Gender Recognition System from Speech Signal Matlab Project with Source Code IEEE Project

              
ABSTRACT
             Signal is a physical quantity that varies with respect to the independent variable like time, space, etc. Signal values can be represented in zero’s and one’s. Processing of digital signal by using digital computer is called as Digital Signal Processing. According to Webster’s dictionary, speech is the expression or communication throughout in speakers. Speech is the most important thing to express our thoughts. Speech signal is used to communicate among people. It not only consists of the information but also carries the information regarding the particular speaker. From which the speaker is male or female can be recognised. The meaning of Gender Recognition (GR) is recognising the gender of the person whether the speaker is male or female. The Information about gender, age, ethnicity, and emotional state are the important ingredients that give rich behavioural information. Such information can be obtained from the speech signal. In this project, an unknown speaker is compared to a database of some known speakers. The best matching system is taken as the recognition decision. From the Recognition decision we conclude whether the given voice sample is generated by a male or female.

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Shape Recognition Using Image Processing Matlab Project with Source Code IEEE Project

         
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 recognise an object in a scene. At the lowest level of object recognition, researchers agree that edge and region information are utilised to extract a “perceptual unit” in the scene. Some of the possible invariant features are recognised 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 the rectangular 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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Matlab Code for Glaucoma Detection Using Image Processing

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ABSTRACT
                Computational techniques have great impact in the field of Medicine and Biology. These techniques help the medical practitioners to diagnose any abnormality in advance and provide fruitful treatment. Retinal image analysis has been an ongoing area of research. Automated retinal image analysis aid the ophthalmologists in detecting abnormalities in the retinal structures namely optic disc, blood vessels, thus diagnosing sight threatening retinal diseases such as Glaucoma and Retinopathy. Glaucoma is the major cause of blindness in working population. Glaucoma is characterised by increased intra-ocular pressure inside the eye leading to changes in the optic disc and optic nerve. It does not reveal its symptoms until later stage. Hence, regular screening of the patients is required to identify the disease, thus demanding high labor, time and expertise. Thus, computational techniques are sought for their analysis.
                 In this project, identification of Glaucoma is carried out through computational techniques namely image processing. As the changes in the profile of optic disc act as a biomarker for the onset of the disease, optic disc is segmented through image processing techniques. Optic disc is the brightest part portrayed as oval structure in the retinal fundus image. It encompasses optic cup, which is the brightest central part, optic rim, the surrounding pale part and the blood vessels. All these structures are segmented and their properties are elicited. Then, properties of the disc, cup and blood vessels within optic disc are mined to design a learning model for prediction of Glaucoma.

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Matlab Code for Lung Cancer Detection Using Image Processing

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ABSTRACT
        Lung cancer prevalence is one of the highest of cancers, at 18 %. One of the first steps in lung cancer diagnosis is sampling of lung tissues or biopsy. These tissue samples are then microscopically analyzed. This procedure is taken once imaging tests indicate the presence of cancer cells in the chest. Lung cancer diagnosis using lung images. One of them is that doctor still relies on subjective visual observation. A medical specialist must do thorough observation and accurate analysis in detecting lung cancer in patients. Hence, there is need for a system that is capable for detecting lung cancer automatically from microscopic images of biopsy. This method will improve the accuracy and efficiency for lung cancer detection. The aim of this research is to design a lung cancer detection system based on analysis of microscopic image of biopsy using digital image processing. Microscopic images of biopsy are feature extracted and classified using support vector machine. This method is implemented to detection of lung cancer of lung samples.

