Image processing in MATLAB - Online Course

Prerequisites:
Basic knowledge of MATLAB programming
Basic understanding of linear algebra and signals
Familiarity with basic image processing concepts (optional)
Course Objectives:
By the end of the course, students will be able to:

Understand and apply core image processing concepts and techniques using MATLAB.
Use MATLAB's Image Processing Toolbox for various tasks such as image enhancement, segmentation, and feature extraction.
Implement algorithms for image filtering, morphological operations, and edge detection.
Work with color images, video, and 3D image data.
Develop simple image processing applications and projects.
Textbooks & References:
Primary Textbook:
"Digital Image Processing" by Rafael C. Gonzalez and Richard E. Woods (4th Edition)
Secondary Reference:
MATLAB's official documentation for Image Processing Toolbox
"MATLAB for Engineers" by Holly Moore (for MATLAB programming basics)
Software Required:
MATLAB with the Image Processing Toolbox
Access to image datasets for practical assignments (many are available online)
Course Outline:
Week 1: Introduction to MATLAB & Image Basics
Overview of MATLAB environment and basic commands
Introduction to digital images: Types, formats, and properties
Reading and displaying images in MATLAB
imread, imshow, figure
Image size, color channels, and intensity levels
Basic image operations (cropping, resizing, and converting between color spaces)
Week 2: Image Enhancement Techniques
Contrast enhancement (histogram equalization, contrast stretching)
Image smoothing and blurring (linear and nonlinear filters)
Convolution and correlation
Gaussian and averaging filters
Edge enhancement (high-pass filtering)
Week 3: Image Filtering and Spatial Domain Operations
Linear spatial filters (mean, median, Gaussian)
Nonlinear filters (order-statistics, adaptive filters)
Filtering in the frequency domain (Fourier transforms)
fft2, ifft2, and frequency filtering
Week 4: Image Thresholding and Segmentation
Global and local thresholding
Otsu’s method for automatic thresholding
Region-based segmentation (region growing, split and merge)
Edge detection techniques
Sobel, Prewitt, Canny edge detectors
Morphological image processing
Dilation, erosion, opening, closing, and skeletonization
strel and structuring elements
Week 5: Color Image Processing
Understanding color models: RGB, HSV, YCbCr
Color space conversions in MATLAB
Color image enhancement and segmentation
Thresholding in color spaces
Week 6: Image Transformation and Geometric Operations
Image scaling, rotation, translation, and affine transformations
Geometric transformations and interpolation methods
Nearest-neighbor, bilinear, and bicubic interpolation
Image warping and registration
Week 7: Feature Detection and Image Analysis
Feature extraction: Corners, edges, and blobs
Harris corner detection, SIFT, and SURF (overview)
Image matching and object recognition
Connected component analysis
Shape and object analysis
Week 8: Advanced Topics and Applications
Introduction to video processing (frame extraction, motion detection)
3D image processing (volumetric data)
Introduction to machine learning for image processing (optional)
Basics of classification and clustering (K-means, SVM)
Week 9: Practical Applications and Case Studies
Medical imaging (image segmentation and enhancement)
Face recognition and object detection
Satellite and aerial image processing
Robotics and autonomous systems
Week 10: Final Project
Students will develop a complete image processing project using MATLAB.
Topics can include (but are not limited to):
Image classification
Object tracking
Image segmentation in medical images
Image enhancement for security systems
Assessment and Grading:
Assignments (40%): Weekly hands-on assignments with MATLAB programming.
Mid-term Exam (20%): A practical exam based on image processing techniques.
Final Project (30%): A complete image processing application/project.
Participation and Discussion (10%): Active participation in class discussions and practical exercises.
Additional Resources:
MATLAB tutorials and documentation: MATLAB Documentation
Online courses (Coursera, edX, etc.) on image processing and MATLAB
Image datasets: Kaggle, OpenCV, and other public repositories

Ready to start learning? Contact us  to sign up for the course and begin your journey with Image processing.

Contact :  +91-9952749533  . Email:-  expertsyssol@gmail.com