Thursday, August 14, 2014

MATLAB Projects List, Matlab IEEE Projects Pune

MATLAB Projects List:

1. Audio Forensic marking technology using DWT-SVD
2. Non-blind Watermarking scheme for color images
in RGB space using DWT-SVD
3. Efficient Compression of Encrypted Grayscale Images using Slepian-Wolf coding.
4. Removal of High Density Salt and Pepper Noise
Through Modified Decision Based Unsymmetric
Trimmed Median Filter
5. Adaptive MMSE Rake Receiver for WCDMA
6. DWDM Effects of Single Model Optical Fiber in Radio over Fiber System
7. Convolutional Codes in Two-Way Relay Networks with Physical-Layer Network Coding
8. Cooperative and Constrained MIMO Communications in Wireless Ad Hoc/Sensor Networks
9. Efficiency of the LDPC Codes in the Reduction
of PAPR in Comparison to Turbo Codes and
Concatenated Turbo-Reed Solomon Codes in a
MIMO-OFDM System
10. New low complexity DCT based video compression method
11. Enhancement of Color Images by Scaling the Coefficients DCT
12. Performance Assessment of OFDM-based and
OWDM-based Radio-over-Fiber Systems in the
Presence of Phase Noise
13. BER of Adaptive Arrays in AWGN Channel
14. Bi-2DPCA: A Fast Face Coding Method for
Recognition
15. Neural Network-Based Face Detection
16. Novel Speech Signal Processing Algorithms for
High-Accuracy Classification of Parkinson’s Disease
17. On Convolution Model for Ultrasound Echo Signal Processing (*provided samples are available)
18. Automatic Segmentation of Digital Images Applied in Cardiac Medical Images
19. A Novel Haar Wavelet-Based BPSK OFDM System
Robust to Spectral Null Channels and with Reduced
PAPR
20. Diversity Gain for MIMO Neyman–Pearson
Signal Detection


Wednesday, August 13, 2014

MATLAB Project guidance: AIRPORT SECURITY USING RFID AND IRIS RECOGNITION

CONTACT: 9764259156

AIRPORT SECURITY USING RFID AND IRIS RECOGNITION

INTRODUCTION:

In today’s information age it is not difficult to collect data about an individual and use that information to exercise control over the individual. Individuals generally do not want others to have personal information about them unless they decide to reveal it. With the rapid development of technology, it is more difficult to maintain the levels of privacy citizens knew in the past. In this context, data security has become an inevitable feature. Conventional methods of identification based on possession of ID cards or exclusive knowledge like Social security number or a password are not altogether reliable. ID cards can be almost lost, forged or misplaced: passwords can be forgotten. Such that an unauthorized user may be able to break into an account with little effort. So it is need to ensure denial of access to classified data by unauthorized persons. Biometric technology has now become a viable alternative to traditional identification systems because of its tremendous accuracy and speed. Biometric system automatically verifies or recognizes the identity of a living person based on physiological or behavioral characteristics. Since the persons to be identified should be physically present at the point of identification, biometric techniques gives high security for the sensitive  information stored in mainframes or to avoid fraudulent use of ATMs.
   Biometric products are used for automated recognition of individuals based on their behavioral and biological characteristics. Iris recognition biometric products recognize individuals based on their iris images more specifically the distinctive patterns in the irises created by various structures, such as crypts, furrows, frills, ridges, ligaments, freckles, coronas, and collarettes. Other common biometric products use fingerprint features, facial images, hand geometry, characteristics of handwritten signatures, and voice recordings to recognize individuals. Fingerprint recognition, known for its low error rates, typically requires an individual to place their finger on a sensor to be recognized. The error rates for facial recognition technologies are typically higher than for fingerprint technologies, but facial recognition is often preferred because its operation is non-contact. Iris recognition combines the advantages of fingerprinting (low error rates) and facial recognition (non-contact operation) and as such may prove valuable for many criminal justice and border control applications.
 This project basically aims at designing an iris matching software system. Firstly, image preprocessing is performed followed by extracting the iris portion of the eye image which is called Localization. The extracted iris part is then normalized using daugman’s rubbersheet model, and Iris Code is constructed. Finally two Iris Codes are compared to find Hamming Distance, which is fractional measure of the dissimilarity. Experimental image results show that unique codes can be generated for every eye image and Hamming Distance between any two different iris code has maximum value.
With the increasing demand of enhanced security in our daily lives, reliable personal identification through biometrics is currently an active topic in the literature of pattern recognition. Nowadays many automatic security systems based on iris recognition have been deployed worldwide for border control, restricted access Iris recognition is based on the most mathematically unique biometric - the iris of the eye. The human iris is absolutely unique, even between twins or an individual's right and left eyes. The iris itself is stable throughout a person's life (approximately from the age of one). The physical characteristics of the iris do not change with age. One key tool in this area is the use of biometrics. Humans have always identified each other by recognizing faces, voices or some other physical characteristic. Personal recognition or identification by a witness is also entrenched in our law and commercial structures. Now the use of biometric technologies is providing a means to positively identify or authenticate large numbers of people without having to primarily rely on human to human identification.



