Matlab Code for Mango Plant Disease Detection Using Neural Network

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ABSTRACT
            The mango fruit is popular because of its wide range of adaptability, high nutritional value, different variety, delicious taste and excellent flavor. The fruit contains vitamin A and vitamin C in a rich extent. The crop is prone to diseases like Powdery mildew, Anthracnose, Red Rust, Golmich, etc. Disorders may also impact the plant in the absence of effective case and control measures. These include change of form, biennial bearing, fall of fruit, black top, clustering, etc. The farmer must consult and take professional support for the prevention / control of diseases and crop disorder. New techniques of detecting mango disease are required to promote better control to avoid this crisis. By considering this, project describes image recognition which provides cost effective and scalable disease detection technology. Paper further describes new deep learning models which give an opportunity for easy deployment of this technology.

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Marathi Character Recognition using Neural Network Matlab Project Source Code

         
ABSTRACT
          Marathi character can be converted in to the digital information using Marathi character Recognition, which is the ability of a computer to receive and interpret handwritten input from documents. Marathi Characters are more complex for recognition due to presence of header line, conjunct characters and similarity in shapes of multiple characters. For Marathi Character recognition using neural network various approaches has been proposed. In general the process involves phases as: Scanning, Pre-processing, Feature Extraction and Recognition. Preprocessing includes noise reduction and normalisation Feature extraction includes extracting some useful information out of the pre-processed image in the form of a feature vector. Artificial neural network is used for classification. 

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Matlab Code for Signature Verification on Bank Cheque using Image processing

              
ABSTRACT
            The area of Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine produced by the claimed individual, or a forgery produced by an impostor. This has demonstrated to be a challenging task, in particular in the offline static scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application is to learn feature representations from signature images. In this project, we present how the problem has been handled in the past few decades, analyse the recent advancements in the field, and the potential directions for future research.

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Matlab Code for RBC and WBC Detection using Image Processing

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         Detection and Counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs) and red blood cells (RBCs) in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs. The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells.

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Matlab Code for Fake Biometric Recognition using Image Processing || 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. 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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Text Image Watermarking using Image Processing Matlab Project code

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ABSTRACT
            Multimedia security is a major issue. Images, video, audio, text files are losing their credibility day by day as they can be distorted or manipulated by using several tools. Ensuring the authenticity and integrity of digital media is a major issue. The manipulation made by forgery tools are so smoothly done that we don’t even suspect that forgery may be involved in digital content. Multimedia data is facing several issues related to illegal distribution, duplication and manipulation of information conveyed by them. The digital watermarking technique plays an important role in protecting digital content. In this project, On the basis of their operating principles different watermarking techniques are categorised. Attacks, applications and requirements related to watermarking techniques are also discussed. Different watermarking techniques proposed by researchers for protecting copyrights of digital media are presented which are based on spatial and frequency domain. Frequency domain are getting much more attention due to use of wavelets which
have high degree of resemblance to human visual system. In digital watermarking, secret information is embedded with original data for maintaining ownership rights of the digital content. Spatial domain watermarking techniques work over pixel characteristics and frequency domain watermarks concerned about different transformations that can be used with digital content. Imperceptibility, robustness, security, complexity and capacity are some requirements of the digital watermarking which completely depends on the algorithm used for watermarking.

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Matlab Code for Breast Cancer Detection using Image Processing

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              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. 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). 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 Code for Rust Defect Detection using Image Processing

         
ABSTRACT
               In this project we propose a methodology for assessment of coating condition of bridge images. The defect recognition algorithm includes conversion of captured images into grey level; these grey level images are grouped into defective & non defective group. Further that is processed to plot correspondence map. The correspondence map is measure of matching image. Straight line with 450 in correspondence map indicates no defect in scene image. In contrast if correspondence map produces nonlinear image it indicates defect (rust) in scene image. The nonlinear shape of grey level distribution in correspondence map can be analyzed by calculating Eigen values. Two similar images will produce smaller Eigen value (approximately zero), whereas it will be distinctly large for dissimilar images.

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Matlab Code for Corn Disease Detection using Image Processing

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            Plant leaf diseases can affect plant leaves to a certain extent that the plants can collapse and die completely. These diseases may drastically decrease the supply of vegetables and fruits to the market, and result in a low agricultural economy. In the literature, different laboratory methods of plant leaf disease detection have been used. These methods were time consuming and could not cover large areas for the detection of leaf diseases. A novel way of training and methodology was used to expedite a quick and easy implementation of the system in practice. The developed model was able to recognise different types of Corn leaf diseases out of healthy leaves, Corn Leaf Blight (Exserohilum), Common Rust (Puccinia Sorghi) and Corn Leaf Spot (Cercospora) diseases were chosen for this study as they affect most parts of Corn Plant.

