Vehicle Number Plate Recognition Using Image Processing Matlab Project with Source Code

ABSTRACT

           This project presents Automatic Number Plate extraction, character segmentation and recognition for Indian vehicles. In India, number plate models are not followed strictly. Characters on plate are in different Indian languages, as well as in English. Due to variations in the representation of number plates, vehicle number plate extraction, character segmentation and recognition are crucial. We present the number plate extraction, character segmentation and recognition work, with english characters. Number plate extraction is done using Sobel filter, morphological operations and connected component analysis. Character segmentation is done by using connected component and vertical projection analysis. Automatic Number Plate Recognition (ANPR) system is an important technique, used in Intelligent Transportation System. ANPR is an advanced machine vision technology used to identify vehicles by their number plates without direct human intervention. It is an important area of research due to its many applications. The development of Intelligent Transportation System provides the data of vehicle numbers which can be used in follow up, analyses

and monitoring. ANPR is important in the area of traffic problems, highway toll collection, borders and custom security, premises where high security is needed, like Parliament, Legislative Assembly, and so on. The complexity of automatic number plate recognition work varies throughout the world. For the standard number plate, ANPR system is easier to read and recognize. In India this task becomes much difficult due to variation in plate model. 

                  The ANPR work is generally framed into the steps: Number plate extraction, character segmentation and character recognition. From the entire input image, only the number plate is detected and processed further in the next step of character segmentation. In character segmentation phase each and every character is isolated and segmented. Based on the selection of prominent features of characters, each character is recognized, in the character recognition phase. Extraction of number plate is difficult task, essentially due to: Number plates generally occupy a small portion of whole image; difference in number plate formats, and influence of environmental factors. This step affects the accuracy of character segmentation and recognition work. Different techniques are developed for number plate extraction. 

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Prof. Roshan P. Helonde

Mobile / WhatsApp:+91-7276355704

Email: roshanphelonde@rediffmail.com

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Breast Cancer Detection Using Neural Networks Matlab Project with Source Code

ABSTRACT

                    Cancer is the major threat for human being health and its number of patients increasing word wide due to the global warming, even if there are new therapies and treatments proposed by research doctors, but level of cancer defines the ability of its cure. There are different types of cancers from which human being is suffering [male and female]. In this project we are focusing on breast cancer in women, rest allcancers are out of scope of this paper. Large number of women population is affected by the breast cancer. A different type of reasons causes the breast cancer such as X-Ray. For women’s, breast cancer is most common cancer, and it has been increasing since from last decade. The early detection of breast cancer helps to completely cure it through the treatment. The early detection is done by self-exam which can be done by woman in each month. This process is refereed as breast cancer early detection. However currently many hospitals and doctors uses the mammography test and resulted as effective technique for breast cancer early detection. The aim of this test is to perform early detection of breast cancer using characteristic masses detection as well as micro calcifications as these characteristics are considered as most important factor of breast cancer.

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Contact:  

Prof. Roshan P. Helonde

Mobile / WhatsApp:+91-7276355704

Email: roshanphelonde@rediffmail.com

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LICENSE NUMBER PLATE RECOGNITION BY NEURAL NETWORKS AND IMAGE PROCESSING MATLAB PROJECT

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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Contact:  

Prof. Roshan P. Helonde

Mobile / WhatsApp:+91-7276355704

Email: roshanphelonde@rediffmail.com

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A ROBUST DIGITAL IMAGE WATERMARKING BASED ON JOINT DWT AND DCT MATLAB PROJECT

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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Mr. Roshan P. Helonde

Mobile:+91-7276355704

WhatsApp: +91-7276355704

Email: roshanphelonde@rediffmail.com

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Automated Blood Cancer Detection Using Image Processing Matlab Project

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.

PROJECT OUTPUT

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Contact:  

Prof. Roshan P. Helonde

Mobile / WhatsApp:+91-7276355704

Email: roshanphelonde@rediffmail.com

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A LSB BASED STEGANOGRAPHY FOR VIDEO STREAM WITH ENHANCED SECURITY AND EMBEDDING/EXTRACTION Full Matlab Project

ABSTRACT

Video Steganography deals with hiding secret data or information within a video. In this paper, a hash based least significant bit (LSB) technique has been proposed. A spatial domain technique where the secret information is embedded in the LSB of the cover frames. Eight bits of the secret information is divided into 3,3,2 and embedded into the RGB pixel values of the cover frames respectively. A hash function is used to select the position of insertion in LSB bits. The proposed method is analyzed in terms of both Peak Signal to Noise Ratio (PSNR) compared to the original cover video as well as the Mean Square Error (MSE) measured between the original and stenographic files averaged over all video frames. Image Fidelity (IF) is also measured and the results show minimal degradation of the stenographic video file. The proposed technique is compared with existing LSB based Steganography and the results are found to be encouraging. An estimate of the embedding capacity of the technique in the test video file along with an application of the proposed method has also been presented.

PROJECT OUTPUT 

Contact:  

Prof. Roshan P. Helonde

Mobile / WhatsApp:+91-7276355704

Email: roshanphelonde@rediffmail.com

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Audio Noise Reduction from Audio Signals and Speech Signals Using Wavelet Transform Matlab Project

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.

PROJECT OUTPUT

Contact:  

Prof. Roshan P. Helonde

Mobile / WhatsApp:+91-7276355704

Email: roshanphelonde@rediffmail.com

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