human computer interaction using image proscessing

Human Computer Interaction Where Controlling Computer And Applications Using Image Processing and Voice Recognition

ISB&M School of Technology Pune Page 1

CHAPTER 1

1. INTRODUCTION

One of the important challenges in Human Computer Interactions is to develop more

intuitive and more natural interfaces. Computing environments presently are strongly tied to the

availability of a high resolution pointing device with a single, discrete two dimensional cursor.

Modern Graphical user interface (GUI), which is a current standard interface on personal

computers (PCs), is well-defined, and it provides an efficient interface for a user to use various

applications on a computer. GUIs (graphical user interfaces) combined with devices such as mice

and track pads are extremely effective at reducing the richness and variety of human

communication down to a single point.

While the utility of such devices in todays interfaces cannot be denied, there are many

users who find that the capability of GUI is rather limited when they try to do some tasks by

using gestures. There are opportunities to apply other kinds of sensors and techniques to enrich

the user experience of such users. For example, video cameras and computer vision techniques

may be used to capture many details of human shape and movement. The shape of the hand may

be analyzed over time to manipulate an onscreen object in a way analogous to the hands

manipulation of paper on a desk. Such an approach may lead to a faster, more natural, and more

fluid style of interaction for certain tasks.

Ubiquitous computing is devoted to changing the relationship between humans and the

computers with which we interact, towards allowing computers to become invisible and recede

into the periphery of peoples lives.

Our project, Human Computer Interaction Where Controlling Computer and

Applications Using Image Processing and Voice recognition is an attempt in ubiquitous

computing. Here, we will be using colored tapes on our fingers. One of the tapes will be used for

controlling cursor movement while the relative distance between the two colored tapes will be

used for click events of the mouse. And center color tape we are using for gestures Also, we will

be enriching our system with voice recognition capability to perform basic actions like

shutdown, search and surfing thus, the system will provide a new experience for users in

interacting with the computer



CHAPTER 2

1. PROBLEM DEFINITION

The project that we are trying to develop will completely change the way people are going to use

the computer system. Presently, we are using the camera and mice to detect the hand gesture and

voice commands to control the computer and its applications.

Also this would lead to a new era of Human Computer Interaction

(HCI) where no physical contact with the device is required. And it can be used in many media

application as well as new product designs. It can be used in advertisement industry for the

natural user interface so that user can be connected with the advertisers more effectively.



CHAPTER 3

3. LITERATURE SURVEY

3.1. RELATED WORK

A lot of research is being done in the fields of Human Computer Interaction (HCI) and

Robotics. Researchers have tried to control mouse movement using video devices for HCI.

However, all of them used different methods to make mouse cursor movement and clicking

events.

3.1.1] A Method for Controlling Mouse Movement using a Real-Time Camera. [1].

Hojoon Park

One approach, by Hojoon Park [1] used index finger for cursor movement and angle between

index finger and thumb for clicking events.

Working:

Hojoon Park [1] used index finger for cursor movement. In his system Hojon park [1] used index

finger movement to move mouse movements on the computer. In which he used the effective

algorithm to detect the fingers. Hojoon Park [1] showed that we can use angle between finger

and thumb for clicking events.

To recognize that a finger is inside of the palm area or not, He used a convex hull algorithm. The

convex hull algorithm is used to solve the problem of finding the biggest polygon including all

vertices. Using this feature of this algorithm, He can detect finger tips on the hand. He used this

algorithm to recognize if a finger is folded or not. To recognize those states, He multiplied 2

times (He got this number through multiple trials) to the hand radius value and check the

distance between the center and a pixel which is in convex hull set. If the distance is longer than

the radius of the hand, then a finger is spread. In addition, if two or more interesting points

existed in the result, then He regarded the longest vertex as the index finger and the hand gesture

is clicked when the number of the result vertex is two or more.

Advantages:

He developed a system to control the mouse cursor using a real-time camera. He implemented all

mouse tasks such as left and right clicking, double clicking, and scrolling. This system is based

on computer vision algorithms and can do all mouse tasks



The Hojoon Park [1] used Simple Effective System which is less complicated. And the

Algorithms are simple and take less times. The system is easy to use can be used to control small

applications.