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Matlab Code for Handwritten Character Recognition using Image Processing

         
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. We tried to recognise 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 normalisation of scanned image, rendering image suitable for segmentation where image is decomposed into sub images. Feature Extraction improves recognition rate and mis classification.

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Matlab code for Hand Gesture Recognition using image processing

         
ABSTRACT
              Sign language is the basic communication method for those who suffer from hearing impairment. The primary component of a sign language is hand gestures. Gesturing is an instinctive way of communication to present a specific meaning. Sign language is the visual manual modality to convey meaning which is quite similar to the hand gestures. Language is expressed via the manual sign-stream in combination with non-manual elements. Sign languages are full-fledged natural languages with their own grammar and lexicon. Gesture is a distinct form of sign language which involves movement of body such as hands or face to express the meaning. Hand gesture has received a greater importance over the last few years because to remove the barrier of communication between mute people and normal people. It is an object consists of distinct features to extract and recognise the gestures or signs exactly, therefore gesture recognition presents a most difficult and challenging tasks in the fields of image processing, computer vision and image analysis. The images are subjected to image processing steps.  In order to achieve a better accuracy the image processing and machine learning techniques are used.

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Paddy Leaf Disease Detection using image processing matlab project with source code

         
ABSTRACT
           Agriculture is the main backbone for most of the developing/developed countries; agriculture production itself is the main feed for ever growing populations and it is the major source of income for the rural people/farmers especially in India. In India farmers are called “the backbone of India”. The main aim of the proposed system is to detect and classify the diseases in paddy leafs. Paddy Diseases Classification comprises of two steps: first one is Detection, Extraction and Segmentation of diseases. Secondly, Feature extraction, Classification and Grade the level of disease by using Support Vector Machine (SVM) classifiers respectively. The proposed system has been experimentally tested for our own dataset and results achieved are encouraging.

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Matlab code for Fake Currency Detection using Image Processing

         
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 Finger Nail Disease Detection using Image Processing

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ABSTRACT
          This project gives idea to predict diseases using the colour of the nail at early stage of diagnosis. The main aim of our project is to analyze the disease without causing harm to humans. In earlier traditional system of disease detection, doctors observe the nails of patients and will predict the disease. Many diseases can be identified by analyzing nails of patients .But it is difficult for human eyes to differentiate the slight changes in colour. So it is less accurate and time consuming. Our proposed system can be quite useful to overcome this issue since it is fully computer based. The input to the proposed system is image of nail. The system will process the nail image and will extract the nail’s features to diagnose the disease. Human nail consist of various features, our proposed system uses nail color changes to diagnose the disease. Here, first training set data is prepared from nail images of patients with specific diseases. This training data set is compared with extracted feature from input nail image to obtain the result. In our experiment, we found that training set data are correctly matched with color feature of nail image results. It is focused on the system of image recognition on the basis of color analysis. The proposed system is based on the algorithm which automatically extracts only nails area from scanned back side of palm (Region of Interest). These selected pixels are processed for further analysis using median filters. The system is fully computer based, so even small discontinuities in color values are observed, and we can detect color changes in the initial stage of disease.

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Rain Removal using Image Processing Matlab Project with Source Code

         
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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Matlab Code for Blood Cancer (Leukemia Cancer) Detection using Image Processing

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ABSTRACT
        Blood cancer is the most prevalent and it is very much dangerous among all type of cancers. Early detection of blood cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose blood cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the technician. To obviate these problems, image processing techniques is use in this study as promising modalities for detection of different types of blood cancer. The accuracy rate of the diagnosis of blood cancer by using the image processing will be yield a slightly higher rate of accuracy then other traditional methods and will reduce the effort and time. We first discuss the preliminary of cell biology required to proceed to implement our proposed method. This project presents a new automated approach for blood Cancer detection and analysis from a given photograph of patient’s cancer affected blood sample. The proposed method is using image improvement, image segmentation for segmenting the different cells of blood, edge detection for detecting the boundary, size, and shape of the cells and finally Clustering then final decision of blood cancer based on the number of different cells.

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Matlab Code for Plant Disease Detection & Classification using Neural Network

         
ABSTRACT
            The detection and classification of plant diseases are the crucial factors in plant production and the reduction of losses in crop yield. This paper proposes an approach for leaf disease detection and classification on plants using image processing. The algorithm presented has three basic steps: Image Pre-processing and analysis, Feature Extraction and Recognition of plant disease. The plant disease diagnosis is restricted by person’s visual capabilities as it is microscopic in nature. Due to optical nature of plant monitoring task, computer visualization methods are adopted in plant disease recognition. The aim is to detect the symptoms of the disease occurred in leaves in an accurate way. Once the captured image is pre-processed, the various properties of the plant leaf such as intensity, color and size are extracted and sent to with Neural Network for classification. 

