International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
Topographical Panoramic Imageproduction using Mobile Cloud
ATHULYA AJAYAN
Computer science and engineering
Noorul islam university
Tamilnadu, India.
Abstract-- Increasing usage of mobile
computing, exploiting its full potential is difficult due to
its inherent problems. The main problems are resource
scarcity, frequent disconnections, battery problems and
mobility. Mobile cloud computing can address the
above problems by executing mobile applications on
resource providers external to the mobile device. Mobile
cloud computing aims to empower the mobile user by
providing a high functionality, because of the resource
limitations of mobiles. Here consider one problem issue
as operational issue and provide some technologies to
overcome the existing problems. The concept of
offloading data and computation in cloud computing is
used to address the inherent problems in mobile
computing by using resource providers other than the
mobile device itself to host the execution of mobile
applications. Mobile cloud in Disaster relief: scenario a
massive earthquake or flood or cyclones results in much
human loss, severe infrastructure and property
destruction. Disaster relief teams usually face several
difficulties because of limited manpower, lack of
transportation, battery power and poor communication.
In proposed work, use photographs taken from
different Mobile devices from a disaster struck area and
send it to a cloud server. The server stitches these
images together to produce a big panoramic image. This
image represents the present status of the topography of
the region after the disaster. The stitching happens
considering several factors like location, time ,
gyroscope value and other common factors.
Keywords---Topographical
panoramic
image,
Offloading, Harris points, Correlation analysis,
RANSAC method, BLEND method.
I.
INTRODUCTION
Mobile devices and mobile technologies are the most
essential part of human life. These are most effective
and convenient tool used for communication purpose
[1]. The day by day progress of mobile computing
(MC) becomes a powerful trend in the development
of IT technology also in industry fields. However,
the mobile devices are facing many challenges in
their resources (e.g. ,battery life, storage, and band
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width) and communications (e.g., mobility and
security) [2].
Tablets, laptops, smart phones and cloud
computing are converging in the new, quick growing
field of mobile cloud computing. Learn about the
devices (smart phones, tablets, Wi-Fi sensors), and
the trends (more flexible application development,
changing work patterns), the issues (device resource
poverty,bandwidth, security), and the enabling
technologies that come along with a more mobile,
device-usable in cloud environment. Mobile cloud
computing aims to empower the mobile user by
providing a seamless and rich functionality,
regardless of the resource limitations of mobile
devices[3].
Issues of mobile computing introduce one
coming technology as cloud computing. Cloud
computing (CC) has been widely recognized as the
new generation’s computing infrastructure field.
Cloud computing offers some advantages by
allowing users to use infrastructure, platforms, and
software provided by cloud providers. Here specify
some infrastructures as server, network ,storages and
platforms like middleware services and operating
systems. Together with an explosive growth of the
mobile applications and emerging of cloud
computing concept, mobile cloud computing
(MCC)[4] has been introduced a technology for
mobile services. Mobile cloud computing integrates
the cloud computing technology into the mobile
environment and overcomes obstacles related to the
performance
(e.g.,
battery
life,
storage,
communication and bandwidth), environment (e.g.,
heterogeneity, scalability, and availability), and
security (e.g., reliability and privacy) in mobile
computing.
In mobile cloud computing introduce some
issues such as operational level issues, End user level
issues, Service and application level issues, Privacy
security and trust, Context awareness, Resource
management and Data management. Here focus
mainly operational level issues. From that introduce
offloading techniques in mobile cloud area. How we
join the images taken from different mobile devices
and stitches the images in cloud area.
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International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
II.
LITERATURE REVIEW
The required works are based on method of
offloading techniques. All references show how to
stitches the images and what are the algorithms used
to connect data. Commonly we used mobile phones
to take the pictures and installed in a central server.
These server stitching the images and form the
panoramic images.
The proposed work” modeling mobility in
