2018 ~ Low Price Projects

Image Compression Using SPIHT Techniques Matlab Code

ABSTRACT

In recent years there has been an astronomical increase in the usage of computers for a variety of tasks. With the advent of digital cameras, one of the most common uses has been the storage, manipulation, and transfer of digital images. The files that comprise these images, however, can be quite large and can quickly take up precious memory space on the computer’s hard drive. In multimedia application, most of the images are in color and color images contain lot of data redundancy and require a large amount of storage space. Set partitioning in hierarchical trees (SPIHT) is wavelet based computationally very fast and among the best image compression based transmission algorithm that offers good compression ratios, fast execution time and good image quality. We will obtain a bit stream with increasing accuracy from EZW algorithm because of basing on progressive encoding to compress an image. All the numerical results were done by using matlab coding and the numerical analysis of this algorithm is carried out by sizing Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR) for standard Image.

Digital image compression is now essential. Internet teleconferencing, High Definition Television (HDTV), satellite communications and digital storage of images will not be feasible without a high degree of compression. Wavelets became popular in past few years in mathematics and digital signal processing area because of their ability to effectively represent and analyze data. Typical application of wavelets in digital signal processing is image compression. Image compression algorithms based on Discrete Wavelet Transform (DWT),such as Embedded Zero Wavelet (EZW) which produces excellent compression performance, both in terms of statistical peak signal to noise ratio (PSNR) and subjective human perception of the reconstructed image. Said and Pearlman further enhanced the performance of EZW by presenting a more efficient and faster implementation called set partitioning in hierarchical trees. SPIHT is one of the best algorithms in terms of the peak signal-to-noise ratio (PSNR) and execution time. Set partitioning in hierarchical trees provide excellent rate distortion performance with low encoding complexity.

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Image Restoration Using Multiple Thresholds Matlab Project Code

ABSTRACT

Image restoration is an art to improve the quality of image via estimating the amount of noises and blur involved in the image. With the passage of time, image gets degraded due to different atmospheric and environmental conditions, so it is required to restore the original image using different image processing algorithms. Application area varies from restoration of old images in museum and radar based image acquisition and restoration. Image restoration is based on the attempt to improve the quality of an image through knowledge of the physical process which led to its formation. The purpose of image restoration is to "compensate for" or "undo" defects which degrade an image. Degradation comes in many forms such as motion blur, noise, and camera mis-focus. In cases like motion blur, it is possible to come up with a very good estimate of the actual blurring function and "undo" the blur to restore the original image. In cases where the image is corrupted by noise, the best we may hope to do is to compensate for the degradation it caused. Image restoration differs from image enhancement in that the latter is concerned more with accentuation or extraction of image features rather than restoration of degradation's.

Restoration tries to reconstruct by using a priori knowledge of the degradation phenomenon. It deals with getting an optimal estimate of the desired result. Some restoration techniques are best achieved in the spatial domain, while there are some cases where frequency domain techniques are better suited The Purpose of smoothing is to reduce noise and improve the visual quality of the image. A variety of algorithms i.e. linear and nonlinear algorithms are used for filtering the images. Image filtering makes possible several useful tasks in image processing. A filtering technique can be applied to reduce the amount of unwanted noise in a particular image Another type of filter can be used to reverse the effects of blurring on a particular picture. Nonlinear filters have quite different behaviour as compared to linear filters. For nonlinear filters, the output or response of the filter does not follow the principles outlined earlier, particularly scaling and shift invariance. Moreover, a nonlinear filter can generate output that varies in a non-intuitive manner.

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Diabetic Retinopathy Detection In Fundus Images Using Neural Network Full Matlab Project Code

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. In this paper, we have presented detail study of various screening methods for Diabetic Retinopathy. Many researchers have made number of attempts to improve accuracy, productivity, sensitivity and specificity.

