Traffic Sign Recognition using Python Project Source Code

ABSTRACT

               Traffic sign recognition is an important but challenging task, especially for automated driving and driver assistance. Its accuracy depends on two aspects: feature exactor and classifier. Current popular algorithms mainly use convolutional neural networks (CNN) to execute feature extraction and classification. Such methods could achieve impressive results but usually on the basis of an extremely huge and complex network. What’s more, since the fully-connected layers in CNN form a classical neural network classifier, which is trained by conventional gradient descent-based implementations, the generalization ability is limited. The performance could be further improved if other favorable classifiers are used in python.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

Read More

Matlab Code for Iris Recognition Using Image Processing full Source 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. It consists of calculating the differences coefficients of overlapped angular patches from the normalized iris image for the purpose of feature extraction. 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.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

Read More

Image Encryption and Decryption Using AES Algorithm Matlab Source Code

ABSTRACT

           In today’s world data security is the major problem which is to be face. In order to secure data during communication, data storage and transmission we use Advance encryption standard(AES). AES is a symmetric block cipher intended to replace DES for commercial applications. The AES algorithms use to secure data from unauthorized user. The available AES algorithm is used for text data as well as for image data. In this project an image is given as input to AES encryption algorithm which gives encrypted output. This encrypted output is given as input to AES decryption algorithm and original image is regained as output. The AES algorithm for image encryption and decryption which synthesizes and simulated with the help of MATLAB.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

Read More

Image Enhancement using Histogram Equalization and Bi-Histogram Equalization Matlab Source Code

ABSTRACT

           Digital image enhancement is one of the most important image processing technology which is necessary to improve the visual appearance of the image or to provide a better transform representation for future automated image processing such as image analysis, detection, segmentation and recognition. Many images have very low dynamic range of the intensity values due to insufficient illumination and therefore need to be processed before being displayed. Large number of techniques have focused on the enhancement of gray level images in the spatial domain. These methods include histogram equalization, gamma correction, high pass filtering, low pass filtering, homomorphic filtering, etc. Image enhancement techniques are of particular interest in photography, satellite imagery, medical applications and display devices. Producing visually natural is required for many important areas such as vision, remote sensing, dynamic scene analysis, autonomous navigation, and biomedical image analysis.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

Read More

Image Fusion using PCA Matlab Project Source Code

ABSTRACT

            Different medical imaging techniques such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI) provide different perspectives for the human body that are important in the physical disorders or diagnosis of diseases .To derive useful information from multimodality medical image data medical image fusion has been used. In the medical field different radiometric scanning techniques can be used to evaluate and examine the inner parts of the body. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, so as to provide as much details as possible for the sake of diagnosis. The objective of image fusion is to merge information from multiple images of the same image. The resultant image after image fusion is more suitable for human and machine perception and further helpful for image-processing tasks such as segmentation, feature extraction and object recognition. This project mainly presents image fusion using wavelet method for multispectral data and high-resolution data conveniently, quickly and accurately in MATLAB. Wavelet toolbox with abundant functions, provide a quick and convenient platform to improve image visibility. The work covers the selection of wavelet function, the use of wavelet based fusion algorithms on CT and MRI medical images, implementation of fusion rules and the fusion image quality evaluation. Matlab Results show that effectiveness of Image Fusion with PCA Principal Component Analysis on preserving the feature information for the test images.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

Read More

Age and Gender Recognition using Convolutional Neural Network CNN full Python Project Source Code

ABSTRACT

         Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. In this project we show that by learning representations through the use of deep-convolutional neural networks (CNN), a significant increase in performance can be obtained on these tasks. To this end, we propose a simple convolutional net architecture that can be used even when the amount of learning data is limited. We evaluate our method on the recent Audience benchmark for age and gender estimation and show it to dramatically outperform current state-of-the-art methods.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Mr. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

Read More

How to Calculate PSNR (Peak Signal to Noise Ratio) in MATLAB

Peak-Signal to Noise Ratio (PSNR)

1. The PSNR is most commonly used as a measure of quality of reconstruction of lossy compression codec’s (e.g., for image compression).
2. The signal in this case is the original data, and the noise is the error introduced by compression.
3. When comparing compression codec’s it is used as an approximation to human perception of reconstruction quality, therefore in some cases one reconstruction may appear to be closer to the original than another, even though it has a lower PSNR (a higher PSNR would normally indicate that the reconstruction is of higher quality).

The PSNR is calculated by using following formula.

MAXI=Maximum value of pixel in Original image
m=No. of Row in Original image
n= No. of Column in Original image

Procedure:
1. Read Original Image from current directory.
2. Read Noisy Image from current directory.
3. If Original Image is equal to Noisy Image then PSNR is 100%.
4. Find out difference between Original Image & Noisy Image.
5. Find out Mean Square Error by using above formula.
6. Find out maximum value of Pixel in Original Image.

7. Find out Peak Signal to Noise Ratio.

Program:

function [psnr]=PSNR(I,K)

I=double(I);

K=double(K);

if (I==K)

    psnr=100;

else

    [r c p]=size(I);

    d=0;

    for i=1:r

        for j=1:c

            d=d+(I(i,j)-K(i,j))^2;

        end

    end

    mse=d/(r*c);

    maximum=max(I(:));

    psnr=10*log10(maximum^2/mse);

end

Output:

PSNR =  25.1560

Read More