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Matlab Code for Fake Currency Recognition Using Image Processing IEEE Project

         
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 Code for Brain Tumor Detection and Classification Using Image Processing

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ABSTRACT
          Brain is the kernel part of the body. Brain has a very complex structure. Brain is hidden from direct view by the protective skull. This skull gives brain protection from injuries as well as it hinders the study of its function in both health and disease. But brain can be affected by a problem which cause change in its normal structure and its normal behavior .This problem is known as brain tumor. Brain tumor causes the abnormal growth of the cells in the brain. The cells which supplies the brain in the arteries are tightly bound together thereby routine laboratory test are inadequate to analyze the chemistry of brain. Brain tumor diagnosis is a very crucial task. This system provides an efficient and fast way for diagnosis of the brain tumor. Proposed system consists of multiple phases. First phase consists of texture feature extraction from brain MR images. Second phase classify brain images on the bases of these texture feature using ensemble base classifier. After classification tumor region is extracted from those images which are classified as malignant or benign. Segmentation consists of tumor extraction phases. Quantitative results show that our proposed system performed very efficiently and accurately.

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Matlab Code for OMR Answer Sheet Evaluation

         
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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Matlab Code for Real Time Video Surveillance System

              
ABSTRACT
             A key technology to fight against terrorism and crime for public safety moving object detection and tracking is become very popular and one of the challenging research topic in various security areas of computer vision and video surveillance applications. Due to increase in criminal and terrorist activities, general social problems, providing the security to citizens, private places, public places has become more important. Therefore watch for 24*7 is required in area of automatic monitoring. The video surveillance system does this job as accurately as possible. The video surveillance system described here is interfacing of camera and alarm system with the computer. Here the video is taken from camera and the unwanted entities are identified using MATLAB. Security, Surveillance, General identity verification, Criminal justice systems, Image database investigations, Smart Card applications are important issues in today’s world. The recent acts of terrorism require urgent need for efficient surveillance. Now a day surveillance systems use digital video recording (DVR) cameras which play host to multiple channels and the major drawbacks with this model is that it requires continuous manual monitoring and cost of manual labour. It is virtually impossible to search through recordings for important events in the past since that would require a playback of the entire duration of video footage. Hence, there is a need for an automated system for video surveillance. 

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Matlab Code for Skin Cancer Detection Using Image Processing

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ABSTRACT
         Skin cancer – also known as malignant melanoma – is one of the deadliest form of cancer if not recognized in time. Since the pigmented areas/moles of the skin can be nicely observed by simple, non-invasive visual inspection (e.g. by a dermatoscope), the clinical protocols of its recognition also consider several visual features. Melanoma is the deadliest form of skin cancer, which is considered one of the most common human malignancies in the world. Early detection of this disease can affect the result of the illness and improve the chance of surviving. The tremendous improvement of deep learning algorithms in image recognition tasks promises a great success for medical image analysis, in particular, melanoma classification for skin cancer diagnosis. Activation functions play an important role in the performance of deep neural networks for image recognition problems as well as medical image classification. Melanin is the pigment that discerns the color of human skin. The special cells produce melanin in the skin. If these cells are damaged or unhealthy, skin discoloration is visible. Skin pigment discoloration on cheeks is a hazardous fact as a symptom of human skin disease with a possibility of losing natural beauty. The extracted information of the skin discoloration can work as a guide to diagnosis the disease. In this research, different imaging techniques like watershed method, edge detection and morphological operations are used to analyze and extract the information of cheek’s discoloration lesion by measuring the pixel number of lesion on skin. The image analyzing results are visually examined by the skin specialist.

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Heart Disease Detection using Matlab Project with Source Code

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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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Brain Tumor Detection Using Neural Network Matlab Code

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ABSTRACT
          The imaging plays a central role in the diagnosis of brain tumors. An efficient algorithm is proposed in this project for brain tumor detection based on digital image segmentation. Brain tumor may be considered among the most difficult tumors to treat, as it involves the organ which is not only in control of the body. We proposed an Artificial Neural Network Approach for Brain Tumor Detection, which gave the edge pattern and segment of brain and brain tumor itself. The segmentation of brain tumors in magnetic resonance images is a challenging and difficult task because of the variety of their possible shapes, locations, image intensities. In this project it is intended to summarize and compare the methods of automatic detection of brain tumor through Magnetic Resonance Image used in different stages of Computer Aided Detection System. Brain Image classification techniques are studied. Existing methods are classically divided into region based and contour based methods. These are usually dedicated to full enhanced tumors or specific types of tumors. The amount of resources required to describe large set of data is simplified and selected in for tissue segmentation. In this project image segmentation techniques were applied on input images in order to detect brain tumors. Also in this project a Neural Network model that is based on machine learning with image and data analysis and manipulation techniques is proposed to carry out an automated brain tumor classification.