Innovations
Conventional security systems are based on face recognition, RF-ID, Finger Print detection which are not 100% foolproof. Facial features can be modified, RF-ID cards can be stolen and misused, cuts and burns on the finger will cause failure of finger print identification. Iris detection has much better accuracy and is a step ahead of the above mentioned systems. The Iris Code generated is unique for every individual even for the identical twins.

 Accuracy is the parameter which differentiates Iris recognition technology from rest of the Biometric techniques such as Face, Voice, Fingerprint, Retina, Signature etc. Iris recognition is more accurate, stable and scalable. Hence iris recognition is popularly used in many applications. 

                           
Here we are developing 2 level airport security ,In this system the 1st technique is the RFID and the 2nd technique is IRIS recognition.

RFID: we are using a passive RFID system .In this case the user has to show the card to the reader. The RFID reader reads the card no and sends it to the µC via wiegand protocol .The µC will then go in its database and check for the RFID of the user .If match occurs then the user is asked to go for the next verification stage of  IRIS.



SOFTWARE OVERVIEW

 PROGRAMMING USING  ‘C’
 PROGRAMMING AT BASE STATION USING MATLAB (7.0)

ADVANTAGES

EFFICIENT WAY  FOR SECURITY CHECK
LESS TIME DELAYS
QUICK RESPONSE TIME
 FULLY AUTOMATE SYSTEROBUST SYSTEM, LOW POWER REQUIREMENT
       

 CONTACT: 9764259156






Monday, August 11, 2014

MATLAB DIP IEEE Based Project Guidance in Pune for ME & BE.

CONTACT: 9764259156

Some latest IEEE 2013 , 2014 DIP MATLAB  projects for this month are listed below:

More projects will be added to this list

1. Audio Forensic marking technology using DWT-SVD
2. Non-blind Watermarking scheme for color images
in RGB space using DWT-SVD
3. Efficient Compression of Encrypted Grayscale Images using Slepian-Wolf coding.
4. Removal of High Density Salt and Pepper Noise
Through Modified Decision Based Unsymmetric
Trimmed Median Filter
5. Adaptive MMSE Rake Receiver for WCDMA
6. DWDM Effects of Single Model Optical Fiber in Radio over Fiber System
7. Convolutional Codes in Two-Way Relay Networks with Physical-Layer Network Coding
8. Cooperative and Constrained MIMO Communications in Wireless Ad Hoc/Sensor Networks
9. Efficiency of the LDPC Codes in the Reduction
of PAPR in Comparison to Turbo Codes and
Concatenated Turbo-Reed Solomon Codes in a
MIMO-OFDM System
10. New low complexity DCT based video compression method
11. Enhancement of Color Images by Scaling the Coefficients DCT 
12. Performance Assessment of OFDM-based and
OWDM-based Radio-over-Fiber Systems in the
Presence of Phase Noise
13. BER of Adaptive Arrays in AWGN Channel
14. Bi-2DPCA: A Fast Face Coding Method for
Recognition
15. Neural Network-Based Face Detection
16. Novel Speech Signal Processing Algorithms for
High-Accuracy Classification of Parkinson’s Disease
17. On Convolution Model for Ultrasound Echo Signal Processing (*provided samples are available)
18. Automatic Segmentation of Digital Images Applied in Cardiac Medical Images
19. A Novel Haar Wavelet-Based BPSK OFDM System
Robust to Spectral Null Channels and with Reduced
PAPR
20. Diversity Gain for MIMO Neyman–Pearson
Signal Detection


Detail Synopsis for MATLAB Projects are as below:

