Efficient Image Processing Method Based on Web Services for Mobile Devices
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27-03-2010, 01:39 AM

I want a Seminar Report (PPT or Word Document) on the Topic "Efficient Image Processing Method based on Web Services for Mobile Devices"
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23-04-2011, 03:24 PM

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Traditionally image processing software in mobile devices was designed based on the centralized computing mode.
Due to resource limitation this had problems running in mobile devices.
To overcome this, a solution is developed here by adopting web service-based image processing method.
Web service-based image processing method can effectively solve the resource bottle-neck that traditional image processing software had.
Web Service
Web service adopts a Service-Oriented Architecture(SOA) as shown in the above figure.
Service Providers are the owners of the services. They create/maintain and publish the services in the service registry center.
Service Requesters are the enterprises, organization and individuals that have needs of specific service functions. Service requesters can discover services and access the information services, binding in the registry center.
Service Registry Center is a database that can store service description information. In the registry center, service providers publish services, service requesters can discover and access binding information.
Web service as a newly distributed computing model has the following advantages
Excellent Encapsulation
Loose coupling
Standard protocols
Integration capabilities
It can make full use of heterogeneous network computing resources and realize resource sharing effectively.
Considering the several advantages of web service, the technology can be applied to mobile devices in order to improve image processing efficiency.
Design and Implementation of Web Service-Based Image Processing System
Experiment and Discussion
Design and Implementation of Web Service-Based Image Processing System
Image processing service can be defined as follows
Definition 1. Image Processing Service Request can be described as following two-tuple
R= <K,C>
Definition 2. An Image Processing Web Service can be defined as following four-tuple
WS=<R, Q, Res, Qos>
WS denotes image processing services names.
R denotes image processing requests.
Q denotes description of specific image processing services.
Res denotes image processing services’ memory usage.
Qos denotes WS’s quality of services. Generally we consider response time to
R denotes image processing requests.
K denotes R’s parameters set ie.., set K is byte array of image files.
C denotes R’s parameters constraint set ie.., the constraints of image file’s format.
 Web Service-Based Image Processing Model
 Architecture of the system
 Constuction of the system
 Implementation of the system
 Key algorithm-WSDA in system
Experiment and discussion

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25-04-2011, 03:21 PM

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The resource limitation of mobile devices causes the problem that the existing image processing software based on the centralized computing mode had difficulty running in mobile devices. A solution is given in the paper by adopting web service based image processing method. For one thing, image processing tasks were distributed to service providers’ service registry and service requesters. For another, what the service providers should do was only to invocate the specific image processing services provided by service providers. Consequently, web service-based solution reduces the resource consumption of mobile devices by redistributing image processing tasks. Compared with traditional methods of image processing, Web service-based image processing method has the advantages of loose coupling and component oriented and can take full advantage of the computing resources in heterogeneous network. Thus web service-based image processing method can effectively solve the resource bottle-neck that traditional image processing software had.
Image processing is a physical process used to covert an image signal into a physical image. The image signal can be either digital or analog. The actual output itself is an actual output physical image or the characteristics of an image.
Image processing techniques were first developed in 1960 through the collaboration of a wide range of scientists and academics. The main focus was to develop medical imaging, character recognition and create high quality images at the microscopic level. During this period, equipment and processing costs were prohibitively high.
The most common type of image processing is photography. In this process, an image is captured using a camera to create a digital or analog image. In order to produce a physical picture, the image is processed using the appropriate technology based on the input source type.
In digital photography, the image is stored as a computer file. This file is translated using photographic software to generate an actual image. The colors, shading, and nuances are all captured at the time the photograph is taken the software translates this information into an image. Image processing usually refers to digital image processing, but optical and analog image processing are also possible.
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of Multidimensional Systems.
When creating images using analog photography, the image is burned into a film using a chemical reaction triggered by controlled exposure to light. The image is processed in a darkroom, using special chemicals to create the actual image. This process is decreasing in popularity due to the advent of digital photography, which requires less effort and special training to product images.
There are three major benefits to digital image processing. The consistent high quality of the image, the low cost of processing and the ability to manipulate all aspects of the process are all great benefits. As long as computer processing speed continues to increase while the cost of storage memory continues to drop, the field of image processing will grow.
Centralized computing is computing done at a central location, using terminals that are attached to a central computer. The computer itself may control all the peripherals directly (if they are physically connected to the central computer), or they may be attached via a terminal server. Alternatively, if the terminals have the capability, they may be able to connect to the central computer over the network. The terminals may be text terminals or thin clients, for example.
It offers greater security over decentralized systems because all of the processing is controlled in a central location. In addition, if one terminal breaks down, the user can simply go to another terminal and log in again, and all of their files will still be accessible. Depending on the system, they may even be able to resume their session from the point they were at before, as if nothing had happened.
This type of arrangement does have some disadvantages. The central computer performs the computing functions and controls the remote terminals. This type of system relies totally on the central computer. Should the central computer crash, the entire system will "go down" (i.e. will be unavailable).
Another disadvantage is that central computing relies heavily on the quality of administration and resources provided to its users. Should the central computer be inadequately supported by any means (e.g. size of home directories, problems regarding administration), then your usage will suffer greatly. Please note, the reverse situation (i.e., a system supported better than your needs) is one of the key advantages to centralized computing.

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