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Matlab Code for Real Time Driver Drowsiness Detection

              
ABSTRACT
             Drivers who do not take regular breaks when driving long distances run a high risk of becoming drowsy and cause accidents. It is a state which they often fail to recognise early enough according to the experts. Studies show that around one quarter of all serious motorway accidents is attributable to sleepy drivers in need of a rest, meaning that drowsiness causes more road accidents than drink-driving. Driver fatigue is a significant factor in a large number of vehicle accidents. The development of technologies for detecting drowsiness at the wheel is a major challenge in the field of accident avoidance systems. Because of the hazard that drowsiness presents on the road, methods need to be developed for counteracting its affects. The main aim of this is to develop a drowsiness detection system by monitoring the eyes and mouth; it is believed that the symptoms of driver fatigue can be detected early enough to avoid a car accident. Detection of fatigue involves the observation of eye movements, blink patterns and mouth opening for yawning. The analysis of face images is a popular research area with applications such as face recognition, and human identification security systems. This project is focused on the localization of the eyes, which involves looking at the entire image of the eye, and determining the position of the eyes.

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Matlab Code for Real Time Face Recognition Using Image Processing

              
ABSTRACT
             The subject of face recognition is as old as computer vision because of the practical importance of the topic and theoretical interest from cognitive scientists. Despite the fact that other methods of identification (such as fingerprints, or iris scans) can be more accurate, face recognition has always remains a major focus of research because of its noninvasive nature and because it is people's primary method of person identification. This electronic document is about face detection. In computer literature face detection has been one of the most studied topics. Given an arbitrary image, the goal of this project is to determine real time face recognition. While this appears to be a trivial task for human beings, it is very challenging task for computers. The difficulty associated with face detection can be attributed to many variations in scale, location, view point, illumination, occlusions, etc. Although there have been hundreds of reports reported approaches for face detection, if one were asked to name a single face detection algorithm that has most impact in recent decades, it will most likely be the face detection, which is capable of processing images extremely rapidly and achieve high detection rates.

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Matlab Code for Fruit Disease Detection and Classification Using Image Processing

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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 Code for Image Steganography Using Image Processing

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ABSTRACT
          Steganography is the one type of powerful technique which is science & art in which we have to write hidden messages, or we hide some important images, audio files, videos in this way that no-one, can find a hidden message which exists in cover images. Steganography is most strong techniques to mask the existence of unseen secret data within a cover object. Actually Stego means "Cover" graphy means "writing" that means It is nothing but we are hiding secret objects in cover image in which medium is different types of images. In practical feasible implementation practical approach would be to make the algorithm as strong as possible. In steganographed images are the most powerful objects that means cover objects, and therefore importance of image steganographed which can Embedding secret information inside images requires systematic computations. Various metrics were used to judge imperceptibility of steganography. The metrics in Matlab indicates how similar or dissimilar the stego-image compares with Cover.

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Diabetic Retinopathy Detection Using Matlab Project Source Code

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ABSTRACT
                  Diabetes is a group of metabolic disease in which a person has high blood sugar.  Diabetic Retinopathy (DR) is caused by the abnormalities in the retina due to insufficient insulin in the body. It can lead to sudden vision loss due to delayed detection of retinopathy. So that Diabetic patients require regular medical checkup for effective timing of sight saving treatment.  This is continuous and stimulating research area for automated analysis of Diabetic Retinopathy in Diabetic patients. A completely automated screening system for the detection of Diabetic Retinopathy can effectively reduces the burden of the specialist and saves cost as well as time. Due to noise and other disturbances that occur during image acquisition Diabetic Retinopathy may lead to false detection and this is overcome by various image processing techniques. Further the different features are extracted which serves as the guideline to identify and grade the severity of the disease. Based on the extracted features classification of the retinal image as normal or abnormal is carried out.