disaster area scenarios”[5] represents the movements
in a disaster area scenario. The model based on an
analysis of tactical issues of civil protection. This
analysis provides characteristics influencing network
performance in public safety communication
networks like heterogeneous area-based movement,
obstacles, and joining/leaving of nodes. When
creating a scenario for performance evaluation of a
disaster area communication system, modeling the
mobility is an important task because the results of
the evaluation strongly depend on the model used.
Typical assumptions of many models are uniform
selection of destinations, nodes are allowed to move
over the whole simulation area, and nodes are part of
the network all the time (are not switched off and do
not leave the network). The goal of this paper is to
study the movement of civil protection units in a
disaster area scenario and figure out how this
movement can be represented in a mobility model.
Mainly describe typical movement in a
disaster area scenario to point out characteristics to
be considered developing mobility models for such a
scenario. After that, evaluates whether the movement
generated with new disaster area mobility model
shows an impact when compared to existing ones.
For the evaluation take one concrete scenario and
model the movement using different models as
accurate as the particular model allows us to do.
Next, choose and adapt a set of mobility metrics and
evaluate the characteristics of the new model. After
shows the characteristics of the new model have an
impact on simulative network performance analysis.
Computation offloading has been shown to
be an effective way to improve performance and
energy saving on mobile devices[6]. Optimal
program partitioning for computation offloading
depends on the tradeoff between the computation
workload and the communication cost. The
computation
workload
and
communication
requirement may change with different execution
instances. Many programs can be invoked under
different execution options, input parameters and data
files. Such different execution contexts may lead to
strikingly different execution instances. The optimal
code generation may be sensitive to the execution
instances.
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The proposed work shows how to use
parametric program analysis to deal with this issue
for the optimization problem of computation
offloading. Many programs can be invoked under
different execution options, input parameters and data
files. The different execution contexts may lead to
strikingly different execution instances. The optimal
code generation may be sensitive to the execution
instances. In this paper, we show how to use
parametric program analysis to deal with this issue
for the optimization problem of computation
offloading. In a client-server distributed computing
environment, the efficiency of an application
program can be improved by careful partitioning of
the program between the server and the client.
The work " map reduce: simplified data
processing on large clusters” [7] says Map Reduce is
a programming model and an associated
implementation for processing and generating large
data sets. Users specify a map function that processes
a key/value pair to generate a set of intermediate
key/value pairs, and a reduce function that merges all
intermediate values associated with the same
intermediate key. The run-time system takes care of
the details of partitioning the input data, scheduling
the program’s execution across a set of machines,
handling machine failures, and managing the required
inter-machine
communication.
This
allows
programmers without any experience with parallel
and distributed systems to easily utilize the resources
of a large distributed system. The input data is
usually large and the computations have to be
distributed across hundreds or thousands of machines
in order to finish in a reasonable amount of time. The
issues of how to parallelize the computation,
distribute the data, and handle failures conspire to
obscure the original simple computation with large
amounts of complex code to deal with these issues.
Cloud computing provides many advantages for
businesses including low initial capital investment,
shorter start-up time for new services, lower
maintenance and operation costs, higher utilization
through virtualization, and easier disaster recovery
that make cloud computing an attractive option. The
primary constraints for mobile computing are limited
energy and wireless bandwidth. Cloud computing can
provide energy savings as a service to mobile users,
though
it
also
poses
some
unique
challenges[8].Mobile systems, such as smart phones,
have become the primary computing platform for
many users. Various studies have identified longer
battery lifetime as the most desired feature of such
systems .Many applications are too computation
intensive to perform on a mobile system. If a mobile
user wants to use such applications, the computation