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Microcalcification Detection Using Wavelet Transform Full Matlab Project with Source Code

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 . In India, breast cancer accounts for 23% of all the female cancers followed by cervical cancers (17.5%) in metropolitan cities such as Mumbai, Calcutta, and Bangalore. 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). Desai et al.,reported an immuno histo chemical analysis of steroid receptor status in 798 cases of breast tumors encountered in Indian patients, suggests that breast cancer seen in the Indian population may be biologically different from that encountered in western practice. 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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Car License Plate Recognition Using Image Processing Matlab Project with Source Code

ABSTRACT

The road becomes more pervasive, our country's road transport development, because of rapid labor management has not filled with actual needs, microelectronics, communications and computer technology in the transport sector of the application has greatly improved the traffic management efficiency. car license plates for automatic identification technology has been widely applied. car license plates automatically identify the entire process is divided into pre-processing, edge extraction, License Plate Positioning, character segmentation and character recognition 5 module, which character recognition process mainly consists of the following three components: 1) correctly to split text image area; 2) correct separation of a single text; 3) correctly identify a single character. The MATLAB software programming to achieve each and every part, and finally identify the license plate of a car. In the study of the same in which the issue of a concrete analysis, and processing. vehicle license plate recognition system as a whole is the main vehicle positioning and character recognition made up of two parts, one license plate positioning and can be divided into image pre-processing and edge extraction module and the licensing of the positioning and segmentation module; character recognition can be divided into character segmentation and feature extraction and a single character recognition two modules.

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Image Watermarking Using DWT and DCT Matlab Project with Source Code

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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Iris Recognition System Using Image Processing Full Matlab Project Code

ABSTRACT

This project presents an iris coding method for effective recognition of an individual. The recognition is performed based on a mathematical and computational method called discrete cosine transform (DCT). It consists of calculating the differences of discrete cosine transform (DCT) coefficients of overlapped angular patches from the normalized iris image for the purpose of feature extraction. DCT is used because it offers efficiency, it is much more practical and its basis vectors are comprised of entirely real-valued components. Iris recognition belongs to the biometric identification. Biometric identification is a technology that is used for the identification an individual based on ones physiological or behavioral characteristics. Iris is the strongest physiological feature for the recognition process because it offers most accurate and reliable results. Iris recognition process mainly involves three stages namely, iris image preprocessing, feature extraction and template matching. In the pre-processing step, iris localization algorithm is used to locate the inner and outer boundaries of the iris. Detected iris region is then normalized to a fixed size rectangular block. In the feature extraction step, texture analysis method is used to extract significant features from the normalized iris image with the help of Discrete Cosine Transform (DCT).

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Leukemia Blood Cancer Detection Using Image Processing Matlab Project Code

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 and a fuzzy inference system 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 fuzzy system 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 Wavelet Transformation for 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 Fuzzy Inference System for Final decision of blood cancer based on the number of different cells.

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Brain Tumor Detection Using Watershed Technique Matlab Project Code

ABSTRACT

In the field of medical image processing, detection of brain tumor from magnetic resonance image (MRI) brain scan has become one of the most active research. Detection of the tumor is the main objective of the system. Detection plays a critical role in biomedical imaging. In this project, MRI brain image is used to tumor detection process. This system includes test the brain image process, image filtering, morphological operation, Detection of the tumor, Finding Tumor Stage and determination of the tumor location. In this system, morphological operation of watershed technique is applied to detect the tumor. The detailed procedures are implemented using MATLAB. The proposed method extracts the tumor region accurately from the MRI brain image. The experimental results indicate that the proposed method efficiently detected the tumor from the brain image. Watershed Segmentation is the best methods to group pixels of an image on the basis of their intensities. Pixels falling under similar intensities are grouped together. Watershed is a mathematical morphological operating tool. Watershed is normally used for checking output rather than using as an input segmentation technique because it usually suffers from over segmentation and under segmentation. The watershed techniques are useful for segmentation of brain tumor. Image segmentation is based on the division of the image into regions. Division is done on the basis of similar attributes.

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Fingerprint Recognition and Matching Using Image Processing Matlab Project Code

ABSTRACT

The popular Biometric used to authenticate a person is Fingerprint which is unique and permanent throughout a person’s life. A minutia matching is widely used for fingerprint recognition and can be classified as ridge ending and ridge bifurcation. In this paper we projected Fingerprint Recognition using Minutia Score Matching method (FRMSM). For Fingerprint thinning, the Block Filter is used, which scans the image at the boundary to preserves the quality of the image and extract the minutiae from the thinned image. Fingerprint is a very vital concept in making us completely unique and can not be altered. It is necessary to recognize fingerprint in proper manner. Here we are trying to recognize the fingerprint image samples by using minute extraction and minute matching techniques. In minute extraction it counts the crossing numbers and from the count it will be classified as normal ridge pixel, termination point and bifurcation point. Then the input finger print data is compared with the template data. This is called as minute matching.

