Video Steganography Hiding Image in Video Matlab Project Source Code

ABSTRACT

          Steganography has become great area of interest for researchers as need for secure transaction of information is increasing day by day. Information may be text, image, audio or video. Steganography is a technique in which required information is hided in any other information such that the second information does not change significantly and it appears the same as original. This paper presents a novel approach of hiding image in a video. The proposed algorithm is replacing one LSB of each pixel in video frames. It becomes very difficult for intruder to guess that an image is hidden in the video as individual frames are very difficult to analyze in a video running at 30 frames per second. The process of analysis has been made more difficult by hiding each row of image pixels in multiple frames of the video, so intruder cannot even try to unhide image until he get full video.

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Rice 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 Rice leafs. Rice Diseases Classification comprises of two steps: first one is Detection, Extraction and Segmentation of diseases. Secondly, Feature extraction, Classification 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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Diabetic Retinopathy Detection using Convolutional Neural Network CNN Matlab Project Source Code

ABSTRACT

            Diabetic Retinopathy (DR) is one of the major causes of blindness in the western world. Increasing life expectancy, indulgent lifestyles and other contributing factors mean the number of people with diabetes is projected to continue rising. Regular screening of diabetic patients for DR has been shown to be a cost-effective and important aspect of their care. The accuracy and timing of this care is of significant importance to both the cost and effectiveness of treatment. If detected early enough, effective treatment of DR is available; making this a vital process. The diagnosis of diabetic retinopathy (DR) through colour fundus images requires experienced clinicians to identify the presence and significance of many small features which, along with a complex grading system, makes this a difficult and time consuming task. In this project , we propose a CNN approach to diagnosing DR from digital fundus images and accurately classifying its severity. We develop a network with CNN architecture and data augmentation which can identify Diabetic Retinopathy.

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Matlab Project on Lung Cancer Detection using Neural Network Full Source Code

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. Neural Network method is implemented here to detection of lung cancer of lung samples.

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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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Mr. Roshan P. Helonde

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Matlab Code for Fake Biometric Recognition using Image Processing

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

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 Marathi Character Recognition using neural Network

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 Maize Plant Disease Detection using Image Processing

ABSTRACT

            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. This study infiltrates through the facilitated principles of the Support Vector Machine (SVM) in order to model a network for image recognition and classification of these diseases. SVM network that recognised and classified images of the maize leaf diseases that were collected by acquisition process. 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 three different types of maize leaf diseases out of healthy leaves.

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Matlab Code for Breast Cancer Detection using Image Processing

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. 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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Mr. Roshan P. Helonde

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