Novel Speech Signal Processing Algorithms for

High-Accuracy Classification of Parkinson’s Disease

There has been considerable recent research into theconnection between Parkinson’s disease (PD) and speech impairment.Recently, awide range of speech signal processing algorithms(dysphonia measures) aiming to predict PD symptom severity usingspeech signals have been introduced. In this paper, we test howaccurately these novel algorithms can be used to discriminate PDsubjects fromhealthy controls. In total, we compute 132 dysphoniameasures fromsustained vowels. Then,we select four parsimonioussubsets of these dysphonia measures using four feature selectionalgorithms, and map these feature subsets to a binary classification
response using two statistical classifiers: random forests andsupport vector machines.We use an existing database consisting of263 samples from 43 subjects, and demonstrate that these new dysphoniameasures can outperform state-of-the-art results, reachingalmost 99% overall classification accuracy using only ten dysphoniafeatures.We find that some of the recently proposed dysphoniameasures complement existing algorithms in maximizing the abilityof the classifiers to discriminate healthy controls from PD subjects.We see these results as an important step toward noninvasivediagnostic decision support in PD.

NEUROLOGICAL disorders affect people’s lives at an epidemicrate worldwide. Parkinson’s disease (PD) is oneof the most common neurodegenerative disorders with an incidencerate of approximately 20/100 000 and a prevalence rate exceeding 100/100 000. Moreover, these statisticsmight underestimate the problem because PD diagnosis is complicated. Given that age is the single most importantfactor for PD and the fact that the population is growingolder, these figures could further increase in the not too distant future.


Software: MATLAB 2010a



Sunday, November 3, 2013

IEEE Matlab Projects in Pune Steganography

LSB Steganography 
                                                Ensuring the confidentiality is one of the biggest challenges while transferring the data. Various methods are used for providing security. One of the methods is the steganography. The word steganography means concealed writing. Steganography , the technique of hiding messages in other files for transmission in a manner that an observer could not identify the occurrence of transmission, is gaining popularity with current industry demands. It includes various techniques of secret communications that veil the message. The various methods include invisible inks, micro dots, character arrangement, digital signatures, convert channels, least significant Bit insertion, Masking & Filtering and spread-spectrum communications.
           
                       Thus  Steganography refers to the science of invisible communication. Unlike cryptography, where the goal is to secure communications from an eaves-dropper, steganographic techniques strive to hide the very presence of the message itself from an observer. The general idea of hiding some information in digital content has a wider class of applications that go beyond steganography,  the techniques involved in such applications are collectively referred to as information hiding.

                          BASIC TYPES OF  STEGANOGRAPHY    
      

TEXT 
             Hiding information in text is historically the most important method of steganography. An obvious method was to hide a secret message in every nth letter of every word of a text message. It is only since the beginning of the Internet and all the different digital file formats that is has decreased in importance.
            Text steganography using digital files is not used very often since text files have a very small amount of redundant data.Given the proliferation of digital images, especially on the Internet, and given the large amount of redundant bits present in the digital representation of an image, images are the most popular cover objects for steganography.
AUDIO/VIDEO STEGANOGRAPHY
           To hide information in audio files similar techniques are used as for image files. One different technique unique to audio steganography is masking, which exploits the properties of the human ear to hide information unnoticeably. A faint, but audible, sound becomes inaudible in the presence of another louder audible sound.
          This property creates a channel in which to hide information. Although nearly equal to images in steganographic potential, the larger size of meaningful audio files makes them less popular to use than images.
 IMAGE STEGANOGRAPHHY
Images are the most popular cover objects used for steganography. In the domain of digital images many different image file formats exist, most of them for specific applications. For these different image file formats, different steganographic algorithms exist.
                              KEY  BASED  STEGANOGRAPHY
   
 On the basis of keys used the types of Steganography are as follows:
1.      Pure Steganography
2.      Public key Steganography
3.      Private  key Steganography
These categories convey the level of security with which the stego message is embedded,transmitted and read.
1.      Pure steganography
       Pure steganography uses no keypad system to embed clear text or ‘null cipher’ text into the cover data in order to hide the existence of a secret message. It is the least secure method. In steganalysis,this type is the easiest to crack since once detected the message can only have been hidden in as many ways as the number of steganographic algorithims which exists.
2. Public key steganography
          A public key steganography allows two parties,who have never met or exchanged a secret, to send hidden messages over a public channel so that an adversary cannot even detect that these hidden messages are being sent. In the more general case, two parties may wish to communicate steganographically, without prior agreement on a secret key. In this principle there are two keys,one being the public key which can be usually obtained from public database and other a private key. Public key is used for encryption and private key is used for decryption.
3.Private key steganography
          A private  key Steganography allows two parties with a shared secret key to send hidden messages undetectatably over a public channel.  This technique can only be used if the two parties communicating trust each other completely.