must be performed in the cloud. Other applications
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International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
such as image retrieval, voice recognition, gaming,
and navigation can run on a mobile system .From this
work we conclude that the offloading technique
reduce the energy for mapping the images.
Mobile systems have limited
resources, such as battery life, network bandwidth,
storage capacity, and processor performance. These
restrictions may be alleviated by computation off
loadin[9]g: sending heavy computation to
resourceful servers and receiving the results from
these servers. Many issues related to offloading have
been investigated in the past decade. This survey
paper provides an overview of the background,
techniques, systems, and research areas for
offloading computation. Off loading is a solution to
augment mobile systems’ capabilities by migrating
computation to more resourceful computers (i.e.,
servers).
III.
PROPOSED SYSTEM
Mobile cloud in Disaster relief[12]: scenario a
massive earthquake or flood or cyclones results in
much human loss, severe infrastructure and property
destruction. Disaster relief teams usually face several
difficulties because of limited manpower, lack of
transportation, and poor communication. The new
paradigm of cloud computing provides an array of
benefits and advantages over the previous computing
paradigms and many organizations are migrating and
adopting it. However, there are still a number of
challenges, which are currently addressed by
researchers, academicians and practitioners in the
field. Several Digital and conventional maps exist but
are not up-to-date with latest information.
Conventional Maps: Printed Maps, Atlas, Static GPS
devices in Vehicles.
Digital Maps: Google Earth, Google Maps,
Nokia Maps, OpenStreetMap, Yahoo! Maps and
Bing Maps.
Here also used some pre installed technology
such as croma [10] , spectra[11] and hyrax in smart
phones. .These technologies are used in client server
communication side.
The proposed system uses photographs
taken from different mobile devices from a disaster
struck area and send it to a cloud server. The server
stitches these images together to produce a big
panoramic image. This image represents the actual
status of the topography of the region after the
disaster. The stitching occurs considering several
factors like GPS location, time and other factors [13].
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Creating a real-time photographic map of an area
after being hit by a disaster. The created photographic
map will be updated one reflecting the topology of
the affected area. This map will help the relief teams
in real-time, by giving them the current map instead
of an out-dated print map. The output of required
work is a huge aerial photographic map. The resultant
map is produced from stitching several images
obtained from different mobile devices in the disaster
affected area[14].
Fig 1 shows the main issues faces in the mobile
cloud areas. Here all the issues in mobile areas
overcome using cloud areas.
IV.
WORKING PRINCIPLE
Today one another functionality, tried in the android
application. It was to provide the longitude and
latitude of the current location in android using it
GPS system. The coding was quite easy. We have to
create a Location Manager and Location Listener.
And sending the GPS_PROVIDE, we can get the
current location value in term of longitude and
latitude. The other value we used is gyroscope. It is a
device used for measuring or maintaining orientation,
based on the principle of angular momentum. This is
used to sense the orientation of the phone. It added a
lot of cool functionality to mobile phone. The UI(user
interface) can be automatically rotated either in
portrait or landscape mode, depending on the phone's
orientation. An accelerometer measures only the
linear acceleration of the device whereas a gyroscope
measures the orientation of the device. It can sense
motion including vertical and horizontal rotation.
CLIENT SIDE:
Here the work has to take the photographic
images using our mobile phones. Then collect all the
images from different mobile phones and uploaded
into server side. Then the taken photos are send to
server with sensor values. The sensor values are the
GPS values used to find the locations. Accelerometer,
gyroscope is the sensor values which we used for
direction and altitude. At last the values are appended
to the image side. The complete images shows the
clear image of each parts.
SERVER SIDE:
The received photos from client side are
stored to the database with GPS and other sensor
values. Then the images are sorted and identified into
different groups based on GPS values.
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International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
Fig 1: issues in mobile cloud technology
 CLIENT SIDE :

