The multi-focus image fusion method can fuse more than one focused image to generate a single image with more accurate description. The purpose of image fusion is to generate one image by combining information from many source images of the same scene. In this project, a multi-focus image fusion method is proposed with a hybrid pixel level obtained in the spatial and transform domains. The proposed method is implemented on multi-focus source images. The proposed method performance is evaluated in terms of PSNR, SNR and SSIM. The results show that the fusion quality of the proposed algorithm is better than obtained by several other fusion methods.
Leukemia 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 Leukemia blood cancer. The accuracy rate of the diagnosis of blood cancer by using 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 and final decision of blood cancer using Convolutional Neural Network (CNN) .
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, classify the diseases and suggest pesticide to recover from disease in paddy leafs. Paddy leaf Diseases Classification done using Convolutional Neural Network (CNN) classifiers and then suggesting pesticide respectively. The proposed system has been experimentally tested for our own dataset and results achieved are encouraging.
Blood cells are important parts of our immune system and they protect our body against infections by eliminating viruses, bacteria, parasites and fungi. Types of blood cell are Lymphocytes, Monocytes, Eosinophils and Neutrophils. In this project a computer-aided automated system used that can easily identify and locate blood cell types in blood images has been proposed. Diseases such as bordetella pertussis, hepatitis, viruses, brucella, leukemia increase lymphocytes in the blood whereas diseases such as HIV, rubeola, poliovirus, chickenpox, tuberculosis reduce the amount of lymphocytes. Listeriosis and malaria as well as bacterial and viral infections are some of the diseases that increase the number of monocytes. Allergic diseases, atopic diseases and parasites are factors that increase eosinophil value. Neutrophils show an increase in blood in cases of hormonal causes, metabolic disorders, hemolysis and bleeding. In addition; bacteria, fungi, exotoxin and endotoxin also cause the increase of neutrophils. For classification of these types of blood cells we have used convolutional neural networks (CNN).
The appearance of the currency is part of this development and it is affected directly, where there is exploited in incorrect form by copying the currency in a manner similar to the reality. Therefore, it became necessary to implement a proposal for being a suitable as solution not inconsistent with the different cultures, time and place. This clear through add the watermarks inside currency, which is difficult to be copied. At the same time, this watermarks may be visible to the naked eye so can easily inferred or it is invisible. However the high resolution imaging devices can copy these additions. In this research, we have proposed a system to distinguish the currencies by the program that working a submission inferred to the watermark by feature extraction determined the type of currency. In addition to, it determined category of the currency. Benefit of it, is reducing as much as possible the spread of counterfeit currency and this system can be used by any user wants to make sure of the currency.
Leukemia 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 Leukemia blood cancer. The accuracy rate of the diagnosis of blood cancer by using 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 for final decision of blood cancer based on the number of different cells.
Lung nodule prevalence is one of the highest of cancers, at 18 %. One of the first steps in lung nodule 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 nodule cells in the chest. Lung nodule 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 nodule in patients. Hence, there is need for a system that is capable for detecting lung nodule automatically from microscopic images of biopsy. This method will improve the accuracy and efficiency for lung nodule detection. The aim of this research is to design a lung nodule 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 nodule of lung samples.
Reversible Data Hiding (RDH) techniques have gained popularity over the last two decades, where data is embedded in an image in such a way that the original image can be restored. Earlier works on RDH was based on the Image Histogram Modification that uses the peak point to embed data in the image. More recent works focus on the Difference Image Histogram Modification that exploits the fact that the neighboring pixels of an image are highly correlated and therefore the difference of image makes more space to embed large amount of data. In this project we propose a framework to increase the embedding capacity of reversible data hiding techniques that use a difference of image to embed data. The main idea is that, instead of taking the difference of the neighboring pixels, we rearrange the columns (or rows) of the image in a way that enhances the smooth regions of an image. Any difference based technique to embed data can then be used in the transformed image. The proposed method is applied on different types of images including textures, patterns and publicly available images.
