Image Compression Using Modified Haar Wavelet Transform Matlab Project with Source Code

ABSTRACT
           Haar transform is one of the simplest and basic transformation from the multi-resolution spectrum. The attracting features obtained from Haar transform make it a potential candidate in modern applications, such as signal and image compression. The Haar wavelet transform provides mean values that compress the image so that it takes up much less storage space, and therefore transmits faster electronically and in progressive levels of detail. The main objective of this work is to modify the weighting factor in or der to study their effects on image compression. This paper tries to implement different scale of weighting factor and study their performance on the overall system of compression. Scale of weighting factor is used in order to prevent the pixel value from exceeding their limits. Different values of weighting factor are applied, these values are spans into two range to evaluate the implemented system. 

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Mr. Roshan P. Helonde
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Matlab Project for Biometric Recognition Using Face, Palm, Retina and Signature using Matlab full Source Code || IEEE Based Project

ABSTRACT
              Achieving high security in varied areas, biometric system has become common analysis space over past decades. Biometric system provides machine-controlled personal identification supported distinctive features of an individual. Biometric system depends on distinguishing every individual on the premise of their physiological options Face, Finger Print, Palm Print, Retina. Security will primarily be achieved by three factors: password or pin, sensible token or access card, biometric technology. Out of those three ways, biometric system is best as a result of user ought not to remember (password or pin) or keep something (smart token or access card) for identification or verification. In this project present a novel approach Biometric Recognition Using Face, Palm, Retina and Signature.

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Mr. Roshan P. Helonde
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Face Recognition Using Image Processing Matlab Project with Source Code || IEEE Based Project

ABSTRACT
             Face recognition from image is a popular topic inbiometrics research. Many public places usually have surveillance cameras for image capture and these cameras have their significant value for security purpose. It is widely acknowledged that the face recognition have played an important role in surveillance system as it doesn’t need the object’s cooperation. The actual advantages of face based identification over other biometrics are uniqueness and acceptance. As human face is a dynamic object having high degree of variability in its appearance, that makes face detection a difficult problem in computer vision. In this field, accuracy and speed of identification is a main issue. The goal of this project is to evaluate various face detection and recognition methods, provide complete solution for image based face detection and recognition with higher accuracy.

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Mr. Roshan P. Helonde
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Matlab Project Breast Cancer Detection in Mammograms Full Source Code || IEEE Based Project

ABSTRACT
            The World Health Organization's International agency for Research on Cancer in Lyon, France, estimates that more than 150 000 women worldwide die of breast cancer each year. The breast cancer is one among the top three cancers in American women. In United States, the American Cancer Society estimates that, 215 990 new cases of breast carcinoma has been diagnosed, in 2004. It is the leading cause of death due to cancer in women under the age of 65 . In India, breast cancer accounts for 23% of all the female cancers followed by cervical cancers (17.5%) in metropolitan cities such as Mumbai, Calcutta, and Bangalore. However, cervical cancer is still number one in rural India. Although the incidence is lower in India than in the developed countries, the burden of breast cancer in India is alarming. Organ chlorines are considered a possible cause for hormone-dependent cancers . Detection of early and subtle signs of breast cancer requires high-quality images and skilled mammographic interpretation. In order to detect early onset of cancers in breast screening, it is essential to have high-quality images. Radiologists reading mammograms should be trained in the recognition of the signs of early onset of, which may be subtle and may not show typical malignant features. Mammography screening programs have shown to be effective in decreasing breast cancer mortality through the detection and treatment of early onset of breast cancers.
          Emotional disturbances are known to occur in patient's suffering from malignant diseases even after treatment. This is mainly because of a fear of death, which modifies Quality Of Life (QOL). Desai et al.,reported an immuno histo chemical analysis of steroid receptor status in 798 cases of breast tumors encountered in Indian patients, suggests that breast cancer seen in the Indian population may be biologically different from that encountered in western practice. Most imaging studies and biopsies of the breast are conducted using mammography or ultrasound, in some cases, magnetic resonance (MR) imaging . Although by now some progress has been achieved, there are still remaining challenges and directions for future research such as developing better enhancement and segmentation algorithms. 