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Blood Group Determination Using Image Processing Matlab Project with Source Code

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ABSTRACT
          Determining of blood types is very important during emergency situation before administering a blood transfusion. Presently, these tests are performed manually by technicians, which can lead to human errors. Determination of the blood types in a short period of time and without human errors is very much essential. A method is developed based on processing of images acquired during the slide test. The image processing techniques such as Pre-processing, Segmentation, Thresholding, Morphological operations and Support Vector Machine are used. The images of the slide test are obtained from the pathological laboratory are processed and the occurrence of agglutination are evaluated. Thus the developed automated method determines the blood type using image processing techniques. The developed method is useful in emergency situation to determine the blood group without human error. The slide test consists of the mixture of one drop of blood and one drop of reagent, being the result interpreted according to the occurrence or not of agglutination. The combination of the occurrence and non occurrence of the agglutination determines the blood type of the patient. 

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Bone Fracture Detection using Image Processing Matlab Project with source Code

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ABSTRACT
         Analysis of medical images plays a very important role in clinical decision making. For a long time it has required extensive involvement of a human expert. However, recent progress in data mining techniques, especially in machine learning, allows for creating decision models and support systems that help to automatise this task and provide clinicians with patient-specific therapeutic and diagnostic suggestions. In this project, we describe a study aimed at building a decision model (a classifier) that would predict the type of treatment (surgical vs. non-surgical) for patients with bone fractures based on their X-ray images. We consider two types of features extracted from images (structural and textural) and used them to construct multiple classifiers that are later evaluated in a computational experiment. Structural features are computed by applying the Hough transform, while textural information is obtained from Gray-level occurrence matrix. In research reported by other authors structural and textural features were typically considered separately. Our findings show that while structural features have better predictive capabilities, they can benefit from combining them with textural ones.

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Audio Steganography for Data Hiding Matlab Project with Source Code

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ABSTRACT
          Information security is one of the most important factors to be considered when secret information has to be communicated between two parties. Cryptography and steganography are the two techniques used for this purpose. Cryptography scrambles the information, but it reveals the existence of the information. Steganography hides the actual existence of the information so that anyone else other than the sender and the recipient cannot recognize the transmission. In steganography the secret information to be communicated is hidden in some other carrier in such a way that the secret information is invisible. In this project an audio steganography technique is proposed to hide audio signal in image in the transform domain using wavelet transform. The audio signal in any format wav is encrypted and carried by the image without revealing the existence to anybody. When the secret information is hidden in the carrier the result is the stego signal. In this work, the results show good quality stego signal and the stego signal is analysed for different attacks. It is found that the technique is robust and it can withstand the attacks. The quality of the stego signal is measured by Peak Signal to Noise Ratio (PSNR), Mean Square Error. The quality of extracted secret audio signal is measured by Signal to Noise Ratio (SNR), Squared Pearson Correlation Coefficient (SPCC). The results show good values for these metrics.

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Brain Tumor Detection using Rough Set Theory Algorithm Matlab Project with Source Code

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ABSTRACT
              Brain tumor is a life threatening disease and its early detection is very important to save life. The tumor region can be detected by segmentation of brain Magnetic Resonance Image (MRI). Once a brain tumor is clinically suspected, radiologic evaluation is required to determine the location, the extent of the tumor, and its relationship to the surrounding structures. This information is very important and critical in deciding between the different forms of therapy such as surgery, radiation, and chemotherapy. The segmentation must be fast and accurate for the diagnosis purpose. Manual segmentation of brain tumors from magnetic resonance images is a tedious and time-consuming task. Also the accuracy depends upon the experience of expert. Hence, the computer aided automatic segmentation has become important. MRI scanned images offer valuable information regarding brain tissues. MRI scans provide very detailed diagnostic pictures of most of the important organs and tissues in our body. It is generally painless and noninvasive. It does not produce ionizing radiation. So MRI is one of the best clinical imaging modalities. Several automated segmentation algorithms have been proposed. But still segmentation of MRI brain image remains as a challenging problem due to its complexity and there is no standard algorithm that can produce satisfactory results. The aim of this research work is to propose and implement an efficient system for tumor detection and classification. The different steps involved in this work are image pre-processing for noise removal, feature extraction, segmentation and classification.