Limitations:

In this project, the problem was when the finger shook at lot. Since He used real-time video, the

Illumination changes every frame. Hence the position of the hand changes every frame. Thus, the

fingertip position detected by convex hull algorithm is also changed. Then the mouse cursor

pointer shakes fast. To fix this problem, we added a code that the cursor does not move if the

difference of the previous and the current finger tips position is within 5 pixels. This constraint

worked well but it makes it difficult to control the mouse cursor sensitively. Another problem by

illumination issue is segmentation of the background for extracting the hand shape. Since the

hand reflects all light sources, the hand color is changed according to the place. If hand shape is

not good then our algorithm cannot work well because our algorithm assumes the hand shape is

well segmented. If the hand shape is not good then we cannot estimate the length of radius of the

hand.

3.1.2] Virtual mouse vision based interface, January 2004 [3]

Robertson P., Laddaga R., Van Kleek M.[3]

Working

There solution was to develop a virtual mouse that enables users to control the kiosk with hand

signs and movements. The kiosk has a standard visual user interface, with arrowcursor to

indicate pointer movement. The user walks up to the kiosk. People approaching the kiosk are

tracked by a robotic head called IGOR (Intelligent Gaze Oriented Robot)[3] described below.

When the user makes a recognized hand sign the kiosk allows movement of the hand to move the

mouse pointer on the kiosk display. Separate hand signs allow for clicking of the mouse buttons

for making selections on the kiosk display.

Note that the arrow pointer is the only feedback the user gets as to where the user is pointing.

The user can use that feedback, adjusting to imperfections in tracking, without the distraction of

a distinct and dierent other signal.



Advantages:

In the face tracking state the face recognition is a higher priority than the sign recognition. In the

sign tracking state the optical ow is given the higher priority. In this way good responsiveness

is achieved in both user tracking and gesture tracking.

Optical ow allows smooth tracking of the hand gestures. It is robust because no recognition is

required to achieve mouse motion, and it also provides smooth motion estimates.

Limitations:

The System was not Capable for complex operations and It cannot control high end system and

applications which lowers its scope.

3.1.3] Real-time Hand Tracking and Finger Tracking for Interaction [4]

Shahzad Malik CSC2503F Project Report, December 18, 2003[4]

Working:

In this system, which is primarily based on the single hand tracker presented in [Segen99].[4]

The system can extract the 3D position and 2D orientation of the index finger for each hand, and

when present the pose of the thumb as well. In interactive applications a single pointing gesture

could then be used for selection operations while both the thumb and index finger could be used

together for pinching gestures [4] in order to grasp and manipulate virtual objects.

Advantages:

This project presents the implementation and analysis of a real-time stereo vision hand tracking

system that can be used for interaction purposes. The system uses two low-cost web cameras

mounted above the work area and facing downward. In real-time, the system can track the 3D

position and 2D orientation of the thumb and index finger of each hand without the use of special

markers or gloves, resulting in up to 8 degrees of freedom for each hand.

Limitations:

Misclassification problem occurs when two hands appear close together in the captured images.

This results in a single large region being segmented by the background subtraction and skin

detection phases. Therefore the contour detector interprets the two hands as a single hand and

thus the fingers are labelled as a right hand.



3.1.4] Portable Vision-Based HCI - A Real-time Hand Mouse System on Handheld Devices

[9]

Chu-Feng Lien,[9]

Working:

They assume the popularity of equipping a low-resolution camera on those handheld devices. By

adopting their embedded cameras, the system can detect a user's hand motion in real-time, and

results in the autonomous manipulation of corresponding programs on the device. To run the

vision-based HCI system on the handheld devices, computing power of frame processing is a

critical concern. They used Adaboost [9] method proposed by Viola and Jones seem to be a

choice for the project, they did not have a good result with a low-resolution camera. Instead of

using gesture recognition methods, then they use Motion History Images. By grabbing and

processing the image on pixels directly, they gain an efficiency way of computing as a result.

Advantages:

There system can find Projected screen and that is so much useful for the Projection purpose

The system is more convenient because it can be used on all the portable devices. The Low

resolution Cameras Can be supported.