Biometric systems operate on behavioral and physiological biometric data to identify a person. The behavioral biometric parameters are signature, gait, speech and keystroke, these parameters change with age and environment. However physiological characteristics such as face, fingerprint, palm print and iris remains unchanged through out the life time of a person. The biometric system operates as verification mode or identification mode depending on the requirement of an application. The verification mode validates a person’s identity by comparing captured biometric data with ready made template. The identification mode recognizes a person’s identity by performing matches against multiple fingerprint biometric templates. Fingerprints are widely used in daily life for more than 100 years due to its feasibility, distinctiveness, permanence, accuracy, reliability, and acceptability. Fingerprint is a pattern of ridges, furrows and minutiae, which are extracted using inked impression on a paper or sensors. A good quality fingerprint contains 25 to 80 minutiae depending on sensor resolution and finger placement on the sensor. The false minutiae are the false ridge breaks due to insufficient amount of ink and cross-connections due to over inking. It is difficult to extract reliably minutia from poor quality fingerprint impressions arising from very dry fingers and fingers mutilated by scars, scratches due to accidents, injuries.

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Audio Denoising Matlab Project with Source Code || Final Year Project || IEEE Based Project

ABSTRACT

Audio noise reduction system is the system that is used to remove the noise from the audio signals. Audio noise reduction systems can be divided into two basic approaches. The first approach is the complementary type which involves compressing the audio signal in some well-defined manner before it is recorded (primarily on tape). The second approach is the single-ended or non-complementary type which utilizes techniques to reduce the noise level already present in the source material in essence a playback only noise reduction system. Noise reduction is the process of removing noise from a signal.Digital filters effectively reduce the unwanted higher or lower order frequency components in a speech signal. In this paper the speech enhancement is performed using different digital filters .In this real noisy environment is taken into consideration in the form of Gaussian noise. The Time domain as well as frequency domain representation of the signal spectra is performed using Fast Fourier transformation technique. MATLAB in built functions are used to carry out the simulation. Gaussian type noise is added using in-built function randn () and keyboard noise is added as a second speech file to the original speech signal. The filters remove the lower frequency components of noise and recover the original speech signal. It is also observed that keyboard noise is typical to remove as compared to Gaussian type but these filters performed well to get sharper spectra of original speech signal. Speech signal analysis is one of the important areas of research in multimedia applications. Discrete Wavelet technique is effectively reduces the unwanted higher or lower order frequency components in a speech signal. Wavelet-based algorithm for audio de-noising is worked out. We focused on audio signals corrupted with white Gaussian noise which is especially hard to remove because it is located in all frequencies.

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Video Compression Matlab Project with Source Code || Final Year Project || IEEE Based Project

ABSTRACT

The huge usage of digital multimedia via communications, wireless communications, Internet, Intranet and cellular mobile leads to incurable growth of data flow through these Media. The researchers go deep in developing efficient techniques in these fields such as compression of data, image and video. Recently, video compression techniques and their applications in many areas (educational, agriculture, medical …) cause this field to be one of the most interested fields. Wavelet transform is an efficient method that can be used to perform an efficient compression technique. This work deals with the developing of an efficient video compression approach based on frames difference approaches that concentrated on the calculation of frame near distance (difference between frames). The selection of the meaningful frame depends on many factors such as compression performance, frame details, frame size and near distance between frames. Three different approaches are applied for removing the lowest frame difference. In this project videos are tested to insure the efficiency of this technique, in addition a good performance results has been obtained.