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Matlab Project with Source Code Electronic Online Voting Machine (EVM) Using Matlab

ABSTRACT
                    Electronic voting machine is generally used now days in some countries including India for conducting election of government in a country. But the Electronic voting machine has certain disadvantages like illegal voting and insecurity. Hence the concept of online voting system is started in some countries for conducting election. Most of the developed countries have started using online voting system but they are facing some problems in conducting it. Estonia is the only country started conducting the online voting system in national election. But the percentage of voting is only 20% to 30%. Different researchers have designed a online voting system But the system are not so much efficient in terms of accuracy and security. Also the voting system has high error rate. Hence the voting system is not flexible and can be used for specific region only. Biometric authentication is found to be more secure and accurate in certain application. Different biometric authentications like fingerprint, retina etc. can be used in designing an application to enhance the security. As fingerprint of every individual is unique it can be used for designing a voting system. Different fingerprint matching techniques has been discussed considering the FRR ratio.

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Matlab Project Handwritten Character Recognition Using Neural Network Full Source Code

ABSTRACT
             Recognition of Handwritten text has been one of the active and challenging areas of research in the field of image processing and pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. In this project we focus on recognition of English alphabet in a given scanned text document with the help of Neural Networks. Using Matlab Neural Network toolbox, we tried to recognize handwritten characters by projecting them on different sized grids. The first step is image acquisition which acquires the scanned image followed by noise filtering, smoothing and normalization of scanned image, rendering image suitable for segmentation where image is decomposed into sub images. Feature Extraction improves recognition rate and mis classification. We use character extraction and edge detection algorithm for training the neural network to classify and recognize the handwritten characters.

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Matlab Project on Lossless Image Compression Using Image Processsing

ABSTRACT
            The lossless compression is that allows the original data to be perfectly reconstructed from the compressed data. Lossless compression programs do two things in sequence: the first step generates a statistical model for the input data, and the second step uses this model to map input data to bit sequences in such a way that probable. The main objective of image compression is to decrease the redundancy of the image data which helps in increasing the capacity of storage and efficient transmission. Image compression aids in decreasing the size in bytes of a digital image without degrading the quality of the image to an undesirable level. Image compression plays an important role in computer storage and transmission. The purpose of data compression is that we can reduce the size of data to save storage and reduce time for transmission. Image compression is a result of applying data compression to the digital image.

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Image Watermarking Based On DWT, DCT, BFO and PBFO Matlab Project with Source Code

ABSTRACT
          The authenticity & copyright protection are two major problems in handling digital multimedia. The Image watermarking is most popular method for copyright protection by discrete Wavelet Transform (DWT) which performs 2 Level Decomposition of original (cover) image and watermark image is embedded in Lowest Level (LL) sub band of cover image. Inverse Discrete Wavelet Transform (IDWT) is used to recover original image from watermarked image and Discrete Cosine Transform (DCT) which convert image into Blocks of M bits and then reconstruct using IDCT. In this project we have compared watermarking using DWT, DCT, BFO and PBFO methods performance analysis on basis of PSNR, NCC and IF Similarity factor of watermark and recovered watermark.

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Mr. Roshan P. Helonde
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Matlab Project for Face Recognition Using Image Processing Full Source Code || IEEE Based Project

ABSTRACT
             Face recognition from image is a popular topic inbiometrics research. Many public places usually have surveillance cameras for image capture and these cameras have their significant value for security purpose. It is widely acknowledged that the face recognition have played an important role in surveillance system as it doesn’t need the object’s cooperation. The actual advantages of face based identification over other biometrics are uniqueness and acceptance. As human face is a dynamic object having high degree of variability in its appearance, that makes face detection a difficult problem in computer vision. In this field, accuracy and speed of identification is a main issue. The goal of this project is to evaluate various face detection and recognition methods, provide complete solution for image based face detection and recognition with higher accuracy.