Limitations:

High error rate on fast moving motion (this issue can be improved by increasing the framing

rate) will result in high false positive detection. If the speaker walks around the projected area

continuously, the system can adapt to this behaviour and performs well, but if the speaker

suddenly stops in the middle of the screen, system will result in a false alarm. If the

environmental lights are changing or a shadow is projected within the scope of the camera, the

system will be misleading to a difference result. If the edge color of projected screen is similar to

its neighbour objects. The screen will not be well detected.



CHAPTER 4

2. SOFTWARE REQUIREMENT SPECIFICATION

4.1 INTRODUCTION

As computer technology continues to develop, people have smaller and smaller electronic

devices and want to use them ubiquitously. There is a need for new interfaces designed

specifically for use with these smaller devices. Increasingly we are recognizing the importance of

human computing interaction (HCI), and in particular vision-based gesture and object

recognition. Simple interfaces already exist, such as embedded keyboard, folder-keyboard and

mini-keyboard. However, these interfaces need some amount of space to use and cannot be used

while moving. Touch screens are also a good control interface and nowadays it is used globally

in many applications. However, touch screens cannot be applied to desktop systems because of

cost and other hardware limitations. By applying vision technology and controlling the mouse by

natural hand gestures, we can reduce the work space required. In this paper, we propose a novel

approach that uses a video device to control the mouse system. This mouse system can control

all mouse tasks, such as clicking (right and left), doubleclicking and scrolling. We employ

several image processing algorithms to implement this.

Our project, Human Computer Interaction Where Controlling Computer and

Applications Using Image Processing and Voice recognition is an attempt in ubiquitous

computing. Here, we will be using colored tapes on our fingers. One of the tapes will be used for

controlling cursor movement while the relative distance between the two colored tapes will be

used for click events of the mouse. And center color tape we are using for gestures Also, we will

be enriching our system with voice recognition capability to perform basic actions like

shutdown, search and surfing thus, the system will provide a new experience for users in

interacting with the computer



4.2 SYSTEM FEATURES

We introduce an effective way of Human Computer Interaction where the proposed system has following

modules

Controlling the Mouse Movements through Hand Movements.

Controlling Media Player Options through Hand Gestures.

Controlling Application like Games, Maps, Image Viewer.

Perform Basic Operations Through Voice Recognition like Search, Shutdown, Restart, etc.

Here we are using Users hand gesture through webcam and process it using image processing techniques

and we are using voice recognition to make it more natural interaction with the system.

4.3 EXTERNAL INTERFACE REQUIREMENT

There are many types of interfaces such as User Interface, Software Interface and Hardware

Interface.

User Interfaces

The user interface for the software shall be compatible to windows operating system.

Where user interface is developed using java media framework and Camera should be

compatible with the system.

Software Interfaces

The system utilizes JDK(1.6) framework which provides it with the necessary

components to build system components and objects, plus providing the system with the required

data access components.

Communication Interfaces

Graphical user interface is most convenience way to do the interaction with the system.

So in our proposed system we have design and developed GUI to interact with the system by

using Java Swing classes.



4.4 FUNCTIONAL REQUIREMENT

Class Function Requirement

Color_Tape_Detection Color_Extraction HSV Color Detection

Algorithm

blob_Detection Histogram-based Skin

Classifier

Cursor_Move Mapping Cursor Control

Algorithm, Weighted Speed

Cursor Control Algorithm

Voice_Recognition Voice_Recognise Dynamic Time Wrapping

Algorithm

Table 1

4.5 NON FUNCTIONAL REQUIREMENT

Performance Requirements

High Speed: System should process voice messages in parallel for various users to give

quick response then system must wait for process completion.

Better component design: To get better performance at peak time

Security Requirements

Secure access of confidential data (users details). Information security means protecting

information and information systems from unauthorized access, use, disclosure,

disruption, modification or destruction.



1. User password must be stored in encrypted form for the security reason

2. All the user details shall be accessible to only high authority persons.

3. Access will be controlled with usernames and passwords.

4. Voice database must secure.

Extensibility

Extensibility allows adding new component to the system, replaces the existing ones.

This is done without affecting the components that are in their original places. Flexible

service based architecture will be highly desirable for future extension

Scalability

The solution should be able to accommodate high number of customers and brokers. Both

may be geographically distributed.

