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1. Problem Identification Phase

1.1 Five problems with five line description of each statement

1.1.1 Image Seeker

Image Seeker is a system used to retrieve an image using Multi Feature

Content Based Image Retrieval System(CBIR). Content Based Image Retrieval is a

technique which uses visual contents, normally called as features, to search images

from large scale image databases according to users requests in the form of a query

image.

1.1.2 Private Keyword Search in Cloud Computing

Recently, Li et al. introduced a fuzzy keyword search over encrypted data

in Cloud Computing. Their approach relies on fuzzy key-word sets which are used by

a symmetric searchable encryption protocol. The idea behind these fuzzy keyword

sets is to index - before the search phase - the exact keywords but also the ones

differing slightly according to a fixed bound on the tolerated edit distance.

1.1.3Mail specialist

This is a software that will receive mails from different mail providers like

yahoo mail, AOL, Gmail, msn and so on. The software will have a text editor for

replying mails back to any of the mail providers.

Additionally the software will be able to arrange the priority of the incoming

mails, that is in such a way that the most important mail will be on top while the less

important will be below. The punch line in this software is that someone/company

can receive, read, send mails from different mail providers without having to open

different web pages for the different mail providers.

Dept. of Computer Science and Engineering, BVBCET, Hubli Page 1


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4.E-library using cloud computing

The main idea of this system is to avoid multiple copies of same books being

purchased and to make books available for all.

Cloud computing is an emerging technique where in the services are provided

to the end user independent of location. In this proposed system the books are

provided as service for the user.

Using this method a single copy (soft) of book of any subject will be stored on

the cloud & the users/students will be able to use these books just as a service not

downloading or copying it. Efficient search system can also be provided in order to

search a book in e-library.

5.Image Processing - Noise Reduction

Images taken with both digital cameras and conventional film cameras will

pick up noise from a variety of sources.

Many further uses of these images require that the noise will be (partially)

removed. Here we are going to develop a software to reduce a particular noise "Salt

and Pepper Noise

In salt and pepper noise , pixels in the image are vastly different in color from

their surrounding pixels. When viewed, the image contains dark and white dots,

hence the term salt and pepper noise. Commonly median filter method is used to

reduce this noise .

1.2 One page description of three problems

Reason for rejecting Mail Specialist:

1. The requirements were not clearly defined.

2. There was not much improvement in the proposed system compared to

existing system.



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Reasons for rejecting E-library using cloud computing

1. Complexity of the proposed system was not sufficient.

2. Hardware and software required were too costly.

1. NOISE REDUCTION USING MODIFIED PROGRESSIVE

SWITCHING MEDIAN FILTER (MPSMF).

Product Description:

The proposed problem is to design a system that reduces a particular noise

called salt & pepper noise. Improved Progressive Switching Median filter

algorithm(IPSMF) used to reduce this noise. In this algorithm sets a limit on the

number of good pixels used in determine median & mean values and substitute

impulse pixel with half of the value of the summation of mean & median values. The

system has better noise filtering ability as the images are highly corrupted.

Concept used:

The digital image is given as input which is corrupted . The input color imageis converted to grey-scale image. Then the grey-scale image is converted to binary

image. In the next step, the noise is detected and filtered. Instead of replacing a

noisy pixel value with a median value of surrounding pixel values we can calculate

mean & median value .Replace the impulse value with half of the summation of

mean & median values.

2. Fuzzy Keyword Search over Encrypted Data in Cloud Computing

Product Description:

To solve the problem of effective fuzzy keyword search over encrypted cloud

data while maintaining keyword privacy. Fuzzy keyword search greatly enhances

system usability by returning the matching files when users searching inputs exactly

match the predefined keywords or the closest possible matching files based on

keyword similarity semantics, when exact match fails.



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Concept Used:

String Matching Algorithm

An instance M of the data type string-matching is an object maintaining a

pattern and a string. It provides a collection of different algorithms for computation ofthe exact string matching problem. Each function computes a list of all starting

positions of occurrences of the pattern in the string.

Figure 1.2.1

3. IMAGE SEEKER

Product Description:The proposed problem is to design a system that retrieves a collection of

related images for a given input image using color histogram and edge histogram

descriptor.

Concept used:

The images to be displayed as output are stored in disc. The color and edge

histogram values are extracted and stored into database. The user uploads the input

image. The input image is pre-processed to perform noise removal and imagesegmentation. Then the color and edge histogram values are extracted from pre-

processed image. Then the extracted values are compared with stored values in

database. The vector distance is calculated ,sorted and images are displayed to

user.



