Pre Pt.1

  • Introduction to Image Processing

    • Importance of learning image processing in engineering
    • Practical applications: advertising, movies, social media

  • Historical Context

    • First digital image captured: 1896 by Wilhelm Röntgen
    • Subject: X-ray image of his wife's hand
    • Use of high-energy X-rays to capture images

  • Digital Images as Mathematical Matrices

    • Digital images are composed of pixels, which encode light intensity
    • Matrices represent these pixels mathematically
    • Grayscale Images:
    • Represented as 2D matrices
    • Minimum pixel value: 0 (black), Maximum pixel value: 1 (white)
    • Values between 0 and 1 represent varying shades of gray
    • Each pixel specified by row (i) and column (j) indices

  • Color Images and RGB Representation

    • Color images more complex than grayscale images
    • RGB color model: Red, Green, Blue
    • Color mixing:
    • Red + Green = Yellow
    • Blue + Green = Cyan
    • Red + Blue = Magenta
    • Red + Green + Blue = White
    • Mathematics of Color Images:
    • Color images represented as 3D matrices
    • Three values needed for each pixel: Red intensity, Green intensity, Blue intensity
    • Each pixel specified by indices (i, j) and additional color layer index (k)
      • k = 1 (Red), k = 2 (Green), k = 3 (Blue)

  • Color Depth and Number of Colors

    • Digital data stored in binary format (base 2)
    • Bits = 0 or 1; 8 bits = 1 byte
    • A byte allows storage of 256 distinct values (0-255)
    • For a color pixel, each RGB component is stored in 1 byte
    • Therefore, total possible colors = 256 (Red) x 256 (Green) x 256 (Blue) = 16,777,216 possible colors

  • Image Manipulation Example

    • Converting color images to grayscale
    • Average red, green, and blue intensities to collapse a 3D matrix into a 2D matrix
    • Example of swapping colors in images: Mario (Red) and Luigi (Green)
    • Objective: Program to swap clothes using color image matrix manipulation techniques

  • Conclusion and Next Steps

    • A review of how digital images are represented in matrices
    • Future classes will focus on image manipulation techniques in MATLAB
    • Practical exercises to reinforce understanding of image processing concepts.