Image Processing for Science & Engineering in MATLAB

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  • Level
  • Duration
  • Price
  • Certificate
  • Beginner
  • 4 Weeks (2 Hours Per Week & 2 Module Per Week)
  • On Process
  • Bharat Academy Education
  • Certified
  • - AIAL(KHDA)
    - Royal Institute for Chartered Engineer - RICE (USA)
Course Info
Key Highlights
Target Audience
Course Overview:

The course plays a critical role in both science and engineering for analyzing and interpreting visual data. MATLAB is a widely-used tool in these fields due to its robust capabilities for image processing, simulation, and algorithm development. Here's an overview of how image processing is utilized in science and engineering. The course would focus on teaching participants how to apply image processing techniques to solve real-world problems in science and engineering using MATLAB. This course would be suitable for engineers, scientists, students, and researchers who want to learn how to process, analyze, and visualize images using MATLAB’s powerful tools and functions.

Requirements:
  • Course Level: Beginner Level [This Course in a Foundation Course]
  • Course Requirement: There are no course requirements or prerequisites
Assessment Methods:
  • Final Hand-On Exam (30% of Total Grade)
  • Final Project/Case Study Presentation (30% of Total Grade)
  • Quizzes & Assignments After Each Module to Assess Understanding (30% of Total Grade)
  • Participation in Discussions & Group Activities (10% of Total Grade)
Recommended Resources:
  • Digital Image Processing using MATLAB by Rafael C. Gonzales, Richard E.
  • Access to MATLAB’s Toolbox, documentation, video tutorials, and sample code.
  • Online Resources (e.g., Coursera, edX) for Supplementary Learning
Course Delivery Methods:
  • Lectures (In-Person or Online)
  • Each Module will include lectures, practical/case studies hand-on sessions & assessments.
Course Instructor:
  • Emtithal Ahmed
  • Doctor of Biomedical Engineering, R&D of AI & IoT
  • eng_emtithal@yahoo.co.jp
Module 1: Digital Imaging Fundamentals with MATLAB
  • Introduction to MATLAB Environment.
  • Image Acquisition & Image Representation
  • Basic Image Manipulation
Module 2: Image Enhancement: Point & Neighbourhood Processing
  • Histogram Equalization
  • Filtering Techniques & Edge Detection
  • Image Smoothing and Sharpening
  • Applications [Practical Exercise: Hands-On Practice]
Module 3: Image Segmentation Processing
  • Introduction to Segmentation & Thresholding Methods
  • Region Based Segmentation & Morphological Operations
  • Applications [Practical Exercise: Hands-On Practice]
Module 4: Feature Extraction and Object Recognition
  • Feature Detection & Shape Analysis
  • Template Matching & Object Recognition
  • Applications [Practical Exercise: Hands-On Practice]
Module 5: Advanced Image Processing Techniques
  • Fourier & Wavelet Transform in Image Processing
  • Image Registration &Image Fusion Techniques
  • Applications [Practical Exercise: Hands-On Practice]
Module 6: Applications of Image Processing in Science and Engineering
  • Biomedical Image Processing
  • Remote Sensing and Geospatial Imaging
  • Materials Science and Microscopy
  • Machine Vision in Industrial Automation
  • Case Studies in DIP Applications
Module 7: MATLAB GUI Development for Image Processing Applications
  • Introduction to GUI Design in MATLAB
  • Creating Interactive Image Processing Tools
  • Integrating Image Processing Algorithms into GUIs
  • Building a Complete DIP Application Applications [Practical Exercise]
Module 8: Practical Applications & Hands-On Case Studies / Projects
  • Review of Key Concepts
  • DIP Application: Real-World Case Studies
  • Hands-On Projects / Case Studies & Workshops
  • Designing a Simple Capstone Project
  • Group Projects Presentations & Final Assessment
  • Electrical, mechanical, civil, and biomedical engineers who need to analyze images for design, diagnostics, and process monitoring.
  • Scientists and researchers involved in fields such as physics, biology, chemistry, and environmental science.
  • Undergraduate & graduate students in engineering, computer science, & natural sciences.
  • Professionals in medical imaging, remote sensing, and machine vision

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