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1.3 Definition of finalized problem with justification for choice.

1.3.1 Introduction

The aim of the project is to retrieve an image using Multi Feature ContentBased Image Retrieval System(CBIR). Content Based Image Retrieval is a

technique which uses visual contents, normally called as features, to search images

from large scale image databases according to users requests in the form of a query

image.

1.3.2 Problem definition

The proposed problem is to design a system that retrieves a collection of

related images for a given input image using color histogram and edge histogram

descriptor.

1.3.3 Objectives of the project

1. Extracting the visual features of an image such as color, edge etc.

2. Converting those visual features into comparable format.

3. Retrieval based on similarity defined in terms of visual features.

4. To provide an easy user interface to input the object image.

5. Comparing the retrieval effectiveness and computation time with QBIC system.



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2. System Study

2.1 Existing System (Advantages and Disadvantages of existing

System)

QBIC (Query By Image Content) was the first prototype to be proposed by IBM.

That system allows queries by color, shape, texture & introduced a sophisticated

similarity function.

Advantages:

1. Images are retrieved based on texture, color and edge features of the image.

Disadvantages:

1. It cannot be used to solve domain specific problems.

2. It is not suited to carry specialized image retrieval.

3. The similarity function has a quadratic time-complexity, the notion of

dimensional reduction was discussed in order to reduce the computation time.

2.2 Proposed System

The proposed system uses the Histogram Intersection-based image retrieval in HSV

color space to efficiently retrieve the image with lesser time complexity.

Fig 2.2.1


Input CBIR System Output

Image Database


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2.3 Advantages of proposed system

The color feature is one of the most widely used visual features in image

retrieval. Images characterized by color features have many advantages:

Robustness. The color histogram is invariant to rotation of the image on the view

axis, and changes in small steps when rotated otherwise or scaled. It is also

insensitive to changes in image and histogram resolution and occlusion.

Effectiveness. There is high percentage of relevance between the query image andthe extracted matching images.

Implementation simplicity. The construction of the color histogram is a

straightforward process, including scanning the image, assigning color values to the

resolution of the histogram, and building the histogram using color components as

indices.

Computational simplicity. The histogram computation has O(X, Y ) complexity for

images of size X Y . The complexity for a single image match is linear, O(n), where

n represents the number of different colors, or resolution of the histogram.

Low storage requirements. The color histogram size is significantly smaller than the

image itself, assuming color quantization.

2.4 Feasibility Study

Phases Estimated duration Actual duration

Problem identification 15-20 hrs 25-30hrs

Software requirementspecification

20-30 hrs 30 hrs

Design phase 33-35hrs 40hrs

Learning andImplementation

65-70hrs

Testing 25hrs



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2.5 Constraints

1. Animated images cannot be given as input.

2. Image database is static.

3. The input and output are always colored images.

4. Only digital images can be used for processing. No graphical images can be

used.

5. System is not integrated with web.



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3. Software Requirement Specification3.1 Introduction

3.1.1 Purpose

The purpose of this project is to make retrieval of images efficient.

Rather than giving the description as input, image is given as input. The

proposed system uses the Histogram Intersection-based image retrieval in

HSV color space to efficiently retrieve the image with lesser time complexity.

3.1.2 Scope of Project

Image seeker is software used to retrieve related images for a given input

image. For example, if the input image is of a car then related images of the car

should be retrieved. Feature extraction of image is limited to color and edge, here

texture and shape features are not considered. Images to be displayed as output

are stored on the disk.

Before using this system, the user should have the image to be given as an

input. When the user uploads the image the system will display the related images

as output. Animated images and images showing emotions cannot be retrieved.