-Identification of nearby images.
CAMERA APPLICATION MODULE
-Take photos from different mobile phones.
-Stitching , joining and merging of images.

-Show the preview of the images.

-Elimination of false positive.
VIEW MODULE
-The output of the images.
GPS MODULE
-Append the current GPS to the photo.

V.
-Append the gyro and accelerometer value.

SENDING/ COMMUNICATION MODULE
-sends the photos to server side.
-Sends the sensor values .
 SERVER SIDE:

RECEIVING MODULE
-Receives the photos.
-Receives the sensor values.

ALGORITHM – PANORAMIC PRODUCTION
SENSOR MODULE
STORAGE MODULE
Input: n unordered images
I. Extract SIFT(scale invariant feature transform)
features from all n images
II. Find all nearest-neighbors’ for each feature .
III. For each image:
(i) Select number of candidate matching images (with
the maximum number of feature matches to this
image)
(ii) Find geometrically consistent feature matches
using RANSAC to solve for the homography
between pairs of images
(iii) Verify image matches using probabilistic model
IV. (i) Find connected components of image matches
(ii) Render panorama using multi-band blending
method
-Stores the photos and GPS values.

Output: Panoramic image(s)
PANORAMA GENERATION
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International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
VI.
IMPLEMENTATION
Mobile cloud in Disaster relief: scenario a
massive earthquake or flood or cyclones results in
much human loss, severe infrastructure and property
destruction. Disaster relief teams usually face several
difficulties because of limited manpower, lack of
transportation, and poor communication.
The images from the client part loaded into the
server side. Here the images are goes through
different methods. The different methods are
explaining below.
Previous maps on terrain and buildings are
suddenly rendered useless at present, which
contributes to slow disaster relief. Data on Google
Earth and Google Maps on this area is now useless
since highways, bridges, landmarks and buildings
would be collapsed. To conduct efficient search and
rescue operations, new data must be gained and a
clear picture of the terrain and buildings state must be
constructed.
Operational issues (method of offloading)


A.
Images are offloaded to a cloud server and
stitched. Images are sent to the cloud using
http/https stream in a per device basis.
Location of the image is also used as a
stitching factor. Images are organized based
on location. Only images from same
location are stitched together.
FLOWCHART FOR CLIENT SIDE
APPLICATION
The fig 2 shows how we take the photographs from
different camera and what are the modules which we
used. The client is used for taking photographs and
sends these images to the cloud server. The client
appends many details such as GPS coordinates, time,
Compass position etc.
fig3 : server side
The images are uploaded into the server
system. The fig 4 shows two sample images which
we use to produce panoramic image.
The harris method select the equal parts
present in different images and generate similar
points from that images. The following steps shows
how to detect the points from images.
Step 1: Detect feature points using Harris Corners
Detector
Step2 : Show the marked points in the original image.
Step3: Concatenate the two images together in a
single image (just to show on screen).
To make the process faster, we could use a
higher suppression threshold for suppressing some
points.Correlation is the another process used,it
identify the angle difference between the images. The
fig 6 specify how they are correlated with each other.
And also specify the steps which we used.
Step 4: Match feature points using a correlation
measure.
Step 5: Get the two sets of points.
Step 6 : Concatenate the two images in a single
image (just to show on screen)
Step 7: Show the marked correlations in the
concatenated image.
fig 2 : client side
B. FLOW CHART FOR SERVER SIDE
APLICATION
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Robust Homography Matrix Estimator
(RANSAC)
is used to straitening the angle
difference. Robustly estimating camera homography
using fuzzy RANSAC from the correspondences
between consecutive two images.
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International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
fig 7 : RANSAC method
Fig 4:uploading 2 images
fig 8 :blend method
Fig 5: harris point generation
Step 10: Concatenate the two images in a single
image (just to show on screen).
Step 11: Show the marked correlations in the
concatenated image.
Step 12: Project and blend the second image using
the homography
Image blending is a common practice in the
generation of panoramic images and applications
such as object insertion, super resolution and texture
synthesis.
fig 6: correlation analysis
RANSAC is opposite method of traditional
smoothing techniques. It uses small an initial data as
feasible and enlarges the consensus set.
Step 8: Create the homography matrix using a
robust estimator.
Step 9: Plot RANSAC results against correlation
results.
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The images should be stitched to generate a
mosaic image. In a set of images, the reference image
is chosen. Second image in the set is transformed to
get aligned with the reference image. This gives us a
blended image of first two. Now this is treated as the
reference and third image is transformed according to
new reference. In this way all images are blended and
a final panoramic image is obtained.
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International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
VII.
CONCLUSION
The proposed work “Topographical Panoramic
Image production” will be playing key role in
disaster management and recovery processes. Ariel
photography captured by the users are utilized
efficiently and can be improved its performance for
future works. Using the offloading technique, can
improve the energy and the battery power.
The image stitching can be enhanced for
future usages with real-time video captured by the
clients on spot of the disaster location. The future
researches can be used for creating Instant maps of
the disaster locations for easy and speedy relief
protocols.
VIII.
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