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Matlab Project with Source Code Image Forgery Detection || Final Year Project || IEEE Based Project

ABSTRACT

Image forgery means manipulation of digital image to conceal meaningful information of the image. The detection of forged image is driven by the need of authenticity and to maintain integrity of the image. A copy move forgery detection theme victimization adaptive over segmentation and have purpose feature matching is proposed. The proposed scheme integrates both block based and key point based forgery detection methods. The proposed adaptive over segmentation algorithm segments the host image into non over lapping and irregular blocks adaptively. Then, the feature points are extracted from each block as block features, and the block features are matched with one another to locate the labeled feature points; this procedure can approximately indicate the suspected forgery regions. To detect the forgery regions more accurately, we propose the forgery region extraction algorithm which replaces the features point with small super pixels as feature blocks and them merges the neighboring blocks that have similar local color features into the feature block to generate the merged regions. Finally, it applies the morphological operation to merged regions to generate the detected forgery regions. In cut paste image forgery detection, proposed digital image forensic techniques capable of detecting global and local contrast enhancement, identifying the use of histogram equalization.

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Rain Removal using Image Processing Matlab Project with Source Code || Final Year Project || IEEE Based Project

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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Object Tracking Form Video Matlab Project with Source Code || Final Year Project || IEEE Based Project

ABSTRACT

The ongoing research on object tracking in video sequences has attracted many researchers. Detecting the objects in the video and tracking its motion to identify its characteristics has been emerging as a demanding research area in the domain of image processing and computer vision. Most of the methods include object segmentation using background subtraction. The tracking strategies use different methodologies like Mean-shift, Kalman filter, Particle filter etc. The performance of the tracking methods vary with respect to background information. In this survey, we have discussed the feature descriptors that are used in tracking to describe the appearance of objects which are being tracked as well as object detection techniques. In this survey, we have classified the tracking methods into three groups, and a providing a detailed description of representative methods in each group, and find out their positive and negative aspects.

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Matlab Project with Source Code DCT Based Image Watermarking IEEE Based Project

ABSTRACT

Digital watermarking is a technology for embedding various types of information in digital content. In general, information for protecting copyrights and proving the validity of data is embedded as a watermark. A digital watermark is a digital signal or pattern inserted into digital content. The digital content could be a still image, an audio clip, a video clip, a text document, or some form of digital data that the creator or owner would like to protect. The main purpose of the watermark is to identify who the owner of the digital data is, but it can also identify the intended recipient. The DCT allows an image to be broken up into different frequency bands, making it much easier to embed watermarking information into the middle frequency bands of an image. It has become easy to connect to the Internet from home computers and obtain or provide various information using the World Wide Web (WWW). All the information handled on the Internet is provided as digital content. Such digital content can be easily copied in a way that makes the new file indistinguishable from the original. Then the content can be reproduced in large quantities. For example, if paper bank notes or stock certificates could be easily copied and used, trust in their authenticity would greatly be reduced, resulting in a big loss. To prevent this, currencies and stock certificates contain watermarks. These watermarks are one of the methods for preventing counterfeit and illegal use. Digital watermarks apply a similar method to digital content. Watermarked content can prove its origin, thereby protecting copyright. A watermark also discourages piracy by silently and psychologically deterring criminals from making illegal copies.In digital management, multimedia content and data can easily be used in an illegal way—being copied, modified and distributed again. In this paper, we apply DCT technique to embed the watermark. With the help of DCT technique we can insert the data in short variation of time. A digital watermark is an invisible mark embedded in digital image which may be used for Copyright Protection. We describe a watermarking scheme for ownership verification and authentication. Depending on the desire of the user, the watermark can be either visible or invisible. The scheme can detect any modification made to the image and indicate the specific locations that have been modified.

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Matlab Project with Code Shape Detection and Recognition Using Image Processing IEEE Based 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 recognize an object in a scene. At the lowest level of object recognition, researchers agree that edge and region information are utilized to extract a “perceptual unit” in the scene. Some of the possible invariant features are recognized 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 Project with Code Real Time Object Detection and Tracking using Background Subtraction IEEE Based Project