Compatibility

Compatibility is the measure with which user can extend the one type of application with

another.

Serviceability

In software engineering and hardware engineering, serviceability also known as

supportability, is one of the aspects (from IBM's RASU (Reliability, Availability,

Serviceability, and Usability). It refers to the ability of technical support personnel to

install, configure, and monitor computer products, identify exceptions or faults, debug or

isolate faults to root cause analysis, and provide hardware or software maintenance in

pursuit of solving a problem and restoring the product into service.



4.6 ANALYSIS MODEL

4.6.1 DFD level 0

Figure 1

2.1.1 DFD level 1

Figure 2



2.1.2 DFD level 2

Figure 3

4.6.4 Class Diagram:

Figure 4



4.6.5 ER Diagram

Figure 5

4.7 SPECIFIC REQUIREMENTS

Project System will be Windows based supporting versions, Windows XP onwards. The

minimum configuration required for system is:

4.7.1 Hardware:

CPU : 2.4 GHZ (intel or AMD)

Hard disk : 80 GB

RAM : 1 GB.

Camera : 2 Megapixel (minimum) and 30 FPS (Frame per Seconds)



4.7.2 Software:

JDK 1.6

NetBeans IDE

Java Advance Imaging

Java Media Framework

SAPI

4.8 SYSTEM IMPLEMENTATION PLAN

Sr. No.

Planning

Start Date

Completion Date

1. Topic search and Finalization 4th

July 2014 25th

July 2014

2. Literature Survey

8th

August 2014

21st August 2014

3. Objective And Planning

22nd

August 2014

28th

August 2014

4. Software ,Hardware Requirements & Budget

29th

August 2014

5th

September 2014

5. DFD , UML diagrams

8th

September 2014

10th

September 2014

6. Algorithm Design

10th

September 2014

13th

September 2014

7. Algorithm Analysis

15th

September 2014

21st October 2014

8. Preliminary Report 22nd

October 2014 29th

September 2014

9. Study of Project related Technology 1st November 2014 3rd January 2014

10. Coding and Implementation of Project. 5th

January 2014 20th

February 2014

11. Working Model and Testing. 24th

February 2014 15th

March 2014

12. Tested and Executable Project Model. 16th

March 2014 29th

March 2014

13. Final Report and Deployment of Project. 2nd

April 2014 20th

April 2014

Table 2



4.9 BUDGET

Note: OSS (Open Source Software)

Sr. No. Product Quantity Cost 1

1. Computer 1 20000

2. WebCamera 1 2500

3. Windows XP 1 3000

4. Net BeansIDE 7.0.1 1 OSS

5. JDK 1.6 1 OSS

6. STAR UML 1 OSS

Total 27500

Table 3



CHATPER -5

5. SYSTEM DESIGN

5.1 SYSTEM ARCHITECTURE:

In our Proposed System We Have 3 Main Blocks.

1. Functional Block

2. Voice Recognition Module

3. Technology

Functional Block Takes a Camera feed as a input and process it through image processing

algorithms as well as voice recognition module takes a voice commands as a input and process

those voice commands both the functional block and voice recognition module performs the

processing using technology block.

Figure 6



4.2 UML DIAGRAMS

4.2.1 USE-CASE Diagram

Use case Diagram

Use case diagrams are one of the five diagrams in the UML for modelling the dynamic

aspects of systems (activity diagrams, state chart diagrams, sequence diagrams, and collaboration

diagrams are four other kinds of diagrams in the UML for modelling the dynamic aspects of

systems). Use case diagrams are central to modelling the behaviour of a system, a subsystem, or

a class. Each one shows a set of use cases and actors and their relationships. You apply use case

diagrams to model the use case view of a system. For the most part, this involves modelling the

context of a system, subsystem, or class, or modelling the requirements of the behaviour of these

elements. Use case diagrams are important for visualizing, specifying, and documenting the

behaviour of an element. They make systems, subsystems, and classes approachable and

understandable by presenting an outside view of how those elements may be used in context. Use

case diagrams are also important for testing executable systems through forward engineering and

for comprehending executable systems through reverse engineering.