3.1.3 Intended AudienceThis document is intended for following readers

1. Developers

2. Testers

3. Users

4. Project guide5. Evaluators

3.1.4 References[1]. RajshreeDubey, RajnishChoubey,SanjeevDubey, Efficient Image Mining

using Multi Feature Content Based Image Retrieval System,IntJr of Advanced

Computer Engineering and Architecture Vol. 1, No. 1, June 2011

[2]. http://ijacea.yolasite.com/resources/3.pdf

[3]. http://encyclopedia.jrank.org/articles/pages/6763/Image-Retrieval.html

[4]. http://www.naun.org/journals/bio/bio-2.pdf

http://ijacea.yolasite.com/resources/3.pdfhttp://encyclopedia.jrank.org/articles/pages/6763/Image-Retrieval.htmlhttp://www.naun.org/journals/bio/bio-2.pdfhttp://ijacea.yolasite.com/resources/3.pdfhttp://encyclopedia.jrank.org/articles/pages/6763/Image-Retrieval.htmlhttp://www.naun.org/journals/bio/bio-2.pdf


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[5]. http://www.jisc.ac.uk/uploaded_documents/jtap-039.doc

3.2 Overall description

3.2.1 Product Perspective

The aim of the project is to retrieve related images for the given input image.

This is achieved using Multi Feature Content Based Image Retrieval System (CBIR).

Content Based Image Retrieval is a technique which uses visual contents, normally

called as features, to search images from large scale image databases according to

users requests in the form of a query image.

A combination of two feature extraction methods namely color Histogram and

Edge Histogram Descriptor are used and the distances are calculated of the every

features are added and the averages are made and the ranked images are retrieved.

3.2.2 User Classes and Characteristics

1. This system can be used in prevention of crime. Law enforcement

agencies can use this system.

2. The system can be used in Medical field. Different user classes undermedical field are doctors, scientists and teachers.

3. System can be used in biodiversity information field. Biologists are the

users under this category.

4. The proposed software can be applied in Journalism & stock markets.

Journalists and brokers are user classes.

3.2.3 Operating Environment

Software Requirements:

1. Matlab 7.8 or above version.

2. Multimedia database.

Hardware Requirements:1. Core 2 Duo and above processor.

2. 1GB or above RAM.

3. 160GB or above hard disk.

http://www.jisc.ac.uk/uploaded_documents/jtap-039.dochttp://www.jisc.ac.uk/uploaded_documents/jtap-039.doc


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3.2.4 Design and Implementation Constraints

1.Software, if integrated with web then the design should be modified.

2.No graphical images can be used for image retrieval.

3.The system does not work forimages taken from different distances

of the same object.

3.2.5 Assumptions and dependencies

Assumptions:

1.Every user is assumed to be provided with login and password.2.It is assumed that the user already has the image to be uploaded.

3.It is assumed that image is uploaded successfully to preprocess it.

4.The image which is given as an input (and also related images) by

the user is assumed to be already present in the database.

Dependencies:

Retrieval efficiency depends upon the database used.

3.3 Requirement Specification

3.3.1 Functional Requirements

1. Apply color and edge histograms to all the images stored in the

database and store the results.

2. User should be able to upload the image.

3.Preprocess the given input image to reduce the noise.

4.Apply the color histogram & edge histogram to the pre processed

input image.

5.Compare the values and sort them in the ascending order and

retrieve top 10 search results.



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Using USE CASE Diagrams:

Description: A login mechanism shall be provided to all the user classes

User Actor: User

Precondition: The user should have the login Id and password.

Main Scenario:

User enters his/her ID and password.

Successful login if ID and password are correct.

Extension Scenario:

If the ID or password is wrong.

Not possible to log in.

Post Condition:

User logs in successfully.

Description: A user shall be able to upload image.

User

Actor: User

Precondition: Input image should be available.

Main Scenario:

User uploads image.

Extension Scenario:

If the image is not compatible.

Not possible to upload file.

Post Condition:

Image successfully uploaded.


Image seeker

Login

Image seeker

Upload


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Description: System should do pre-processing of input image.

System

Actor: System

Precondition: Image should be already uploaded.

Main Scenario:

System does pre processing of image

Post Condition:

Pre processed image obtained.

Description: System should obtain histogram of an image.

System

Actor: System

Precondition: Preprocessed input image.

Main Scenario:

System extracts color and edge histogram values of

preprocessed image.

Post Condition:

Histogram values of pre processed image obtained.

Description: System should compare and sort histogram results.


Image seeker

Preprocessi

ng

Image seeker

Obtaininghistogram

Image seeker

Compare andsort values


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System

Actor: System

Precondition: Stored histogram values of images present in database.

Main Scenario:

System compares histogram values of input image and

database images and sorts.

Post Condition:

Sorted histogram values obtained.

Description: System should display results

Actor: System

Precondition: Compared and sorted histogram values.

Main Scenario:

System displays results

Post condition:

User obtains images related to the query image.