     
             PRIVATE KEY STEGANOGRAPHY



                                          




                                        BLOCK  DIAGRAM



           The block diagram of our project is as shown above. The data is to be sent by hiding it into the cover image. A sender will first select the cover image in which the secret data is to be hidden. Then according to his priorities he will decide which algorithm to be used and finally the secret data will be embedded in the cover image. This is called as Stego object. Both the original and stego image are compared and if there is no visible difference then the stego image will be sent to the receiver. The receiver should enter the decided key to start the decryption phase. At the receiver side after entering the key the secret data is extracted from the stego image.
                                    IMAGE STEGANOGRAPHY

Description:

·         Image definition

                        To a computer, an image is a collection of numbers that constitute different light intensities in different areas of the image. This numeric representation forms a grid and the individual points are referred to as pixels.Most images on the Internet consists of a rectangular map of the image’s pixels (represented as bits) where each pixel is located and its colour. These pixels are displayed horizontally row by row. The number of bits in a colour scheme, called the bit depth, refers to the number of bits used for each pixel.
           
                       The smallest bit depth in current colour schemes is 8, meaning that there are 8 bits used to describe the colour of each pixel. Monochrome and greyscale images use 8 bits for each pixel and are able to display 256 different colours or shades of grey. Digital colour images are typically stored in 24-bit files and use the RGB colour model, also known as true colour . All colour variations for the pixels of a 24-bit image are derived from three primary colours: red, green and blue, and each primary colour is represented by 8 bits.
           
                       Thus in one given pixel, there can be 256 different quantities of red, green and blue, adding up to more than 16-million combinations, resulting in more than 16-million colours. Not surprisingly the larger amount of colours that can be displayed, the larger the file size.

·         

Friday, October 4, 2013

Automatic Number Plate Recognition System

Automatic Number Plate Recognition System

Automatic vehicleidentification is an essential stage in intelligent traffic systems. Nowadays vehicles play a very big role in transportation. Also the use of vehicles has been increasing because of population growth and human needs in recent years. Therefore, control of vehicles is becoming a big problem and much more difficult to solve. Automatic vehicle identification systems are used for the purpose of effective control.

Automatic number plate recognition (ANPR) is a form of automatic vehicle identification. It is an image processing technology used to identify vehicles by only their number plates. In this study, the proposed algorithm is based on extraction of plate region, segmentation of plate characters and recognition of characters.

ANPR can be used to store the images captured by the cameras as well as the text from the number plate. Systems commonly use infrared lighting to allow the camera to take the picture at any time of the day. ANPR technology tends to be region-specific, owing to plate variation from place to place.
Concerns about these systems have centered on privacy fears of government tracking citizens' movements and media reports of misidentification and high error rates. However, as they have developed, the systems have become much more accurate and reliable.

1.1 Project Idea
The increase in the vehicles in day to day life makes it difficult to monitor each and every vehicle at toll plazas, parking and societies, so we are developing a system for the personal interest of one and all that will allow to decrease the human labour and time constraint.
The automatic number plate recognition system helps us doing it by recognizing the vehicles automatically, tracking the number plates and storing the numbers in a database.

1.2 Need of the Project

Due to the increase in the number of vehicles a lot of time is spent in checking and preparing receipts at various places like societies , toll stations and parking spaces for companies. Thus to reduce the time and the labor cost, our aim is to implement a system which  automatically recognizes the registration number of vehicles . This system can also be use for security purpose.


1.3  Literature Survey

1.3.1 Image Processing

Image Processing and Analysis can be defined as the "act of examining images for the purpose of identifying objects and judging their significance" Image analyst study the remotely sensed data and attempt through logical process in detecting, identifying, classifying, measuring and evaluating the significance of physical and cultural objects.

In computer science, image processing is any form of signal processing for which the input is an image, such as photographs or frames of video; the output of image processing can be either an image or a set of characteristics or parameters related to the image.

Remote sensing images are recorded in digital forms and then processed by the computers to produce images for interpretation purposes. Images are available in two forms - photographic film form and digital form. Variations in the scene characteristics are represented as variations in brightness on photographic films. A particular part of scene reflecting more energy will appear bright while a different part of the same scene that reflecting less energy will appear black. Digital image consists of discrete picture elements called pixels. Associated with each pixel is a number represented as DN (Digital Number), that depicts the average radiance of relatively small area within a scene. The size of this area effects the reproduction of details within the scene. As the pixel size is reduced more scene detail is preserved in digital representation. 