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Mr. Roshan P. Helonde
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Matlab Project Currency Recognition Using Image Processing full Source Code

ABSTRACT
                The Reserve Bank is the one which issue bank notes in India. Reserve Bank, changes the design of bank notes from time to time. Reserve bank uses several techniques to detect fake currency. Common people faces many problems for the fake currency circulation and also difficult to detect fake currency, suppose that a common people went to a bank to deposit money in bank but only to see that some of the notes are fake, in this case he has to take the blame. As banks will not help that person. Some of the effects that fake currency has on society include a reduction in the value of real money; and inflation due to more fake currency getting circulated in the society or market which disturbs our economy and growth - an some illegal authorities an artificial increase in the money supply,a decrease in the acceptability of paper money and losses. Our aim is to help common man to recognize currency. Proposed system is based on image processing and makes the process automatic and robust. Shape information are used in our algorithm. Original Note Detection Systems are present in banks but are very costly. We are developing an image processing algorithm which will extract the currency features and compare it with features of original note image. This system is cheaper and can provide accuracy on the basics of visual contents of note.

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Matlab Project Character Recognition from Text Images Using Image Processing Full Source Code || IEEE Based Project

ABSTRACT
                 Optical character recognition (OCR) is becoming a powerful tool in the field of Character Recognition, now a days. In the existing globalized environment, OCR can play a vital role in different application fields. Basically, OCR technique converts images into editable format. This technique converts images in the form of documents such as we can edit, modify and store data more safely for longtime. This paper presents basic of OCR technique with its components such as pre-processing, Feature Extraction, Classification, post-processing etc. There are various techniques have been implemented for the recognition of character. This Review also discusses different ideas implemented earlier for recognition of a character. This paper may act as a supportive material for those who wish to know about OCR. Now a days, globalization is reaching to a great level. In this globalized environment, character recognition techniques also getting a valuable demand in number of application areas. OCR is an effective technique which converts image into suitable format such that data can be edit, modify and stored. This technique performs several operations such as, scans the input image, processes over the scanned image thereby image gets converted into portable formats .For instance, the hard copy of old historical books, novels, etc. .cannot be stored safely for a long time. Rather, its safety has limitations. If we apply OCR technique for such cases, the different historical documents can be stored, modified for a longtime. OCR also having variety of applications in almost all fields, including security. OCR implementation helps us to edit, store and process over the scanned data more effectively. User can handle the stored data whenever he wants with the internet support. So Optical character recognition is most successful application used in pattern recognition.

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Fruit Disease Detection and Classification Using Image Processing Matlab Project with Source Code || IEEE Based Project

ABSTRACT
            Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this paper, a solution for the detection and classification of fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following steps; in the first step K-Means clustering technique is used for the image segmentation, in the second step some features are extracted from the segmented image, and finally images are classified into one of the classes by using a Support Vector Machine. Our experimental results express that the proposed solution can significantly support accurate detection and automatic
classification of fruit diseases.
             Fruit diseases can cause significant losses in yield and quality appeared in harvesting. For example, soybean rust (a fungal disease in soybeans) has caused a significant economic loss and just by removing 20% of the infection, the farmers may benefit with an approximately 11 million-dollar profit (Roberts et al., 2006). Some fruit diseases also infect other areas of the tree causing diseases of twigs, leaves and branches. An early detection of fruit diseases can aid in decreasing such losses and can stop further spread of diseases. A lot of work has been done to automate the visual inspection of the fruits by machine vision with respect to size and color. However, detection of defects in the fruits using images is still problematic due to the natural variability of skin color in different types of fruits, high variance of defect types, and presence of stem/calyx. To know what control factors to consider next year to overcome similar losses, it is of great significance to analyze what is being observed.

