Dive into image processing with MATLAB through this comprehensive 90-minute tutorial. Learn to extract image properties, read and write images, display and manipulate color spaces, and access pixel values in both grayscale and RGB formats. Master essential techniques such as convolution, image construction, arithmetic operations, and binary transformations. Explore advanced topics including histogram analysis, thresholding, contrast enhancement, noise reduction, and edge detection. Apply these skills to both static images and videos, gaining practical experience in implementing various filters and detectors. By the end of this tutorial, develop a strong foundation in MATLAB-based image processing, enabling you to tackle complex visual data analysis tasks with confidence.
Overview
Syllabus
Lesson 1: Extracting Image Properties using MATLAB.
Lesson 2: Reading, Changing Format and Writing Image using MATLAB.
Lesson 3: Displaying Images using MATLAB.
Lesson 4: Changing Color Space.
Lesson 5: Separating / Splitting Channels using MATLAB.
Lesson 6: Accessing the Pixel Value of a Grayscale Image using Matlab.
Lesson 7: Accessing Pixel Values of an RGB Image using Matlab.
Lesson 8: Convolution of 1D Signal using Matlab.
Lesson 9: Convolution of 2D Image.
Lesson 10: Image Construction using Sample Points.
Lesson 11: Adding Pixel Values using Matlab.
Lesson 12: Image Subtraction in Matlab.
Lesson 13: Multiplication and Division on Image using Matlab.
Lesson 14: Image Complement using Matlab.
Lesson 15: xor Operation on Matlab.
Lesson 16: Changing Binary Threshold using Matlab.
Lesson 17: Logarithmic Transformation of Image using Matlab.
Lesson 18: Exponential Transformation of Image using Matlab.
Lesson 19: Power Law Transformation of Image Using MATLAB.
Lesson 20: Histogram Analysis using Matlab.
Lesson 21: Counting the Values of bins of Histogram using Matlab.
Lesson 22: Threshold Selection for Image using Matlab.
Lesson 23: Adaptive Threshold in Image Processing using Matlab.
Lesson 24: Contrast Stretching in Image Processing using Matlab.
Lesson 25: Histogram Equalization in Image Processing using Matlab.
Lesson 26 Histogram Matching in Image Processing using Matlab.
Lesson 27: Color Histogram in Image Processing using Matlab.
Lesson 28: Max and Min Filtering in Image Processing using Matlab.
lesson 29: Adding Salt and Pepper Noise using Matlab.
Lesson 30: Removing Salt and Pepper Noise using Mean Filter in Matlab.
Median Filter to Remove Noise from Image.
Lesson 32: Rank Filter.
Lesson 33: Gaussian Filter.
Lesson 34: Roberts Edge Detector.
Lesson 35: Prewitt Edge Detection.
Lesson 36: Sobel Edge Detector.
Lesson 37: Edge Detection in Videos.
Taught by
Nuruzzaman Faruqui
