Signature Recognition & Verification using Image Processing Matlab Project Code 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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Mr. Roshan P. Helonde

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

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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Gender Recognition 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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Real Time Shape Detection and Recognition Using Image Processing Matlab Project with Source Code

ABSTRACT

                    Doing image processing and especially blob analysis it is often required to check some objects' shape and depending on it perform further processing of a particular object or not. For example, some applications may require finding only circles from all the detected objects, or quadrilaterals, rectangles, etc. Human vision seems to make use of many sources of information to detect and 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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Mr. Roshan P. Helonde

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Matlab Code for Lung Cancer Detection using Image Processing IEEE Project

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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Mr. Roshan P. Helonde

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Matlab Code for Brain Tumor Detection and Classification Using Image Processing IEEE Project

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 behaviour .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 analyse 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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Mr. Roshan P. Helonde

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Matlab code for Fake Currency Recognition Using Image Processing Matlab Project with Source Code

ABSTRACT

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

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Mr. Roshan P. Helonde

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OMR Answer Sheet Evaluation Matlab Project with Source Code IEEE Project

ABSTRACT

           This project aims to develop Image processing based Optical Mark Recognition sheet scanning system. Today we find that lot of competitive exams are been conducted as entrance exams. These exams consists of MCQs. The students have to fill the right box or circle for the appropriate answer to the respective questions. During the inspection or examining phase normally a stencil is provided to the examiner to determine the right answer to the questions. This is a manual process and a lot of errors can occur in the manual process such as counting mistake and many more. To avoid this mistakes OMR system is used. In this system OMR answer sheet will be scanned and the scanned image of the answer sheet will be given as input to the software system. Using Image processing we will find the answers marked to each of the questions. Summation of the marks & displaying of total marks will be also implemented. The implementation is done using Matlab

        In today’s modern world of technology when everything is computerized, the Evaluation exercise of examining and assessing the educational system has become absolute necessity. Today, more emphasis is on objective exam which is preferred to analyze scores of the students since it is simple and requires less time in the examining objective answer-sheet as compared to the subjective answer-sheet. This project proposes a new technique for generating scores of multiple-choice tests which are done by developing a technique that has software based approach with computer & scanner which is simple, efficient & reliable to all with minimal cost. Its main benefit to work with all available scanners, In addition no special paper & colour required for printing for marksheet. To recognize & allot scores to the answer marked by of the student’s.

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Mr. Roshan P. Helonde

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Anemia Diseases Detection Present in Blood Cells Smear Using Image Processing Matlab Project with Source Code

ABSTRACT

          Anemia is a blood disorder which results from the abnormalities of red blood cells and shortens the life expectancy to 42 and 48 years for males and females respectively. It also causes pain jaundice, shortness of breath, etc. Anemia is characterized by the presence of abnormal cells like sickle cell, ovalocyte, anisopoikilocyte. Sickle cell disease usually presenting in childhood, occurs more commonly in people from parts of tropical and subtropical regions where malaria is or was very common. A healthy RBC is usually round in shape. But sometimes it changes its shape to form a sickle cell structure; this is called as sickling of RBC. Majority of the sickle cells (whose shape is like crescent moon) found are due to low haemoglobin content. An image processing algorithm to automate the diagnosis of sickle-cells present in thin blood smears is developed. Images are acquired using a charge-coupled device camera connected to a light microscope. Clustering based segmentation techniques are used to identify erythrocytes (red blood cells) and Sickle-cells present on microscopic slides. Image features based on colour, texture and the geometry of the cells are generated, as well as features that make use of a priori knowledge of the classification problem and mimic features used by human technicians. The proposed image processing based identification of sickle-cells in anemic patient will be very helpful for automatic, sleek and effective diagnosis of the disease.

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Mr. Roshan P. Helonde

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Ulcer Disease Detection Using Image Processing Matlab Project with Source Code

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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Mr. Roshan P. Helonde

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