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Matlab Project for Malaria Parasite Detection Using Image Processing Techniques || IEEE Based Project

ABSTRACT
              Malaria is an extremely infectious disease cause due to blood parasite of genus plasmodium. Malaria is a terrible disease in the hematological region causing millions of mortality; hence the fast diagnosing is the extreme requirement of era. Conventional microscopy, which is presently “the gold Standard” for malaria detection has occasionally proved ineffective as it takes lots of time and outcomes are complicated to reproduce. Since it poses a global health problem, automation of the evaluation method is of high significance. An image processing system is able to enhance outcomes of detection of malaria parasite cell. A variety of image processing techniques are used in the proposed method. The method proceeds in steps like image transformation, classification and feature extraction. This method assists to reduce time as well as afford the accuracy to detect malaria to certain extent. There are lots of methods to detect malaria, among them manual microscopy is considered to be "the gold standard". However because of the various steps essential in manual estimation, this diagnostic technique takes too much time. Malaria infections are detected manually by pathologists who observe the microscopic images of strained blood records on glass slides and calculate the contaminated blood cells. If sample size of patient is great, there is always a possibility to detect imprecisely. There is a chance to occur human error, so computer based classification using digital image processing methods gives better outcome than the manual diagnoses of Malaria. Intend of this work is to build up a detection method to correctly detected malaria parasites present in images. In the pre-processing stages digital image processing systems are used to obtain high-quality medical images.In this project, Image Processing is used to detect the existence of Malaria Parasite. In the proposed system, various steps are used such as image transformation, feature extraction and image classification.

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Age and Gender Recognition Using Image Processing Matlab Project with Source Code || IEEE Based Project

ABSTRACT
               In this project, a fast and efficient gender and age estimation system based on facial images is developed. There are many methods have been proposed in the literature for the age estimation and gender classification. However, all of them have still disadvantage such as not complete reflection about face structure, face texture. We classified the gender and age based on the association of two methods: geometric feature based method and Principal Component Analysis (PCA) method for improving the efficiency of facial feature extraction stage. The face database contains the 13 individual groups. Within a given database, all weight vectors of the persons within the same age group are averaged together. Experimental results show that better gender classification and age estimation. Gender classification is important visual tasks for human beings, such as many social interactions critically depend on the correct gender perception. As visual surveillance and human-computer interaction technologies evolve, computer vision systems for gender classification will play an increasing important role in our lives. Age prediction is concerned with the use of a training set to train a model that can estimate the age of the facial images.

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Mr. Roshan P. Helonde
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Matlab Project with Source Code for Detection of Cardiac Disease from ECG Signal Data || IEEE Based Project

ABSTRACT
            Modern day lifestyle and our ignorance towards health have put the most vital organ of our body Heart at great risk. India today is witnessing a lot many young people suffering from heart diseases which even lead to untimely demise. Most common heart abnormality includes arrhythmia which is nothing but irregular beating of heart. Going by the trend/statistics, middle aged people (30-45yrs) are at great risk because of high stress in both personal and professional lives. This necessitates the need for a system which can not only detect any anomaly in functioning of our heart but warns us against any threat. Our project is based on developing such a system that can give us prior information about the upcoming threat or the heart disease which we are prone to. Cardiac arrhythmia is a major kind of abnormal heart activity. An arrhythmia is a problem with the heartbeat rate or rhythm of the heartbeat. For the period of an arrhythmia, the heart may beat too fast or too slow, or with an irregular rhythm. Fast heartbeat is said to be tachycardia whereas slow is called Bradycardia. Classification of cardiac arrhythmia is a difficult task. One of the ways to detect cardiac arrhythmia is to use electrocardiogram (ECG) signals. The ECG is the most important biomedical-signal used by cardiologists for diagnostic purposes. The ECG signal provides all the required information about the electrical activity of the heart. The early detection of the cardiac arrhythmias can save lives and enhance the quality of living through appreciates treatment.

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Matlab Project for Emotion and Gender Recognition Using Image Processing || IEEE Based Project

ABSTRACT
              The most important and impressive biometric feature of human being is the face. It conveys various information including gender, ethnicity etc. Face information can be applied in many sectors like biometric authentication and intelligent human-computer interface. Many potential applications such as human identification, smart human computer interface, computer vision approaches for monitoring people, passive demographic data collection, and etc needs a successful and dependable classification method. It is really a very challenging job to detect male or female accurately separating two sets of data. So it is very urgent to have a reliable classifier to improve the classification performance.This project presents an approach to extract effective features for face detection and gender classification system. In various biometric applications, gender recognition from facial images plays an important role. In this project gender recognition image sequence have been successfully investigated. Gender recognition plays an important role for a wide range of application in the field of Human Computer Interaction. The system comprises two modules: a face detector and a gender classifier. The human faces are first detected and localized in the input image. Each detected face is then passed to the gender classifier to determine whether it is a male or female.

