Why this course?

Description

About the Program :
Image Processing using MATLAB is a complete program with records sessions , Presentations and Code which helps the learners to equip themselves periodically over time on MATLAB Programming . Designed suitably with the inputs from the Industrial Standards , this program on Image Processing deals with the complete A- Z of MATLAB based on Imaging Concepts. 

 

What You will Get?

✅Recorded videos ,Online support through forums 
✅All Video access for 3 Months
✅Download all source code
✅PPTs
✅Course completion Certificate
✅Assignments
✅10+ Projects

 

 

IMAGE PROCESSING USING MATLAB

 ·      Lesson 1 - Introduction to image processing & its applications

·      Lesson 2- MATLAB Fundamentals & Tools Box

·      Lesson 3- GUI , Graphs & Plots in MATLAB

·      Lesson 4- Graphical User Interface ( GUI - I ) in MATLAB

·      Lesson 5-Graphical User Interface ( GUI - II ) in MATLAB

·      Lesson 6-Commands , Control Statements & Loops in MATLAB

·      Lesson 7-Basic Image Manipulation

·      Lesson 8-Morphological Image Pre-processing Techniques - Blurring , De Blurring

·      Lesson 9-Morphological Image Pre-processing Techniques - Erosion , Dilution & Fusion

·      Lesson 10-De Noising Images

·      Lesson 11-Filters in Images

·      Lesson 12-Image Compression - DWT & SWT Based

·      Lesson 13-Image Compression - SWT & Watermarking

·      Lesson 14-Feature Extraction in Images

·      Lesson 15-Feature Extraction using GLCM

·      Lesson 16-Image Segmentation - K Means

·      Lesson 17-Image Segmentation - APP

·      Lesson 18-Image segmentation - Otsu thresholding

·      Lesson 19-Clustering of Images - Region based Segmentation

·      Lesson 20-Image segmentation-watershed

·      Lesson 21-Texture segmentation using Gabor filter

·      Lesson 22-Edge Detection in Images

·      Lesson 23-Face detection using HAAR cascade

·      Lesson 24-Image Encryption - AES Algorithms

·      Lesson 25-RSA Algorithm based Image Encryption

·      Lesson 26-Image Pattern Recognition

·      Lesson 27-Training Datasets in Images

·      Lesson 28-Neural Networks in Images

·      Lesson 29-Project Demo - I

·      Lesson 30-Project Demo - II