Print Data Matrix Barcode using Python смотреть онлайн

Title: - A Watermarking Technique to Secure Printed Matrix Barcode—Application for Anti-Counterfeit Packaging
------------------------------------------------------------------------------------------------------------------------------------------------------
Implementation Plan:
----------------------------------
 
Step 1: Initially, we load the original digital images and select the input image.
 
Step 2: Next we perform the CLIPPING GAUSSIAN NOISE TEXTURE process, In this process the CGN texture is created from an input image of a 2D random Gaussian signal by replacing all values.

Step 3: Next MAGE TEXTURE IDENTIFICATION SOLUTION, In this step we add some white noise using a dithering algorithm. After adding some white noise, the image passes through a Gaussian filter to be blurred.

Step 4: Next we perform the image noise and it is extracted and embedded into the image after that. Finally, the image is scaled down by 1/2 to obtain the final simulated printed image.

Step 5: Next, we perform CLASSIFICATION OF SIMULATED IMAGES, In this process based on the extracted features we classified the image as Numerical, Genuine and Falsified.

Step 6: Finally, we validate the performance of these work,

• Number of images vs. Accuracy
• Number of images vs. Recall
• Number of images vs. Precision
• Number of images vs. F-Score
• ROC Curve
=======================================================================================================================================================================================
 
Software Requirement:
-----------------------------------

Language : Python
IDE : Python-3.10.4
Operating System : Windows-10 (64-bit)
=========================================================================================================================================================================================

We are pleased to offer our support to Research Scholars in selection
from a wide range of topics and ideas for their Research Venture.

In the above mentioned video, we have provided TOP MOST guidance for the selected PhD Topic.

mail us at : [email protected]

visit us at : https://phdtopic.com

call us at : +91 900 344 6899