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Lung Cancer Detection Using Neural Network Matlab Project with Source Code || IEEE Based Project

ABSTRACT
             In today’s world ,image processing methodology is very rampantly used in several medical fields for image improvement which helps in early detection and analysis of the treatment stages ,time factor also plays a very pivtol role in discovering the abnormality in the target images like-lung cancer ,breast cancer etc. this research focuses upon image quality and accuracy. image quality assessment as well as improvement are dependent upon enhancement stage where low pre-processing techniques are used based upon gabor filter within Gaussian rules; thereafter the segmentation principles are applied over the enhanced region of the image and the input for feature extraction is obtained, further depending upon the general features, a normality comparison is made .in the following research the crucial detected features for accurate image comparison are pixel percentage and masking labelling. In this research we have done classification based upon artificial neural networks which is more satisfactory than other current classification methods.

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Matlab Project Object Tracking Form Video Full Source Code || Final Year Project || IEEE Based Project

ABSTRACT
               The ongoing research on object tracking in video sequences has attracted many researchers. Detecting the objects in the video and tracking its motion to identify its characteristics has been emerging as a demanding research area in the domain of image processing and computer vision. Most of the methods include object segmentation using background subtraction. The tracking strategies use different methodologies like Mean-shift, Kalman filter, Particle filter etc. The performance of the tracking methods vary with respect to background information. In this survey, we have discussed the feature descriptors that are used in tracking to describe the appearance of objects which are being tracked as well as object detection techniques. In this survey, we have classified the tracking methods into three groups, and a providing a detailed description of representative methods in each group, and find out their positive and negative aspects.

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Mr. Roshan P. Helonde
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Matlab Project for Rain Removal using Image Processing full Source Code || Final Year Project || IEEE Based Project

ABSTRACT
                 The rain removal from an image in the rainy season is also a required task to identify the object in it. It is a challenging problem and has been recently investigate extensively. In this project the entropy maximization and background estimation based method is used for the rain removal. This method is based on single-image rain removal framework. The raindrops are greatly differing from the background, as the intensity of rain drops is higher the background. The entropy maximization is very much suitable for the rain removal. Experimental results express the efficacy of the rain removal by proposed algorithm is better than the method based on saturation and visibility features. The rain and non-rain parts in a single image are very closely mixed up and the identification of rain streaks is not an easy task. In this project, we compare a single-image rain streak removal based on morphological component analysis (MCA) by decomposition of rain streaks. The signal and image processing for the filtering and region specification are discussed in the previous works. In this method, a bilateral filter is applied for an image to decompose it into the low-frequency (LF) and high-frequency (HF) parts. The HF part is then decomposed into rain component and non-rain component by performing sparse coding and dictionary learning on MCA.

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Mr. Roshan P. Helonde
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Matlab Project for Attendance Maintainance Using Face Recognition Full Source Code || Final Year Project || IEEE Based Project

ABSTRACT
        Preserving the attendance is very crucial in all the institutes for checking the overall performance of students. Each institute has its very own method in this regard. A few are taking attendance manually using the old paper or document based approach and some have adopted techniques of automated attendance the use of few biometric techniques. There are many computerised methods to be had for this reason i.e. biometric attendance. All these methods additionally waste time due to the fact that college students or employees have to make a queue to contact their thumb on the scanning device. This gadget makes use of the face recognition approach for the computerised attendance of students in the study room environment without lectures intervention or the employee .This attendance is recorded with the aid of usage of a digital camera connected in the study room or the working environment i.e. constantly shooting photos of students or employees, discover the faces in pix and examine the detected faces with the database and mark the attendance.