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Video Steganography Using Matlab Project with Source Code

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ABSTRACT
            Information security has become the area of concern as a result of widespread use of communication medium over the internet. This paper focuses on the data security approach when combined with encryption and steganographic techniques for secret communication by hiding it inside the multimedia files. The high results are achieved by providing the security to data before transmitting it over the internet. The files such as images, audio, video contains collection of bits that can be further translated into images, audio and video. The files composed of insignificant bits or unused areas which can be used for overwriting of other data. This Project explains the proposed algorithm using video steganography for enhancing data security. The Steganography, Cryptography and Digital Watermarking techniques can be used to obtain security and privacy of data. The steganography is the art of hiding data inside another data such as cover medium by applying different steganographic techniques. While cryptography results in making the data human unreadable form called as cipher thus cryptography is scrambling of messages. Whereas the steganography results in exploitation of human awareness so it remains unobserved and undetected or intact. It is possible to use all file medium, digital data, or files as a cover medium in steganography. Generally steganography technique is applied where the cryptography is ineffective.

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Kidney Stone Detection Using Image Processing Matlab Project Code

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ABSTRACT
            Nowadays, kidney stone has become a major problem and if not detected at an early stage then it may cause complications and sometimes surgery is also needed to remove the stone. So, to detect the stone and that too precisely paves the way to image processing because through image processing there is a tendency to get the precise results and it is an automatic method of detecting the stone. This project presents a technique for detection of kidney stones through different steps of image processing. The first step is the image pre-processing using filters in which image gets smoothed as well as the noise is removed from the image. Image enhancement is a part of preprocessing which is used to enhance the image which is achieved with power law transformation. Next, the image segmentation is performed on the preprocessed image using thresholding technique. In this way Kidney stone detection done using image processing.

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Ulcer Disease Detection Using Image Processing Matlab Project Code

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ABSTRACT
            Ulcer is one of the most common indications of many serious diseases in the human digestive tract. Especially for ulcers in the small intestine where other methods may not display properly, capsule endoscopy (WCE) is increasingly used in the diagnosis and clinical management. Since WCE generates lots of images of the entire inspection process, computer-aided detection ulcer is considered an essential relief for clinicians. In this work, a CAD system is proposed for fully automated computer in two stages to detect images WCE ulcer. In the first step, a detection method based on the effective prominence super pixel multilevel outline representation candidates proposed ulcer. To find the perceptual and semantically meaningful salient regions, the first image segment in multilevel super pixel segmentation. Each level corresponds to different initial sizes of super pixels region. Then the corresponding prominence according to the characteristics of color and texture of each level super pixel region is evaluated.

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Image Watermarking Using DWT and DCT Matlab Project with Source Code

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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 paper we have compared watermarking using DWT & DWT-DCT methods performance analysis on basis of PSNR, Similarity factor of watermark and recovered watermark.

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Emotion Recognition from Facial Expression Using Image Processing Matlab Project Code

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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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Content Based Image Retrieval (CBIR) Matlab Project with Source Code

         
ABSTRACT
                  Advances in the data storage and image acquisition technologies have enabled the creation of large datasets. It is necessary to develop appropriate information systems to efficiently manage these collections. The most common approaches use Color-Based Image Retrieval (CBIR) system. The goal of CBIR system is to support image retrieval based on color. In a color based image retrieval system querying can be done by a query image. The goal is to find the images most resembling the query. In this Project we mainly focused on color histogram-based method. Color is most intuitive feature of an image and to describe colors generally histograms are adopted. Histogram methods have the advantages of speediness, low demand of memory space. Color features are the most important elements enabling human to recognize images. For categorizing images, color features can provide powerful information and they are used for image retrieval, so color based image retrieval is mostly used method. Color features of the images are generally represented by color histograms. Before using color histograms, however, we need to select and quantify a color space model and choose a distance metric.