3.4 Nonfunctional Requirements

3.4.1 Performance requirements

90% of image retrieval shall be completed within 2 seconds.

3.4.2 Safety requirements

No provision for safety is made in case of system crash.

3.4.3 Security Requirements

Application is general purpose and it can be used by any user provided with

login and password.

3.4.4 Software Quality Attributes


Image seeker

Displayin

g results


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Software quality can be classified into a set of characteristics and sub-

characteristics as follows

Functionality: This software will deliver on the functional requirements

mentioned in this document.

Reliability: This software will work reliably under the said conditions.

Learnability: The software is very easy to use and comes with documentation

which reduces the learning curve.

Portability: Since the software is a standalone system it can be used in

different operating system environments thus making it portable.

3.4.5 User DocumentationUser manual will be provided in order to help users understand operation of

the software.

3.5 External Interface Requirements

3.5.1 User Interfaces

Login Screen:



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I m a g e S e e k e r

L o g i n C a n c e l

U s e r n a m e

P a s s w o r d

Before Search:



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U p l o a d S e a r c h

B r o w s e

O K

C a n c e l

C : /

Icon.bmp Icon.bmp Icon.bmp Icon.bmp


F e r r a r i . j p g

F e r r a r i . j p gI c o n 1 . j p gI c o n 2 . j p gI c o n 3 . j p g

I c o n 4 . j p gI c o n 5 . j p gI c o n 6 . j p gI c o n 7 . j p g

A l l . j p g f i l e s

After Search:



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3.5.2 Software Interfaces

Database used: Built-in Database of Matlab (using JDBC) / MySQL.

Operating system: Windows XP

Tool used: Matlab 7.8.0 version

3.6 Other Requirements

3.6.1 Total Cost:

Since all the tools used are open source total project cost is Rs. 0/-.

3.6.2 Process model used:

Iterative process model

3.6.3 Acceptance test plan:



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Test Id Input Description Expected Output Actual Output1 User provides login and

password

If the login and password is

valid allow the user to login

else

Ask the user to verify the

login and password

2 User clicks on the

browse button

User shall be able to upload

the input query image

3 User clicks on the

search button

User shall be able to view

the retrieved images.

4 Uploaded Image is

given for Pre

processing module

Noise free uploaded image

5 Histogram values of pre

processed input imageand histogram values of

images stored in

database are given for

comparison module.

To retrieve the images which

has the least differenceduring comparison and

displays output

3.7 Appendix- A

Glossary:

1. HSV- Hue Saturation Value

2. CBIR- Content Based Image Retrieval

4. Software design

4.1 Introduction4.1.1 Summary:



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Purpose:

The purpose of SDD is to provide design for Image Seeker.

Image Seeker is software used to retrieve related images for a given input

image. A SDD details how the software requirements should be implemented.

Programmers will be able use this document to understand and work on the

system.

Scope: The document acts as a guide for the developers in the implementation

phase.

It provides the information about the various methods and mechanisms to be

used to implement the functionalities stated in the requirement specification

document Image Seeker.

People interested in extending this project can refer this document.

Intended Audience:

Developers.

Guide.

Evaluators.

People interested in extending this project can refer this document.

4.1.2 Terminology: SDD- Software design document.

CCH- Conventional color histogram.

FCH- Fuzzy color histogram.

4.1.3 Design goals and Non goals:Goals:



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The main goal is to design a software product that allows :

user to upload the input query image.

System to perform pre-processing of uploaded image.

System to extract the color and edge histogram values.

System to calculate vector distance

System to retrieve and display top 10 images.

Non-goals:

The system can be extended by integrating with web.

Animated images cannot be used.

The features extracted from images are limited to color and edge.

4.1.4 Common Scenarios:

The user uploads the input query image.

System does the pre-processing of uploaded image.

System extracts the color and edge histogram of the pre-processed image.

System calculates the vector distance.

System retrieves and displays top ten images as result.

Scenario : To upload an input query image.

-user logins using username and password and clicks on login button.

-if authentication is successful user is navigated to home screen.

-user clicks on browse button to upload input query image.

Scenario : To Pre-process the uploaded image.

-system performs noise removal of uploaded image.

-system performs the segmentation of uploaded image.

Scenario: To extract histogram values of pre-processed image.

-system extracts the color histogram values.



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-system extracts the edge histogram values.

Scenario: To display the result to user.

- the vector distance is calculated.

-the vector distance are sorted.