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Mr. Roshan P. Helonde
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Matlab Project for Image Forgery Detection full Source Code || Final Year Project || IEEE Based Project

ABSTRACT
             Image forgery means manipulation of digital image to conceal meaningful information of the image. The detection of forged image  is  driven  by  the  need  of  authenticity  and  to  maintain integrity of the image. A copy move forgery detection theme victimization adaptive  over  segmentation  and have  purpose feature matching is proposed. The proposed scheme integrates both block based   and   key point based   forgery   detection  methods. The proposed adaptive over segmentation algorithm segments  the  host  image  into  non over lapping  and  irregular blocks adaptively. Then, the feature points are extracted from  each  block  as  block  features,  and  the  block  features  are matched with one another to locate the labeled feature points; this   procedure can   approximately indicate   the   suspected forgery    regions.    To    detect    the    forgery regions more accurately, we propose the forgery region extraction algorithm which  replaces  the  features  point  with  small super  pixels  as feature  blocks  and  them  merges  the  neighboring  blocks  that have  similar  local color  features  into  the  feature  block  to generate    the    merged    regions. Finally,    it    applies    the morphological  operation  to  merged  regions  to  generate  the detected forgery regions. In cut paste image forgery detection, proposed   digital   image   forensic techniques capable   of detecting  global  and  local contrast  enhancement,  identifying the use of histogram equalization.

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Matlab Project for Video Compression full Source Code || Final Year Project || IEEE Based Project

ABSTRACT
            The huge usage of digital multimedia via communications, wireless communications, Internet, Intranet and cellular mobile leads to incurable growth of data flow through these Media. The researchers go deep in developing efficient techniques in these fields such as compression of data, image and video. Recently, video compression techniques and their applications in many areas (educational, agriculture, medical …) cause this field to be one of the most interested fields. Wavelet transform is an efficient method that can be used to perform an efficient compression technique. This work deals with the developing of an efficient video compression approach based on frames  difference approaches that concentrated on the calculation of frame near distance (difference between frames). The selection of the meaningful frame depends on many factors such as compression performance, frame details, frame size and near distance between frames. Three different approaches are applied for removing the lowest frame difference. In this project videos are tested to insure the efficiency of this technique, in addition a good performance results has been obtained. 

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Matlab Project for Audio Denoising full Source Code || Final Year Project || IEEE Based Project

ABSTRACT
                Audio noise reduction system is the system that is used to remove the noise from the audio signals. Audio noise reduction systems can be divided into two basic approaches. The first approach is the complementary type which involves compressing the audio signal in some well-defined manner before it is recorded (primarily on tape). The second approach is the single-ended or non-complementary type which utilizes techniques to reduce the noise level already present in the source material in essence a playback only noise reduction system. Noise reduction is the process of removing noise from a signal.Digital filters effectively reduce the unwanted higher or lower order frequency components in a speech signal. In this paper the speech enhancement is performed using different digital filters .In this real noisy environment is taken into consideration in the form of Gaussian noise. The Time domain as well as frequency domain representation of the signal spectra is performed using Fast Fourier transformation technique. MATLAB in built functions are used to carry out the simulation. Gaussian type noise is added using in-built function randn () and keyboard noise is added as a second speech file to the original speech signal. The filters remove the lower frequency components of noise and recover the original speech signal. It is also observed that keyboard noise is typical to remove as compared to Gaussian type but these filters performed well to get sharper spectra of original speech signal. Speech signal analysis is one of the important areas of research in multimedia applications. Discrete Wavelet technique is effectively reduces the unwanted higher or lower order frequency components in a speech signal. Wavelet-based algorithm for audio de-noising is worked out. We focused on audio signals corrupted with white Gaussian noise which is especially hard to remove because it is located in all frequencies.

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Automatic Evaluation of OMR Answer Sheet & Finding Exam Score Using Image Processing Matlab Project with Source Code