ABSTRACT

Digital image processing is one of the most researched fields nowadays. The ever increasing need of surveillance systems has further on made this field the point of emphasis. Surveillance systems are used for security reasons, intelligence gathering and many individual needs. Object tracking and detection is one of the main steps in these systems. Different techniques are used for this task and research is vastly done to make this system automated and to make it reliable. In this research subjective quality assessment of object detection and object tracking is discussed in detail. In the proposed system the background subtraction is done from the clean original image by using distortion of color and brightness. The detection of a moving object and tracking of different objects in a video or video sequence is a very important task in the surveillance videos, analysis and monitoring of traffic, tracking and detection of humans and different gesture recognition in human-machine interface. The technique of Object tracking can be explained to be the method of tracking the different number of objects in the video and also the certain directions those objects are traversing in and also to track the entrances to the surveillance site as per the unit time. The sophistication and the complexity of the system determine the resolution of the measurement. This system is often deployed in public places such as shopping malls, metro stations, airports and independent surveillance requests. Different approaches can be used for the surveillance and different technologies used as computer vision, infrared beams and thermal imaging. The reasons for object tracking are many For example People counting in retail stores for intelligence gathering can be regarded as one. This is used for the calculation of the conversion rate and rating of the store by the number of customers to the store rather than the old use of the sales data.

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Matlab Project with Code Plant Disease Detection & Classification on Leaf Images using Image Processing (IEEE Based Project)

ABSTRACT

Diseases decrease the productivity of plant. Which restrict the growth of plant and quality and quantity of plant also reduces. Image processing is best way for detecting and diagnosis the diseases. In which initially the infected region is found then different features are extracted such as color, texture and shape. Finally classification technique is used for detecting the diseases. There are different feature extraction techniques for extracting the color, texture and edge features such as color space, color histogram, grey level co-occurrence matrix (CCM), Gabor filter, Canny and Sobel edge detector. India is agricultural country and most of population depends on agriculture. Farmers have wide range of selection in Fruit and Vegetable crops. The cultivation can be improved by technological support. Disease is caused by pathogen in plant at any environmental condition. In most of the cases diseases are seen on the leaves, fruits and stems of the plant, therefore detection of disease plays an important role in successful cultivation of crops. In most of cases plant diseases are caused by pathogens, microorganism, fungi, bacteria, viruses, etc. Sometimes unhealthy environment include soil and water is also responsible for diseases in plants.

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Matlab Project Code Facial Expression Based Emotion Detection System (IEEE Based Project)

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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Matlab Project Automatic Evaluation of OMR Answer Sheet & Finding Exam Score Using Image Processing full Source Code

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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Rough Set Theory Based Brain Tumor Detection on Dicom Images Matlab Project with Source Code

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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Brain Tumor Detection Using SOM Segmentation and K Clustering Matlab Project with Source Code

ABSTRACT

Image processing is a process where input image is processed to get output also as an image or attributes of the image. Main aim of all image processing techniques is to recognize the image or object under consideration easier visually. Segmentation of images holds a crucial position in

the field of image processing. In medical imaging, segmentation is important for feature extraction, image measurements and image display. A tumor can be defined as a mass which grows without any control of normal forces. Real time diagnosis of tumors by using more reliable algorithms has been an active of the latest developments in medical imaging and detection of brain tumor in MR and

CT scan images. Hence image segmentation is the fundamental problem used in tumor detection. Image segmentation can be defined as the partition or segmentation of a digital image into similar regions with a main aim to simplify the image under consideration into something that is more meaningful and easier to analyze visually.

Brain tumor is an abnormal growth caused by cells reproducing themselves in an uncontrolled manner. Magnetic Resonance Imager (MRI) is the commonly used device for diagnosis. In MR images, the amount of data is too much for manual interpretation and analysis. During the past few years, brain tumor segmentation in Magnetic Resonance Imaging(MRI) has become an emergent research area in the field of medical imaging system. Accurate detection of size and location of brain tumor plays a vital role in the diagnosis of tumor. Image processing is an active research area in which medical image processing is a highly challenging field. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions from the medical images. In this project an efficient algorithm is proposed for tumor detection based on segmentation of brain MRI images using KNN clustering.