-image paths are retrieved and displayed to user as output.

4.2 Architecture:

First we store the images in disc, then color and edge histogram values areextracted and stored in database containing one table. Then user enters user name



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and password. Once he is authenticated he is directed to home screen. There he

can upload image. This uploaded image will be preprocessed by administrator then

color and edge histogram values are extracted which will be compared with

histogram values of images which are stored in database and sorted. Top 10 images

with least difference are displayed to the user.

4.2.1

Tier View:

It is the 3 tire architecture. Data access tire includes database containing features of

stored images. Business tire inclues preprocessing of input image,extracting

histogram values of stored database images and input image, comparing and sortingthose values. Then, presentation tire includes login screen and home screen to



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upload query image and display output.

4.3 Detailed design

4.3.1 Class Diagram

This is the initial class diagram. Now we have identified classes user, image,

preprocessing,histogram,similarity, result and database. User class has username

and password methods. Image class has upload method.preprocessing class has

segmentation and noise_removal methods. Histogram class has color_extract and

edge_extract methods. Similarity class has compare and sort methods. Result class

has retrieve and display methods which makes use of database class.

4.3.2 Sequence Diagram



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In this sequence diagram,objects are user,image,histogram values, database,

similarity and result. User uploads query image. From that color and edge histogram

values are extracted and they compared with histogram values of images which are

stored in database. Images with least differences are displayed to the user. Here we

display top 10 images.

4.3.3 Data Flow Diagram:



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First we store the images in disc, then color and edge histogram values are

extracted and stored in database containing one table. Then user enters user name

and password. Once he is authenticated he is directed to home screen. There he

can upload image. This uploaded image will be preprocessed by administrator then

color and edge histogram values are extracted which will be compared with

histogram values of images which are stored in database and sorted. Top 10 images

with least difference are displayed to the user.

Module Level Data flow diagram:



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Training set:

Login and Upload:

Preprocessing:

Color and Edge histogram extraction:



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Compare, Sort and Retrive Images

Use Case Diagram:

User login by entering username and password and clicks on login button. System

verifies the username and password and if authentication is successful user is

navigated to home screen, else an appropriate message is displayed to the user.



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When the user is navigated to home screen user clicks on browse button to upload

an input query image.

The uploaded image is pre-processed. Noise removal and image segmentation is

performed on uploaded image.



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The color histogram and edge histogram values are extracted from pre-processed

image .

The color and edge histogram values of pre-processed image is compared with the

color and edge histogram of images stored in database. Then the compared values



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are sorted. Image path of top ten images are retrieved from database.

System displays the images based on the image path to the user.

4.4 User Interface Design:

Login Screen


L o g i n C a n c e l

U s e r n a m e

P a s s w o r d

HomeScreen:



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B r o w s e

O K

C a n c e l

C : /



F e r r a r i . j p g

F e r r a r i . j p gI c o n 1 . j p gI c o n 2 . j p gI c o n 3 . j p g

I c o n 4 . j p gI c o n 5 . j p gI c o n 6 . j p gI c o n 7 . j p g

A l l . j p g f i l e s

After Search:





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Navigation Hierarchy:

Login screen

User enters username and password If authentication is successful user is navigated to home screen.

Else appropriate message is displayed to the user.

Home screen

User can upload input query image

User can view top ten output images.

4.5 Database Design:

Database is used to store the features of images stored in Disc.

Only one table is used which stores the following features:

Path to the image on Disc. Color histogram values.



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Edge histogram values.

Image Path Color histogram Edge histogram

Since there is only one table there exists no relationship hence E-R diagrams and

normalization are not applicable.

4.6 Logging:No log records are maintained for the following reasons:

No backup is maintained.

The user who knows the username and password will be able to login.

Simple database is maintained.

4.7 Exceptions: Exceptions may arise when

When the user clicks on search button without uploading the input query

image, an exception will be thrown with the message Upload image.

When the user enters wrong username and password, an exception is thrown

with the message Incorrect Username/Password .

If database connection failure occurs then an exception will be thrown with the

message Database Connection failure.

4.8 Localization: The user interface provided is in English, which can be provided in regional

language.

4.9 Dependencies:

Operating system Windows XP.



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End user characteristics the end user should know the username and

password in order to login.

If the software is integrated with web then there will be changes in design.

All the images that are to be displayed as the output should be stored in disc.