ABSTRACT
           This project aims to develop Image processing based Optical Mark Recognition sheet scanning system. Today we find that lot of competitive exams are been conducted as entrance exams. These exams consists of MCQs. The students have to fill the right box or circle for the appropriate answer to the respective questions. During the inspection or examining phase normally a stencil is provided to the examiner to determine the right answer to the questions. This is a manual process and a lot of errors can occur in the manual process such as counting mistake and many more. To avoid this mistakes OMR system is used. In this system OMR answer sheet will be scanned and the scanned image of the answer sheet will be given as input to the software system. Using Image processing we will find the answers marked to each of the questions. Summation of the marks & displaying of total marks will be also implemented. The implementation is done using Matlab
        In today’s modern world of technology when everything is computerized, the Evaluation exercise of examining and assessing the educational system has become absolute necessity. Today, more emphasis is on objective exam which is preferred to analyze scores of the students since it is simple and requires less time in the examining objective answer-sheet as compared to the subjective answer-sheet. This project proposes a new technique for generating scores of multiple-choice tests which are done by developing a technique that has software based approach with computer & scanner which is simple, efficient & reliable to all with minimal cost. Its main benefit to work with all available scanners, In addition no special paper & colour required for printing for marksheet. To recognize & allot scores to the answer marked by of the student’s.

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Mr. Roshan P. Helonde
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WhatsApp: +91-7276355704
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Facial Expression Based Emotion Detection System Full Matlab Project with Source Code

ABSTRACT
                This project objective is to introduce needs and applications of facial expression recognition. Between Verbal & Non-Verbal form of communication facial expression is form of non-verbal communication but it plays pivotal role. It express human perspective or filling & his or her mental situation. A big research has been addressed to enhance Human Computer Interaction (HCI) over two decades. This project includes introduction of facial emotion recognition system, Application, comparative study of popular face expression recognition techniques & phases of automatic facial expression recognition system. Emotional aspects have huge impact on Social intelligence like communication understanding, decision making and also helps in understanding behavioral aspect of human. Emotion play pivotal role during communication. Emotion recognition is carried out in diverse way, it may be verbal or non-verbal .Voice (Audible) is verbal form of communication & Facial expression, action, body postures and gesture is non-verbal form of communication. While communicating only 7% effect of message is contributes by verbal part as a whole, 38% by vocal part and 55% effect of the speaker’s message is contributed by facial expression. For that reason automated & real time facial expression would play important role in human and machine interaction. Facial expression recognition would be useful from human facilities to clinical practices. Analysis of facial expression plays fundamental roles for applications which are based on emotion recognition like Human Computer Interaction (HCI), Social Robot, Animation, Alert System & Pain monitoring for patients.

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Mr. Roshan P. Helonde
Mobile: +91-7276355704
WhatsApp: +91-7276355704
Email: roshanphelonde@rediffmail.com
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Plant Disease Detection & Classification on Leaf Images using Image Processing Matlab Project with Source Code

ABSTRACT
                  Diseases decrease the productivity of plant. Which restrict the growth of plant and quality and quantity of plant also reduces. Image processing is best way for detecting and diagnosis the diseases. In which initially the infected region is found then different features are extracted such as color, texture and shape. Finally classification technique is used for detecting the diseases. There are different feature extraction techniques for extracting the color, texture and edge features such as color space, color histogram, grey level co-occurrence matrix (CCM), Gabor filter, Canny and Sobel edge detector. India is agricultural country and most of population depends on agriculture. Farmers have wide range of selection in Fruit and Vegetable crops. The cultivation can be improved by technological support. Disease is caused by pathogen in plant at any environmental condition. In most of the cases diseases are seen on the leaves, fruits and stems of the plant, therefore detection of disease plays an important role in successful cultivation of crops. In most of cases plant diseases are caused by pathogens, microorganism, fungi, bacteria, viruses, etc. Sometimes unhealthy environment
include soil and water is also responsible for diseases in plants.

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Mr. Roshan P. Helonde
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WhatsApp: +91-7276355704
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Object Detection and Tracking using Background Subtraction Matlab Project with Source Code

