dimensions. For example, we show how one can reconstruct a piecewise constant (one- or two-dimensional) object from incomplete frequency samples - provided that the number of jumps (discontinuities) obeys the condition above - by minimizing other convex functionals such as the total variation of f.

Exact signal reconstruction from highly incomplete frequency information

Article

Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?

Article

Nov 2006

Suppose we are given a vector f in a class F ⊂ ℝN, e.g., a class of digital signals or digital images. How many linear measurements do we need to make about f to be able to recover f to within precision ε in the Euclidean (ℓ2) metric? This paper shows that if the objects of interest are sparse in a fixed basis or compressible, then it is possible to reconstruct f to within very high accuracy from a small number of random measurements by solving a simple linear program. More precisely, suppose that the nth largest entry of the vector |f| (or of its coefficients in a fixed basis) obeys |f|(n) ≤ R n^(1-p), where R > 0 and p > 0. Suppose that we take measurements yk = {f,Xk}, k = 1, . . .,K, where the Xk are N-dimensional Gaussian vectors with independent standard normal entries. Then for each f obeying the decay estimate above for some 0 < p < 1 and with overwhelming probability, our reconstruction f#, defined as the solution to the constraints yk = 〈f#, Xk〉 with minimal ℓ1 norm, obeys [equation]. There is a sense in which this result is optimal; it is generally impossible to obtain a higher accuracy from any set of K measurements whatsoever. The methodology extends to various other random measurement ensembles; for example, we show that similar results hold if one observes a few randomly sampled Fourier coefficients of f. In fact, the results are quite general and require only two hypotheses on the measurement ensemble which are detailed.

Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?

Article

Jan 2007

Suppose we are given a vector f in a class FsubeRopf^N, e.g., a class of digital signals or digital images. How many linear measurements do we need to make about f to be able to recover f to within precision epsi in the Euclidean (lscr₂) metric? This paper shows that if the objects of interest are sparse in a fixed basis or compressible, then it is possible to reconstruct f to within very high accuracy from a small number of random measurements by solving a simple linear program. More precisely, suppose that the nth largest entry of the vector |f| (or of its coefficients in a fixed basis) obeys |f|_(n)lesRmiddotn^-1p/, where R>0 and p>0. Suppose that we take measurements y_k=langf^#,X_krang,k=1,...,K, where the X_k are N-dimensional Gaussian vectors with independent standard normal entries. Then for each f obeying the decay estimate above for some 0<p<1 and with overwhelming probability, our reconstruction f^t, defined as the solution to the constraints y_k=langf^#,X_krang with minimal lscr₁ norm, obeys parf-f^#par_lscr2lesC_pmiddotRmiddot(K/logN)^-r, r=1/p-1/2. There is a sense in which this result is optimal; it is generally impossible to obtain a higher accuracy from any set of K measurements whatsoever. The methodology extends to various other random measurement ensembles; for example, we show that similar results hold if one observes a few randomly sampled Fourier coefficients of f. In fact, the results are quite general and require only two hypotheses on the measurement ensemble which are detailed

Reconstruction of Objects with VSN

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