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Matlab Code for Diabetic Retinopathy Detection Using Neural Network

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ABSTRACT
                  Diabetes is a group of metabolic disease in which a person has high blood sugar.  Diabetic Retinopathy (DR) is caused by the abnormalities in the retina due to insufficient insulin in the body. It can lead to sudden vision loss due to delayed detection of retinopathy. So that Diabetic patients require regular medical checkup for effective timing of sight saving treatment.  This is continuous and stimulating research area for automated analysis of Diabetic Retinopathy in Diabetic patients. A completely automated screening system for the detection of Diabetic Retinopathy can effectively reduces the burden of the specialist and saves cost as well as time. Due to noise and other disturbances that occur during image acquisition Diabetic Retinopathy may lead to false detection and this is overcome by various image processing techniques. Further the different features are extracted which serves as the guideline to identify and grade the severity of the disease. Based on the extracted features classification of the retinal image as normal or abnormal is carried out.

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Matlab Code for Brain Tumor Detection and Classification Using Image Processing

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ABSTRACT
          Brain tumors are the most common issue in children. Approximately 3,410 children and adolescents under age 20 are diagnosed with primary brain tumors each year. Brain tumors, either malignant or benign, that originate in the cells of the brain. The conventional method of detection and classification of brain tumor is by human inspection with the use of medical resonant brain images. But it is impractical when large amounts of data is to be diagnosed and to be reproducible. And also the operator assisted classification leads to false predictions and may also lead to false diagnose. Medical Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies classification. The use Support Vector Machine, Kmean and PCA shown great potential in this field. Principal Component Analysis gives fast and accurate tool for Feature Extraction of the tumors. Kmean is used for Image Segmentation. Support Vector Mahine with Training and Testing of Brain Tumor Images techniques is implemented for automated brain tumor classification.

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Mr. Roshan P. Helonde
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Matlab Code for Signature Recognition Using Image Processing Matlab Project Code

              
ABSTRACT
         The fact that the signature is widely used as a means of personal identification tool for humans require that the need for an automatic verification system. Verification can be performed either Offline or Online based on the application. However human signatures can be handled as an image and recognized using computer vision and neural network techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are various approaches to signature recognition with a lot of scope of research. Signature verification and recognition is a technology that can improve security in our day to day transaction held in society. This project presents a novel approach for offline signature verification. In this project signature verification using Image Processing is projected, where the signature is written on a paper are obtained using a scanner or a camera captured and presented in an image format. For authentication of signature, the proposed method is based on geometrical and statistical feature extraction and then the entire database. The extracted features of investigation signature are compared with the previously trained features of the reference signature. This technique is suitable for various applications such as bank transactions, passports with good authentication results etc.

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Mr. Roshan P. Helonde
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Matlab Code for Background Subtraction

         
ABSTRACT
         Background subtraction (BGS) is a commonly used technique for achieving this segmentation. Background subtraction is a widely used approach to detect moving objects from static and dynamic cameras. Many different methods have been proposed over the recent years and there are a number of object extraction algorithms proposed in this survey it has most efficiently constrained environments where the background is relatively easy and static. In this paper, we analysis most popular, state-of- the-art BGS algorithms and propose a neuro fuzzy model for determining thresholds, we examine how threshold algorithm poor their performance. Our method shows that threshold plays a major role in obtaining the foreground segmentation masks produced by a BGS algorithm and our experimental results demonstrate that neuro fuzzy system is much more accuracy and robust than existing system approaches.