Figure 7



4.2.2 Activity Diagram

A class diagram shows a set of classes, interfaces, and collaborations and their

relationships. These diagrams are the most common diagram found in modeling object-oriented

systems. Class diagrams address the static design view of a system. Class diagrams that include

active classes address the static process view of a system

Figure 8



4.2.3 Sequence Diagram

A statechart diagram shows a state machine, consisting of states, transitions, events, and

activities. You use statechart diagrams to illustrate the dynamic view of a system. They are

especially important in modelling the behaviour of an interface, class, or collaboration.

Statechart diagrams emphasize the event-ordered behaviour of an object, which is especially

useful in modelling reactive systems.

Figure 9



CHAPTER -6

5. TECHNICAL SPECIFICATION

6.1 TECHNOLOGY USED IN PROJECT

6.1.1 JDK 1.6

JDK (Java Development Kit) is a free software development package from Sun

Microsystems that implements the basic set of tools needed to write, test and debug Java

applications and applets.

6.1.2 Java Advance Imaging

The Java Advanced Imaging API extends the Java 2 platform by allowing sophisticated,

high-performance image processing to be incorporated into Java applets and applications. It is a

set of classes providing imaging functionality beyond that of Java 2D and the Java Foundation

classes, though it is designed for compatibility with those APIs. This API implements a set of

core image processing capabilities including image tiling, regions of interest, deferred execution

and a set of core image processing operators, including many common point, area, and frequency

domain operators.

The Java Advanced Imaging API is intended to meet the needs of technical (medical,

seismological, remote sensing, etc.) as well as commercial imaging (such as document

production and photography). The API can benefit all Java developers who want to incorporate

imaging into their Java applets and applications.

Features of JAI

Rich set of functionality for digital imaging.

High level of extensibility to allow arbitrary processing capabilities.

Support for a wide variety of data types.

Deferred Execution.

Remote Imaging and truly distributed imaging.

Allow multiple implementations with different trade-offs of memory usage, operator

optimization, and hardware acceleration.



6.1.3 Java Media Framework

JMF is a framework for handling streaming media in Java programs. JMF is an optional

package of Java 2 standard platform. JMF provides a unified architecture and messaging protocol

for managing the acquisition, processing and delivery of time-based media. JMF enables Java

programs to

Present ( playback) multimedia contents,

Capture audio through microphone and video through Camera,

Do real-time streaming of media over the Internet,

Process media ( such as changing media format, adding special effects),

Store media into a file.

Features of JMF

JMF supports many popular media formats such as JPEG, MPEG-1, MPEG-2,

QuickTime, AVI, WAV, MP3, GSM, G723, H263, and MIDI. JMF supports popular

media access protocols such as file, HTTP, HTTPS, FTP, RTP, and RTSP.

JMF uses a well-defined event reporting mechanism that follows the Observer design

pattern. JMF uses the Factory design pattern that simplifies the creation of JMF

objects. The JMF support the reception and transmission of media streams using Real-

time Transport Protocol (RTP) and JMF supports management of RTP sessions.

JMF scales across different media data types, protocols and delivery mechanisms. JMF

provides a plug-in architecture that allows JMF to be customized and extended.

Technology providers can extend JMF to support additional media formats. High

performance custom implementation of media players, or codecs possibly using hardware

accelerators can be defined and integrated with the JMF.

6.2 ADVANTAGE

Hand as an acceptable tool to control a computer cursor.

It will enable people to interact with computers without physical contact.

It Will give more natural way to interact with computer

Benefits from Finger Mouse & Voice in other applications as commercials and

interactive advertisements.



6.3 APPLICATIONS

Our system can be used for the human computer interaction

It can also used to control computer

It can be used to control various software applications

Our system can be used to control power point presentation

Our system can be used to control or play games



CHAPTER 7

7. CONCLUSION

The product that we are trying to develop will improve the way people are going to use the

computer system. Presently, the webcam, microphone and mouse are an integral part of the

computer system. Our product which uses only two of them i.e. webcam and microphone would

may eliminate the mouse. Also this would lead to a new era of Human Computer Interaction

(HCI) where no physical contact with the device is required.

This technology can be further enhanced for use in robotics, gaming and developing

systems which could understand human behaviour based on their way of interaction.