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Face Recognition Based Attendance Maintenance Matlab Project with Source Code

              
ABSTRACT
               In this project we are proposing an automatic attendance system which can be used in every organisation to mark the attendance of employees. The main application of Automatic attendance system is seen in teaching institutions, where the attendance of students has to be regularly monitored on daily basis. The method developed provides a secure and effective may recording attendance. Automatic face recognition (AFR) technologies have made many improvements in the changing world. Smart Attendance using Real-Time Face Recognition is a real-world solution which comes with day to day activities of handling student attendance system. Face recognition-based attendance system is a process of recognizing the students face for taking attendance by using face biometrics based on high - definition monitor video and other information technology. In my face recognition project, a computer system will be able to find and recognize human faces fast and precisely in images or videos that are being captured through a surveillance camera.

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Matlab Code for Osteoporosis Detection using Image Processing Matlab Project with source code

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ABSTRACT
               Osteoporosis is a progressive bone disease that is characterized by a decrease in bone mass and density which can lead to an increased risk of fracture. Osteoporosis is a state of having brittle and fragile bone which arises due to vitamin deficiency, tissue loss, hormonal changes. Osteoporosis can be efficiently detected by calculating various features like Bone mineral density (BMD), statistical features from various trabecular region such as hip, toe, elbow, etc. Detection of bone disorders are done with the help of bone densitometer. The bone densitometer uses a technique that the bone density can be measured in-terms of Tscore. Osteoporosis is a condition in which the bone becomes porous and fragile due loss in bone mineral density and gets more susceptible to fracturing. osteopenia refers to early signs of bone loss that can turn to osteoporosis. Both osteoporosis and osteopenia are increasingly found in aging women who have attained their menopause. The symptoms of osteoporosis include pain in the bones, or lower back, bone fracture and
loss of height over a course of time.

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Traffic Sign Recognition using Image Processing Matlab Project Code

         
ABSTRACT
          The main objective of this project is to develop an algorithm so that we can automatically recognise traffic signs. This work uses basic image processing technique for automatically recognising two different traffic signs- stop sign and yield sign. The proposed method detects the location of the sign in the image, based on its geometrical characteristics and recognizes it using colour information. Firstly thresholded on RGB domain to separate out the regions with red color, which is those traffic signs usually have, then we do region mapping due to which the rest of the parts which are too small or too large are removed since they are unlikely to be a traffic sign. Here we get the signs whose shapes are octagon or triangular thus major axis to minor axis ratio is one. Hence the regions which are very large are eliminated.

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Blood Group Detection Using Image Processing Matlab Project Code

           ,    
ABSTRACT
          Determining of blood types is very important during emergency situation before administering a blood transfusion. Presently, these tests are performed manually by technicians, which can lead to human errors. Determination of the blood types in a short period of time and without human errors is very much essential. A method is developed based on processing of images acquired during the slide test. The image processing techniques such as thresholding and morphological operations are used. The images of the slide test are obtained from the pathological laboratory are processed and the occurrence of agglutination are evaluated. Thus the developed automated method determines the blood type using image processing techniques. The developed method is useful in emergency situation to determine the blood group without human error. The slide test consists of the mixture of one drop of blood and one drop of reagent, being the result interpreted according to the occurrence or not of agglutination. The combination of the occurrence and non occurrence of the agglutination determines the blood type of the patient. 

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Matlab Code for Vegetable Plant Recognition using Image Processing IEEE Project

              
ABSTRACT
            Recognising plants is a vital problem especially for biologists, agricultural researchers, and environmentalists. Plant recognition can be performed by human experts manually but it is a time consuming and low-efficiency process. Automation of plant recognition is an important process for the fields working with plants. This project presents an approach for plant recognition using leaf images. In this study, the proponents demonstrated the development of the system that gives users the ability to identify vegetables based on photographs of the leaves taken with a high definition camera.  At the heart of this system is a modernise process of identification, so as to automate the way of identifying the vegetable plants through leaf image and digital image processing. The system used the pre-processing, Segmentation, feature extraction and classification to acquire the physical parameter of the leaves. The output parameters are used to compute well documented metrics for the statistical and shape. Base on the study, the following conclusion are drawn: The system can extract the physical parameters from the leaf’s image that will be used in identifying Vegetable`s. From the extracted leaf parameters, the system provides the statistical analysis and general information of the identified leaf. 

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Matlab Code for Signature Recognition & Verification using Image Processing IEEE Project

              
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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Prof. Roshan P. Helonde
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Matlab code for Image Compression Using EZW 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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Mr. Roshan P. Helonde
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Email: roshanphelonde@rediffmail.com
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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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Mr. Roshan P. Helonde
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Email: roshanphelonde@rediffmail.com
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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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Email: roshanphelonde@rediffmail.com
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