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Fingerprint Recognition using Image Processing full Matlab Project with Source Code

              
ABSTRACT
        Recently, monitoring and security have become an essential and important affair because the number of counterfeiters and hacker are increased for the conventional methods like Personal Identification Number (PIN) and passwords. The traditional methods suffer from some type of contraventions and breaches for example the unauthorized user can arrive to important data in a dedicated system to delete, change, or even steal it. For averting whole these concerns; the modern community directs to more credibility methods recently utilize the biometric-technologies. Biometrics provides more secure way of person authentication, they are difficult to be stolen and replicated. Biometrics method can be depicted as an automate technique to recognize person automatically based on his or her behavioral and/or physiological features. This technology has possessed a big amount of concern and care for security in almost all aspects of our daily life since person cannot forget or lose their physiological features in the way that they might lose password or an identity card. Biometric technologies have been developed for automatic recognizing of human identity depending on person special biological features, such as face, Iris, speech and fingerprint. The online security of authentication systems is not only a substitution of secret codes and passwords, but it is also related to securing and monitoring the system in different level of potential applications. This project was analyzed and evaluation Uni-modal biometric system based on fingerprint identification system. The feature extraction was performed by using a popular texture feature which called Local Binary Pattern. The matching process was done by comparing the test with a template by using probabilistic neural networks. The decision was performing with help of threshold values to decide the person to identify or not identify. 

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Mr. Roshan P. Helonde
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Palmprint Recognition Using Image Processing Matlab Project with Source Code

              
ABSTRACT
                 Palm  print  authentication  is  one  of  the  modern  bio-metric techniques, which employs the vein pattern  in  the  human palm  to  verify  the  person.  The merits  of  palm  vein  on classical  bio-metric  (e.g.  fingerprint,  iris,  face)  are  a  low risk  of  falsification,  difficulty  of  duplicated  and  stability. In  this  Project,  a  new  method  is  proposed  for  personal verification  based  on  palm  Print  features.  In  the propose method,  the  palm  vein  images  are  firstly  enhanced  and then  the  features  are extracted  by  using  bank  of  Gabor filters. Bio-metric   technology   refers   to   a pattern   recognition system  which  depends  on  physical  or  behavioral  features for the  person  identification.

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Matlab Code for Image Compression Using EZW Embedded Zerotree Wavelet Full Matlab Project with Source Code

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ABSTRACT
          Recently, the wavelet transform has emerged as a cutting edge technology, within the field of image compression research. Wavelet methods involve overlapping transforms with varying-length basis functions. This overlapping nature of the transform alleviates blocking artifacts, while the multi-resolution character of the wavelet decomposition leads to superior energy compaction and perceptual quality of the decompressed image. Embedded Zero-tree wavelet (EZW) coder is the first algorithm to show the full power of wavelet-based image compression. The main purpose of this project is to investigate the impact and quality of wavelet for EZW. Meanwhile, we also look into the effect of the level of wavelet decomposition towards compression efficiency. The compression simulations are done on few modalities of images. The qualitative and quantitative results of these simulations are presented. 

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Matlab Code Diabetic Retinopathy Detection Using Image Processing full Matlab Project Code

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ABSTRACT
        Diabetic Retinopathy is one of the leading impairing chronic diseases and one of the leading causes of preventable blindness in the world. Most of the ophthalmologist depends on the visual interpretation for the identification of Diabetic Retinopathy. But incorrect diagnosis will change the course of treatment planning which leads to fatal results. Hence there is a requirement for a impartial automated system which gives highly accurate results. Early diagnosis of diabetic retinopathy enables timely treatment. To achieve this a major effort will have to be invested into automated screening programs. For automatic screening programs to work vigorously efficient image processing and analysis algorithms have to be developed. Nowadays, Scanning laser ophthalmoscopes (SLOs) can be used for detection of retinal diseases. In this project, we propose a novel framework for the extraction of retinal area of input images and diagnosis of retinal diseases from the retinal area using Support vector machine. The proposed method analyze the retinal images for important features of diabetic retinopathy using image processing techniques and an image classifier based on SVM which classify the images conforming to disease conditions. The features were extracted for classification Process by GLCM.

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