ABSTRACT
             Digital image processing is one of the most researched fields nowadays. The ever increasing need of surveillance systems has further on made this field the point of emphasis. Surveillance systems are used for security reasons, intelligence gathering and many individual needs. Object tracking and detection is one of the main steps in these systems. Different techniques are used for this task and research is vastly done to make this system automated and to make it reliable. In this research subjective quality assessment of object detection and object tracking is discussed in detail. In the proposed system the background subtraction is done from the clean original image by using distortion of color and brightness.  The detection of a moving object and tracking of different objects in a video or video sequence is a very important task in the surveillance videos, analysis and monitoring of traffic, tracking and detection of humans and different gesture recognition in human-machine interface. The technique of Object tracking can be explained to be the method of tracking the different number of objects in the video and also the certain directions those objects are traversing in and also to track the entrances to the surveillance site as per the unit time. The sophistication and the complexity of the system determine the resolution of the measurement. This system is often deployed in public places such as shopping malls, metro stations, airports and independent surveillance requests. Different approaches can be used for the surveillance and different technologies used as computer vision, infrared beams and thermal imaging. The reasons for object tracking are many For example People counting in retail stores for intelligence gathering can be regarded as one. This is used for the calculation of the conversion rate and rating of the store by the number of customers to the store rather than the old use of the sales data.

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Mr. Roshan P. Helonde
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Shape Detection and Recognition Using Image Processing Matlab Project with Source Code

ABSTRACT
                    Doing image processing and especially blob analysis it is often required to check some objects' shape and depending on it perform further processing of a particular object or not. For example, some applications may require finding only circles from all the detected objects, or quadrilaterals, rectangles, etc. Human vision seems to make use of many sources of information to detect and recognize an object in a scene. At the lowest level of object recognition, researchers agree that edge and region information are utilized to extract a “perceptual unit” in the scene. Some of the possible invariant features are recognized and additional signal properties (texture or appearance) are sent to help in making the decision as to whether a point belongs to an object or not. In many cases, boundary shape information, such as therectangular shapes of vehicles in aerial imagery, seems to play a crucial role. Local features such as the eyes in a human face are sometimes useful. These features provide strong clues for recognition, and often they are invariant to many scene variables.The study of shapes is a recurring theme in computer vision. For example, shape is one of the main sources of information that can be used for object recognition. In medical image analysis, geometrical models of anatomical structures play an important role in automatic tissue segmentation. The shape of an organ can also be used to diagnose diseases. In a completely different setting, shape plays an important role in the perception of optical illusions (we tend to see particular shapes) and this can be used to explain how our visual system interprets the ambiguous and incomplete information available in an image. Characterizing the shape of a specific rigid object is not a particularly hard problem, although using the shape information to solve perceptual tasks is not easy.

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Mr. Roshan P. Helonde
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DCT Based Image Watermarking Full Matlab Project with Source Code

ABSTRACT
              Digital watermarking is a technology for embedding various types of information in digital content. In general, information for protecting copyrights and proving the validity of data is embedded as a watermark. A digital watermark is a digital signal or pattern inserted into digital content. The digital content could be a still image, an audio clip, a video clip, a text document, or some form of digital data that the creator or owner would like to protect. The main purpose of the watermark is to identify who the owner of the digital data is, but it can also identify the intended recipient. The DCT allows an image to be broken up into different frequency bands, making it much easier to embed watermarking information into the middle frequency bands of an image. It has become easy to connect to the Internet from home computers and obtain or provide various information using the World Wide Web (WWW). All the information handled on the Internet is provided as digital content. Such digital content can be easily copied in a way that makes the new file indistinguishable from the original. Then the content can be reproduced in large quantities. For example, if paper bank notes or stock certificates could be easily copied and used, trust in their authenticity would greatly be reduced, resulting in a big loss. To prevent this, currencies and stock certificates contain watermarks. These watermarks are one of the methods for preventing counterfeit and illegal use. Digital watermarks apply a similar method to digital content. Watermarked content can prove its origin, thereby protecting copyright. A watermark also discourages piracy by silently and psychologically deterring criminals from making illegal copies.In digital management, multimedia content and data can easily be used in an illegal way—being copied, modified and distributed again. In this paper, we apply DCT technique to embed the watermark. With the help of DCT technique we can insert the data in short variation of time. A digital watermark is an invisible mark embedded in digital image which may be used for Copyright Protection. We describe a watermarking scheme for ownership verification and authentication. Depending on the desire of the user, the watermark can be either visible or invisible. The scheme can detect any modification made to the image and indicate the specific locations that have been modified.

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Email: roshanphelonde@rediffmail.com
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