Optical character recognition (OCR) is becoming a powerful tool in the field of Character Recognition, now a days. In the existing globalized environment, OCR can play a vital role in different application fields. Basically, OCR technique converts images into editable format. This technique converts images in the form of documents such as we can edit, modify and store data more safely for longtime. This paper presents basic of OCR technique with its components such as pre-processing, Feature Extraction, Classification, post-processing etc. There are various techniques have been implemented for the recognition of character. This Review also discusses different ideas implemented earlier for recognition of a character. This paper may act as a supportive material for those who wish to know about OCR. Now a days, globalization is reaching to a great level. In this globalized environment, character recognition techniques also getting a valuable demand in number of application areas. OCR is an effective technique which converts image into suitable format such that data can be edit, modify and stored. This technique performs several operations such as, scans the input image, processes over the scanned image thereby image gets converted into portable formats .For instance, the hard copy of old historical books, novels, etc. .cannot be stored safely for a long time. Rather, its safety has limitations. If we apply OCR technique for such cases, the different historical documents can be stored, modified for a longtime. OCR also having variety of applications in almost all fields, including security. OCR implementation helps us to edit, store and process over the scanned data more effectively. User can handle the stored data whenever he wants with the internet support. So Optical character recognition is most successful application used in pattern recognition.
India is an agricultural country and soybean production is one of the major sources of earning. Due to the major factors like diseases, pest attacks, and sudden changes in the weather condition, the productivity of the soybean crop decreases. Automatic detection of soybean plant diseases is essential to detect the symptoms of soybean diseases as early as they appear on the growing stage. This project proposed a methodology for the analysis and detection of soybean plant leaf diseases using recent digital image processing techniques. In this project, experimental results demonstrate that the proposed method can successfully detect and classify the major soybean diseases like Soybean Rust, Powdery Mildew, Frogeye Leaf Spot, Downy Mildew, etc.
Emotion Recognition is a recent research topic in the field of Human Computer Interaction Intelligence and mostly used to develop wide range of applications such as stress management for call centre employee, and learning & gaming software, In E-learning field, identifying students emotion timely and making appropriate treatment can enhance the quality of teaching. Main aim of HCI is to achieve a more natural interaction between machine and humans. HCI is an emerging field using which we can improve the interactions between users and computers by making computers more respond able to the user’s needs. Today’s HCI system has been developed to identify who is speaking or what he/she is speaking. If in the HCI system, the computers are given an ability to detect human emotions then they can know how he/she is speaking and can respond accurately and naturally like humans do. In this project methodology for emotion recognition from speech signal is presented. Here, some of acoustic features are extracted from speech signal to analyze the characteristics and behavior of speech. The system is used to recognize the basic emotions. It can serve as a basis for further designing an application for human like interaction with machines through natural language processing and improving the efficiency of emotion. In this format, energy, Mel Frequency Cepstral Coefficients (MFCC) has been used for feature extraction from the speech signal. Support Vector Machine (SVM) are used for recognition of emotional states. English datasets are used for analysis of emotions with SVM Kernel functions.
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.
Due to the rapid rise of telemedicine, a lot of patients’ information will be transmitted through the Internet. However, the patients’ information is related to personal privacy, therefore, patients’ information needs to be encrypted when transmitted and stored. Medical image encryption is a part of it. Due to the informative fine features of medical images, a common image encryption algorithm is no longer applied. Common encryption algorithm has a single theory based on chaos image encryption algorithm, other encryption algorithms are based on information entropy. However, the images processed with these cipher text encryption algorithm are cyclical, the outline is clear and the anti-tamper capability is not strong. In view of the bit being the smallest measure unit of pixel, in order to overcome the weakness from above algorithm, and take the advantage of the chaotic system, this project present a chaotic medical image encryption algorithm based on bit-plane decomposition.
Steganography is one of the methods of secret communication that hides the existence of message so that a viewer cannot detect the transmission of message and hence cannot try to Extract it. It is the process of embedding secret data in the cover Audio without significant changes to the cover audio. These algorithms keep the messages from stealing, destroying from unintended users on the internet and hence provide security. In this project we perform Audio Steganography Hiding Secret audio In Cover audio using matlab.