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Matlab Project with Source Code Currency Recognition 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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Seam Carving Using Image Processing Full Matlab Project with Source Code

ABSTRACT

Image Processing is an important technology for performing image operations. The analysis and manipulation on a digitized image helps to improve its quality. Image Processing offers a number of techniques to process an image such as Image Resizing, Image Enhancement etc. Image resizing is a key process for displaying visual media on different devices, and it has attracted much attention in the past few years. This paper defines preserving an important region of an image, minimizing distortions, and improving efficiency. Image Resizing can be more effectively reached with a better interpretation of image semantics. A new image importance map and a new seam criterion for image re-targeting is presented. Content-aware image resizing is a promising theme in computer vision and image processing. The seam carving method can effectively achieve image resizing which needs to define image importance to detect the salient context of images.

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Matlab Project with Source Code Extraction of Red, Green and Blue Color from Color Images

ABSTRACT

A RGB image is a colorful image consisting of fixed values of color contents for each pixel. These color contents have different values ranging from 0 to 255.There are inbuilt functions and commands available in MATLAB to extract the required color content from a RGB image. If we required extracting a particular color from a RGB image, there are no integral commands that we use directly to do so. For such type of operations we required some algorithms. A simple algorithm is introduced having series of MATLAB commands and looping statements to extract a particular color from a RGB image. It is very helpful in image processing such as in pattern reorganization and mapping to find best equivalent used in many application fields. To extract a particular color from a RGB image or extract a particular area of interest for processing then we have no need to course the whole image. We have less number of values for processing further. It becomes easier to process the image for some other errands. So a simple algorithm or a simple method is introduced in this project to extract a requisite area of interest and a particular color from a RGB image.

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Image Fusion On Medical Images Using Wavelet Transform Full Matlab Project with Source Code

ABSTRACT

Image fusion is the technique of merging several images from multi-modal sources with respective complementary information to form a new image, which carries all the common as well as complementary features of individual images. With the recent rapid developments in the domain of imaging technologies, multisensory systems have become a reality in wide fields such as remote sensing, medical imaging, machine vision and the military applications.

Image fusion provides an effective way of reducing this increasing volume of information by extracting all the useful information from the source images. Image fusion creates new images that are more suitable for the purposes of human/machine perception, and for further image-processing tasks such as segmentation, object detection or target recognition in applications such as remote sensing and medical imaging. The overall objective is to improve the results by combining DWT with PCA and non-linear enhancement. The proposed algorithm is designed and implemented in MATLAB using image processing toolbox. The comparison has shown that the proposed algorithm provides a significant improvement over the existing fusion techniques.

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Audio Noise Reduction from Audio Signals and Speech Signals Using Wavelet Transform Full Matlab Project with Source Code

ABSTRACT

Speech signal analysis is one of the important areas of research in multimedia applications. Discrete Wavelet technique is effectively reduces the unwanted higher or lower order frequency components in a speech signal. Wavelet-based algorithm for audio de-noising is worked out. We focused on audio signals corrupted with white Gaussian noise which is especially hard to remove because it is located in all frequencies. We use Discrete Wavelet transform (DWT) to transform noisy audio signal in wavelet domain. It is assumed that high amplitude DWT coefficients represent signal, and low amplitude coefficients represent noise. Using thresholding of coefficients and transforming them back to time domain it is possible to get audio signal with less noise. Our work has been modified by changing universal thresholding of coefficients which results with better audio signal. In this various parameters such as SNR, Elapsed Time, and Threshold value is analyzed on various types of wavelet techniques alike Coiflet, Daubechies, Symlet etc. In all these, best Daubechies as compared to SNR is more for Denoising and Elapsed Time is less than others for Soft thresholding. In using hard thresholding Symlet wavelet also works better than coiflet and Daubechies is best for all. Efficiency is 98.3 for de-noising audio signals which also gives us better results than various filters.

Audio noise reduction system is the system that is used to remove the noise from the audio signals. Audio noise reduction systems can be divided into two basic approaches. The first approach is the complementary type which involves compressing the audio signal in some well-defined manner before it is recorded (primarily on tape). The second approach is the single-ended or non-complementary type which utilizes techniques to reduce the noise level already present in the source material—in essence a playback only noise reduction system. This approach is used by the LM1894 integrated circuit, designed specifically for the reduction of audible noise in virtually any audio source. Noise reduction is the process of removing noise from a signal.

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Palm Print Recognition System Using Gabor Filter Full 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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