4.10 Deployment Diagram:

N o d e

U s e r I n t e r f a c e P r o c e s s i n g

D a t a b a s e

4.11 Design Decision: We adopted Iterative model as the requirements or design may be needed to

be revisited.

VB.Net is used to design front end.

Database used is SQL sever.

The software can be extended by integrating with web.

Processing of an image is done using Matlab 7.8

Algorithms: Color histogram algorithms



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Conventional color histogram.

Fuzzy color histogram.

Color correlogram.

The color-shape based method.

Algorithms CCH FCH Colorcorrelogram

Color-shapebased method

Averageretrieval score

80.12% 82.05% 69.48% 70.03%

Fuzzy Color Histogram:

Given a color image f, of size M by N pixels, characterized by the color c at location

(i,j), i.e., c = f(I,j), the color distribution (histogram) of the color set is given by,

Edge histogram algorithms

Canny edge detection algorithm.

Prewitts algorithm.

Roberts Cross algorithm.

Algorithm for Canny Edge Detection:1. Smoothing: Blurring of the image to remove noise.

2. Finding gradients: The edges should be marked where the gradients of the

image has large magnitudes.

3. Non-maximum suppression: Only local maxima should be marked as edges.

4. Double thresholding: Potential edges are determined by thresholding.

5. Edge tracking by hysteresis: Final edges are determined by suppressing all

edges that are not connected to a very certain (strong) edge.



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Vector distance calculation algorithm

Histogram Euclidean distance.

Histogram Intersection distances.

Histogram Quadratic distance.

The intersection of histograms h & g is given by

Where |h| and |g| gives the magnitude of each histogram, which is equal to the

number of samples. Colors not present in the users query image donot contribute to

the intersection distance. This reduces the contribution of background colors. Thesum is normalized by the histogram with fewest samples.

Algorithm for Preprocessing:INPUT: n segmented images, {I1, I2,.,In}

Where Ii is a record containing an image id and a blob descriptor vector bd.

OUTPUT: Set of n records, {R1,R2,.,Rn} containing the object identifiers for the

blobs.

FOR i1 = 1 to n DO

Ri1 = 0

END FOR object_id = 0

FOR i1 = 1 to n-1 DO

FOR i1 = 1 to size(Ii.bd)

first_time = true

FOR j2 = i1+1 TO n

IF Ii2.bdj2 is not matched yet THEN

IF similar (Ii1,bdj1,Ii2,bdj2,similarity_threshold,standard_deviation) THEN

IF first_time THEN

object_id = object_id+1

first_time = false

ENDIF

Ri1 = Ri1 U {object_id}

Ri2 = Ri2 U {object_id}

Mark Ii2.bdj2 as matched

ENDIF



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ENDIF

ENDFOR

Mark Ii1.bdj1 as matched if there was one match at least

ENDFOR

ENDFOR

Filter out unwanted matched objects.

5. References/Bibliography

[1]. www.seminarprojects.com/Thread-noise-reduction-using-modified-progressive-

switching-median-filter

[2].www.ieeepapers.com[3]. RajshreeDubey, RajnishChoubey,SanjeevDubey, Efficient Image Mining using

Multi Feature Content Based Image Retrieval System,IntJr of Advanced Computer

Engineering and Architecture Vol. 1, No. 1, June 2011

[4]. http ://ijacea.yolasite.com/resources/3.pdf
http://var/www/apps/conversion/current/tmp/scratch3273/www.seminarprojects.com/Thread-noise-reduction-using-modified-progressive-switching-median-filterhttp://var/www/apps/conversion/current/tmp/scratch3273/www.seminarprojects.com/Thread-noise-reduction-using-modified-progressive-switching-median-filterhttp://var/www/apps/conversion/current/tmp/scratch3273/www.ieeepapers.comhttp://ijacea.yolasite.com/resources/3.pdfhttp://ijacea.yolasite.com/resources/3.pdfhttp://var/www/apps/conversion/current/tmp/scratch3273/www.seminarprojects.com/Thread-noise-reduction-using-modified-progressive-switching-median-filterhttp://var/www/apps/conversion/current/tmp/scratch3273/www.seminarprojects.com/Thread-noise-reduction-using-modified-progressive-switching-median-filterhttp://var/www/apps/conversion/current/tmp/scratch3273/www.ieeepapers.comhttp://ijacea.yolasite.com/resources/3.pdfhttp://ijacea.yolasite.com/resources/3.pdf

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