1.3.2 Matrix Laboratory (MATLAB)

MATLAB, which stands for MATrix LABoratory, is a state-of-the-art mathematical        software package, which is used extensively in both academia and industry. It is an interactive program for numerical computation and data visualization, which along with its programming capabilities provides a very useful tool for almost all areas of science and engineering.

It is a Image Processing Toolbox software that provides a comprehensive set of reference-standard algorithms and graphical tools for image processing, analysis, visualization, and algorithm development. You can restore noisy or degraded images, enhance images for improved intelligibility, extract features, analyze shapes and textures, and register images. Most toolbox functions are written in the MATLAB , giving you the ability to inspect the algorithms, modify the source code, and create your own custom functions.


1.3.4 Algorithm-

The number plate is normalized for brightness and contrast, and then the characters are segmented to be ready for OCR.
There are six primary algorithms that the software requires for identifying a license plate:
  1. Plate localization and preprocessing – responsible for finding and isolating the plate on the picture. In this process the captured RGB image is converted to binary format  ,then filtering is carried out to eliminate the unwanted noise.
  2. Plate orientation and sizing – compensates for the skew of the plate and adjusts the dimensions to the required size. In this process the plate is isolated from the image and the image of the plate is resized. The centroid of the first number is located and he plate is cropped accordingly.
  3. Normalization – adjusts the brightness and contrast of the image.This process is carried out to make the numbers more prominent than the background.
  4. Character segmentation – finds the individual characters on the plates.In this process the individual numbers are cropped using region properties (area,centroid) and bounding box.
  5. Character recognition using neural networks.In this process the neural network is pre trained with a number of fonts to identify the actual cropped number images.


The complexity of each of these subsections of the program determines the accuracy of the system. During the third phase (normalization), this system uses region properties(centroid) technique to detect black portions in the picture. A median filter may also be used to reduce the visual noise

Thursday, October 3, 2013

IRIS RECOGNITION USING MATLAB







IRIS RECOGNITION USING MATLAB

Introduction
Iris recognition is a method of biometric authentication that uses pattern recognition techniques based on high-resolution images of the irises of an individual's eyes. Not to be confused with another less prevalent ocular-based technology, retina scanning, iris recognition uses camera technology, and subtle IR illumination to reduce specular reflection from the convex cornea to create images of the detail-rich, intricate structures of the iris. These unique structures converted into digital templates, provide mathematical representations of the iris that yield unambiguous positive identification of an individual.
Iris recognition efficacy is rarely impeded by glasses or contact lenses. Iris technology has the smallest outlier (those who cannot use/enroll) group of all biometric technologies. The only biometric authentication technology designed for use in a one-to many search environment, a key advantage of iris recognition is its stability, or template longevity as, barring trauma, a single enrolment can last a lifetime.
Breakthrough work to create the iris recognition algorithms required for image acquisition and one-to-many matching was pioneered by John G.Daugman, who holds key patents on the method. Daugman's algorithms are the basis of almost all currently (as of 2006) commercially deployed iris-recognition systems. It has a so far unmatched practical false-accept rate of zero; that is there is no known pair of images of two different irises that the Daugman algorithm in its deployed configuration mistakenly identifies as the same.
            We will use IRIS authentication technique to control the hardware in our project. The hardware can either be a electro-mechanical lock, generator, access panel, ATM, etc.



IRIS AUTHENTICATION STEPS:
  • Grayscale Conversion
  • NTSC Weighted Averaging Conversion
  • RGB Averaging Conversion
  • Segmentation
  • Sharpening
  • Threshold
  • Edge Detection (Horizontal, Vertical)

