Certification Program on MATLAB Programming – Imaging & ML Applications
Engineers and scientists worldwide use MATLAB for a wide range of applications, in Industries and Institutions, R & D Division and Productions including multiple concepts such as deep learning and machine learning, signal processing and communications, image and video processing, control systems, test and measurement and more.
Applications of MATLAB are widely applicable to almost all domains in the area of Research, Practice, and Implementation. This program brings out the complete Tools and Explores MATLAB sequentially segment-wise providing the participants a deeper insight into the applicational capabilities of MATLAB
This IETE, Ranchi, and DSIR associative Certification Program on MATLAB – Imaging & ML Applications draws more attention.
MATLAB stands out from the rest when it comes to AI in specific. That is the reason behind its vast usage and deployment by Industries and Research Organisations. This certified course on Image Processing using MATLAB focuses mainly on AI and Applications using MATLAB ToolBox. At the end of the course, the participants will gain unlimited exposure and will effectively learn and deploy Image processing techniques for specific applications. Certification Program on MATLAB – Imaging & ML Applications
Certification Program on MATLAB – Imaging & ML Applications
Total Duration: 30 Hrs Modules: 9 Assignments: 14 Capstone Project – 1
Module 1: MATLAB Programming – Installation, Features & Tool Box
Module 2: Digital Image Fundamentals & Image Processing Techniques
Module 3: Image Pre-processing & Noise Removal
Module 4: Image Compression
Module 5: Application of GLCM
Module 6: Image Segmentation & Detection
Module 7: ML Algorithm
Module 8: Image Recognition & NN Applications
Module 1: MATLAB Programming – Installation, Features & Tool Box
Key Learning Objectives: This Module will introduce the participants to the Overview of MATLAB and its tool boxes pertaining to Imaging with LIVE Interactive Sessions and Hands on Programs.
Tools Covered: MATLAB
Lesson 1: MATLAB – Fundamentals & Tool Box
MATLAB Introduction – Installation of MATLAB – Matrices in MATLAB – Functions in MATLAB – Save & Load Variables in MATLAB – Graphs & Plots in MATLAB – Customising Plots – Graphical User Interface in MATLAB – Creating Buttons in GUI.
Assignment 1: Data Visualisation
Go the Extra Mile – Design a Calculator using MATLAB – GUI
Lesson 2: MATLAB Programming
MATLAB Commands – Control Statements – Loops in MATLAB (For, IF, While) – Loading Images – Basic Conversions – Introduction to Filters – Graphical User Interface in MATLAB (Level II) – Creating Other Specifications (GUI)
Assignment 2 – Graph variation using slider
Other Related Materials – Pdfs / PPT and Video Links Software Download Links – MATLAB
Module 2: Image Fundamentals & Image Processing Techniques
Key Learning Objectives: Participants will get updated towards the basic and important Image Processing Techniques in this Module.
Tools Covered: MATLAB
Lesson 3: Digital Image Processing & Basic Image Manipulation
Types of Images – Import – Visualize and Extract Information from Images – Sampling & Quantisation of Images – RGB Conversion – Gray Scale Conversion of Images – Discrete Wavelet Transform – Discrete Fourier Transform
Assignment 3 – Conversion of images using GUI
Module 3: Image Pre-Processing & De Noising using Filters
Key Learning Objectives:
Understand the concept of Pre-Processing of Images. Comes in handy when it is required to program MATLAB for specific Image-based applications and projects
Lesson 4: Morphological Image Pre-processing Techniques
Image Enhancement – Image Blurring & De Blurring – Transformation – Image Erosion – Dilation & Fusion Techniques – Application Demo
Assignment 4 – Combine two images into a single image
Go the Extra Mile – Image enhancement using the deblurring method
Lesson 5: De Noising & Filtering Images
Noise Estimation – Noise Reduction – Spatial Filters (Mean & Adaptive Filters) & Frequency Domain Filters (Band Pass Filters) – Noise reduction in Image (Demo & Hands-On)
Assignment 5 – Apply median filter for salt & pepper noise
Go the Extra Mile –Remove noise from MRI Scan images
Module 4: Compression Techniques of Images in MATLAB
Key Learning Objectives:
Compressions play a significant role in MATLAB. Learn and apprehend the basic applications of Compression techniques in MATLAB and Apply the same on the Images
Lesson 6: Image Compression
Compression Concept – Image Coding & Decoding Model – Lossless & Lossy Compression – DWT & DFT Based Compression – SWT Based Compression –Watermarking- Applications of Image Compression – MATLAB
Assignment 6 – Compress an image using Discreet Wavelet transform
Go the Extra Mile – Create watermark using Stationary Wavelet transform
Module 5: Application of GLCM in Image Processing
Lesson 7: GLCM – Application based Learning
Compressed Images – Fundamentals of Feature Extraction in Images
Assignment 7 – Extract features of an image using DWT & GLCM
Module 6: Image Segmentation & Detection
Key Learning Objectives:
Lesson 8: Segmentation & Image Detection Techniques
Image Segmentation – Concepts – Binary Based Image Segmentation – K Means Based Image Segmentation – Spatial Fuzzy Means clustering of Image Segmentation- Segmenting based on Colour – Region-based segmentation – Hands-on Exercises –
Assignment 8 – Segmentation of image using Binary
Go the Extra Mile – Segmentation of image using K Means Algorithm
Module 7: ML Algorithms
Key Learning Objectives:
Lesson 9: Support Vector Machine
Intro to SVM-Prediction using CSV Dataset-Classification using an image dataset
Assignment 9- Multiple object detection using CSV dataset
Go the extra Mile – Brain tumor classification using an image dataset
Lesson 10: K-Nearest neighbour
Intro to KNN-Prediction using CSV Dataset-Classification using an image dataset
Assignment 10- Regression analysis using CSV dataset
Go the extra Mile – Brain tumor classification using an image dataset
Lesson 11: Linear regression
Intro to Linear Regression-Multiple Linear Regression-Prediction using CSV Dataset
Assignment 11- Covid-19 affected prediction using CSV dataset
Lesson 12: Decision Tree
Intro to Decision Tree-Prediction using CSV Dataset
Assignment 12- Decision making a prediction using CSV dataset
Lesson 13: Naïve byes
Intro to Naïve byes-Prediction using CSV Dataset
Assignment 13- Attendance prediction using CSV dataset
Lesson 14: Logistic regression
Intro to Logistic regression -Prediction using CSV Dataset
Lesson 15: Random forest
Intro to Random Forest-Prediction using CSV Dataset
Lesson 16: XG Boost
Intro to XG Boost -Prediction using csv Dataset
Lesson 17: Light GBM
Intro to Light GBM -Prediction using csv Dataset
Module 8: Image Pattern Recognition & NN Applications
Key Learning Objectives:
Lesson 18: Image Recognition, Feature Extraction & Neural Networks
Fundamentals of Pattern Recognition – Data Processing – Training Datasets – Feature Extraction in Images – Image Labelling – Neural Networks in MATLAB – Applications of NN in Imaging Domain –
Assignment 14 – Image recognition using deep neural networks
Certification Program on MATLAB – Imaging & ML Applications
Pantech eLearning
Agile Project Expert
1 Comment
Leave a Reply
You must be logged in to post a comment.
Helpful to learn