CHAPTER -8

REFERENCES

1. Hojoon Park, A Method for Controlling Mouse Movement using a Real-Time Camera.

2. Hart Lambur, Blake Shaw, Gesture Recognition, CS4731 Project, December 21,2004.

3. Robertson P., Laddaga R., Van Kleek M., Virtual mouse vision based interface, January

2004.

4. Shahzad Malik, Real-time Hand Tracking and Finger Tracking for Interaction CSC2503F

Project Report, December 18, 2003

5. A. Erdem, E. Yardimci, Y. Atalay, V. Cetin, Computer vision based mouse, A. E.

Acoustics, Speech, and Signal Processing, 2002. Proceedings. (ICASS). IEEE

International Conference

6. Y. Sato, Y. Kobayashi, H. Koike. Fast tracking of hands and fingertips in infrared images

for augmented desk interface. In Proceedings of IEEE, International Conference on

Automatic Face and Gesture Recognition (FG), 2000. pp. 462-467.

7. J. Segen, S. Kumar. Shadow gestures: 3D hand pose estimation using a single camera. In

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR),

1999. Vol. 1, pp. 479-485.

8. James M. Rehg, Takeo Kanade, DigitEyes: Vision-Based Hand Tracking for Human-

Computer Interaction, Proc. of the IEEE Workshop on Motion of Non-Rigid and

Articulated Objects, Austin, Texas, November 1994, pages 16-22.

9. Chu-Feng Lien, Portable Vision-Based HCI - A Real-time Hand Mouse System on

Handheld Devices.

10. Asanterabi Malima, Erol Ozgur, and Mujdat Cetin, A Fast Algorithm for Vision-Based

Hand Gesture Recognition for Robot Control.

11. Stephen Tu, HSV Color Detection Algorithm, White Paper.



ANNEXURE

ANNEXURE A:

Project Analysis of Algorithm Design

Project Analysis:

Let G be a closed graph that represents our system of mouse simulation and application

control; such that G = {E, V} where E represents the set of edges; E = {e0, e1, e2, e3 ....., e14}

and V is a set of vertices; V = {v0, v1, v2, v3....., v10}.

In the graphical representation of the system, vertices in the set V represent the modules

which are connected through directed edges in the set E representing the input/output of

modules. We define the vertices as,

Figure 10



VERTEX MODULE

v0 Initialize

v1 Image Capture

v2 Voice Capture

v3 Finger Tape Colour Detect

v4 Voice Recognition

v5 Event Detect

v6 Mouse Operation Events

v7 OperationGesture events

v8 Key Strokes

v9 Voice Operations

v10 Aggregation

Table 4

We define the edges as,

EDGE INPUT/OUTPUT

e0 Call to camera

e1 Call to microphone

e2 Image frames

e3 Voice records

e4 Pixel position

e5 Distance between tapes and wait time

e6 Voice command

e7 Operation Gesture Detected

e8 Gesture For Key stroke

e9 Voice Command



e10 Call to aggregation module

e11 -------------------------------

e12 -------------------------------

e13 -------------------------------

e14 Iteration Call

Table 5

Let fe be a rule of E into V such that for given edge; it returns vertices. fe (E) | V.

Thus, for our system,

fe (e0) = {v1}.......v1 is called using e0 to capture image.

fe (e1) = {v2}.......v2 is called using e1 to capture voice.

fe (e2) = {v3}.......frames are passed to v3 using e2 for detection.

fe (e3) = {v4}......voice data is passed to v4 using e3 for recognition.

fe (e4) = {v6}......position is passed to v6 using e4 for cursor movement.

fe (e5) = {v5}......distance between coloured tapes or wait time is passed to v5 using e5 for event

detection.

fe (e6) = {v5}......voice command is passed to v5 using e6 for event detection.

fe (e7) = {v7}.......v7 is called for Operation Gesture event using e7.

fe (e8) = {v8}.......v8 is called for key stroke event using e8.

fe (e9) = {v9}.......v9 is called for Voice Operation event using e9.

fe (e10) ={v10}

fe (e11) = {v10}

fe (e12) = {v10} e10, e11, e12, e13 aggregate at v10.

fe (e13) = {v10}

fe (e14) = {v0}......v0 is called again to iterate using e14.