NORMALIZATION

DAUGMAN’S RUBBER SHEET MODEL

MATLAB Project List:
A Two-Level FH-CDMA Scheme for Wireless Communication Systems over Fading Channels.
Efficient SNR Estimation in OFDM System.
IMAGE Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition.
A Fast Adaptive Kalman Filtering Algorithm for Speech Enhancement.
Quality Assessment of Deblocked Images.
Number Plate Recognition for Use in Different Countries Using an Improved Segmentation.
A NOVEL APPROACH OF IMAGE FUSION ON MR AND CT IMAGES USING WAVELET TRANSFORMS.
Stationary and Non-Stationary noise removal from Cardiac Signals using a Constrained Stability Least Mean Square Algorithm.
A New ZCT Precoded OFDM System with Pulse Shaping: PAPR Analysis.
Candidate Architecture for MIMO LTE-Advanced Receivers with Multiple Channels Capabilities and Reduced Complexity and Cost.
Super-Resolution Method for Face Recognition Using Nonlinear Mappings on Coherent Features.
A Single Image Enhancement using Inter-channel Correlation.
Adaptive Steganalysis of Least Significant Bit Replacement in Grayscale Natural Images.
HAIRIS: A Method for Automatic Image Registration Through Histogram-Based Image Segmentation.
Non-blind watermarking scheme for color images in RGB space using DWT-SVD.
Research and implementation of information hiding based on RSA and HVS.
Audio Forensic marking using Quantization in DWT-SVD Domain.
Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetrical Trimmed Median Filter.
Color Constancy for Multiple Light Sources.
Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering.
On Performance Improvement of Wireless Push Systems via Smart Antennas.
A Semi supervised Segmentation Model for Collections of Images.
Secure Communication in the Low-SNR Regime.
Interpolation-Based Image Super-Resolution Using Multi surface Fitting.

Illumination Recovery from Image with Cast Shadows via Sparse Representation.


FEATURE EXTRACTION
Feature Matching
IRIS Template Bit Pattern Generation (Please note that we wont be using Gabor Filters for this but rather histogram filters)


Bit Pattern Comparison

Result

Matlab Projects Pune







Device Control Using Hand-Gesture Recognition

Project Definition:  
This project aims in implementing real time gesture recognition. The primary goal of the project is to create a system that can identify human generated gestures and use this information for device control.

The user performs a gesture in front of a camera, which is linked to the computer. The picture of the gesture is then processed to identify the gesture indicated by the user. Once the gesture is identified corresponding control action assigned to the gesture is actuated.

Scope: 
In this system, we basically recognize hand gestures through software and these hand gestures will be used for controlling certain software as well as hardware devices.

Objective:
·        Provide a more natural Human Computer Interface.
·        Provide the physically challenged users a better way to interact with the computers.
·        Interfacing user with software.
·        User can give input without using keyboard, mouse, etc .i.e. to give input  in the form of different body gestures.
·        Helpful for handicap people.
·        Actually finds out the directional gesture vector.
·        Direction as well as count both will be responsible for the hardware control.
E.g. Advanced Hardware Control, Robot Control.

Relevant theory:
We propose a fast algorithm for automatically recognizing a limited set of gestures from hand images for a robot control application. Hand gesture recognition is a challenging problem in its general form. The algorithm is invariant to translation, rotation, and scale of the hand.

We demonstrate the effectiveness of the technique on real imagery. Vision-based automatic hand gesture recognition has been a very active research topic in recent years with motivating applications such as human computer interaction (HCI), robot control, and sign language interpretation. The general problem is quite challenging due to a number of issues including the complicated nature of static and dynamic hand gestures, complex backgrounds, and occlusions. Attacking the problem in its generality requires elaborate algorithms requiring intensive computer resources. What motivates us for this work is a robot navigation problem, in which we are interested in controlling a robot by hand pose signs given by a human. Due to real-time operational requirements, we are interested in a computationally efficient algorithm.

Early approaches to the hand gesture recognition problem in a robot control context involved the use of markers on the finger tips. An associated algorithm is used to detect the presence and color of the markers, through which one can identify which fingers are active in the gesture.

Device Control Using Hand-Gesture Recognition

Project Definition:   
This project aims in implementing real time gesture recognition. The primary goal of the project is to create a system that can identify human generated gestures and use this information for device control.

The user performs a gesture in front of a camera, which is linked to the computer. The picture of the gesture is then processed to identify the gesture indicated by the user. Once the gesture is identified corresponding control action assigned to the gesture is actuated.

Scope:  
In this system, we basically recognize hand gestures through software and these hand gestures will be used for controlling certain software as well as hardware devices. 

Objective:
Provide a more natural Human Computer Interface.
Provide the physically challenged users a better way to interact with the computers.
Interfacing user with software.
User can give input without using keyboard, mouse, etc .i.e. to give input  in the form of different body gestures.
Helpful for handicap people.
Actually finds out the directional gesture vector.
Direction as well as count both will be responsible for the hardware control.
E.g. Advanced Hardware Control, Robot Control.