Overloading on {e4, e5, e6}

The mouse movement and events are overloaded using pixel position (e4), distance

between coloured tapes and wait time (e5), and voice commands (e6).

COMPLEXITY

Our system involves three main modules

Image recognition and analysis (v3)

Voice recognition and analysis (v4)

Event selection (v5)

For a standard image recognition and analysis module/system, the complexity is logarithmic

and it is given as

___________________________________________________________________________

O( mn + (mn/k2 )log(mn/k

2 ))

where (m x n) are width and height of image and (k x k) is segmentation blob respectively.

___________________________________________________________________________

Also, for a standard voice recognition module/system, the complexity is quadratic in

nature and it is given as

___________________________________________________________________________

O( n2

v)

where v is number of words in dictionary and n is length of sequence respectively.

___________________________________________________________________________

And the complexity of the event selection module depends on the number of events involved in

it. Thus for n events, the complexity is

___________________________________________________________________________

O(n).

___________________________________________________________________________

Thus, total complexity of our system is given as



___________________________________________________________________________

Total complexity = Image recognition complexity + Voice recognition complexity + Event

selection complexity

= O( mn + (mn/k2 )log(mn/k

2 )) + O( n

2 v) + O(n)

___________________________________________________________________________

Hence, Overall complexity of our system nearly comes out to be O(n2).

P Class Problem

A problem is in P class if it is solvable in polynomial time by a deterministic algorithm.

For our system, algorithms are deterministic and the overall complexity is O(n2) which shows

that it is in P class.



ANNEXURE B:

Project Quality and Reliability Testing of Project Design

Testing is the process of evaluating a system or its component(s) with the intent to find that

whether it satisfies the specified requirements or not. This activity results in the actual, expected

and difference between their results. In simple words testing is executing a system in order to

identify any gaps, errors or missing requirements in contrary to the actual desire or requirements.

There are different types of testing which may be used to test a Software during SDLC.

Manual testing: This type includes the testing of the Software manually i.e. without using

any automated tool or any script.

Automation testing: Automation testing which is also known as Test Automation, is when

the tester writes scripts and uses another software to test the software.

There are different methods which can be use for Software testing.

Black Box Testing: The technique of testing without having any knowledge of the interior

workings of the application is Black Box testing.

White Box Testing: White box testing is the detailed investigation of internal logic and

structure of the code. White box testing is also called glass testing or open box testing.

Grey Box Testing: Grey Box testing is a technique to test the application with limited

knowledge of the internal workings of an application.



ANNEXURE C

ABBREVIATIONS

A

AMD: Advanced Micro Devices

API: Application Programming Interface

AVI: Audio Video Interleave

C

CPU: Central Processing Unit

D

DFD: Data Flow Diagram

E

ER Diagram: Entity Relationship Diagram

F

FPS: Frame per Seconds

FTP:File Transfer Protocol

G

GB: GigaByte

GUI : Graphical user Interface

GSM: Global System for Mobile

H

HCI: Human Computer Interaction

HSV: Hue Saturation Value

HTTP: Hyper Text Transfer Protocol

HTTPS:Hyper Text Transfer Protocol Secure

I

IGOR: Intelligent Gaze Oriented Robot

IBM: International Business Machines

IDE: Integrated Development Environment



J

JDK(1.6): JAVA Development Kit 1.6

JAI: Java Advance Imaging

JMF: Java Media Framework

JPEG: Joint Photographic Experts Group

M

MPEG-1: Motion Picture Experts Group 1

MPEG-2: Motion Picture Experts Group 2

MP3: MPEG-2 Audio Layer III

MIDI: Musical Instrument Digital Interface

O

OSS:Open Source Software

P

PC : Personal Computer

R

RASU: (Reliability, Availability, Serviceability, and Usability)

RAM: Random Access Memory

RTP: Real-Time Transport Protocol

RTSP: Real-Time Streaming Protocol.

S

SAPI: Speech Application Programming Interface

SDLC: Software Development Life Cycle

U

UML: Unified Modelling Language

W

WAV: WAVEform audio format

Numbers

3D: Three Dimensional

2D: Two Dimensional

human computer interaction using image proscessing

Documents