Introduction to Introduction to Digital Image Digital Image ProcessingProcessing

Digital Image ProcessingDigital Image Processing

• Digital image processing is the use of computer algorithms to perform image processing on digital images.

Taken from Wikipedia

Why should we care?Why should we care?

• Everyone has a digital camera• Everyone shares photos and videos• Concepts can be used in videogames• Robotics• New technologies (Google Glass, Oculus Rift)• Useful in other fields of science (medical

imaging, astronomy)• Many frameworks for image processing• It’s just fun

What is a digital image?What is a digital image?

A Pixel!!!!A Pixel!!!!

Pixel!!!!Pixel!!!!

IR y UVIR y UV

Representing PixelsRepresenting Pixels

Representing Pixels: 2Representing Pixels: 2

Representing Pixels: 4Representing Pixels: 4

Representing Pixels: 8Representing Pixels: 8

Representing Pixels: 16Representing Pixels: 16

Representing Pixels: 32Representing Pixels: 32

Representing Pixels: 64Representing Pixels: 64

Representing Pixels: 128Representing Pixels: 128

Representing Pixels: 256Representing Pixels: 256

Colors: RGB spaceColors: RGB space

Colors: RGBColors: RGB

Lab: Image loading, color Lab: Image loading, color channelschannelsimg = imread ( 'Desktop/guitarra.jpg' );img_size = size ( img )

img_red = uint8 ( zeros ( img_size ) );img_green = uint8 ( zeros (img_size) );img_blue = uint8 ( zeros ( img_size ) );

Lab: Image loading, color Lab: Image loading, color channelschannelsimg_red (:, :, 1 ) = img ( :, :, 1 );img_green (:, :, 2 ) = img ( :, :, 2 );img_blue (:, :, 3 ) = img ( :, :, 3 );

imshow ( img );imshow ( img_red );imshow ( img_green );imshow ( img_blue );

Lab: Image loading, color Lab: Image loading, color channelschannelsimg = rgb2gray ( img );imshow ( img );

Image: Matrix of Pixel ValuesImage: Matrix of Pixel Values

Image: Matrix of Pixel ValuesImage: Matrix of Pixel Values

Imagen: Matriz de PixelesImagen: Matriz de Pixeles

Imagen: Matriz de PixelesImagen: Matriz de Pixeles

Imagen: Matriz de PixelesImagen: Matriz de Pixeles

Image: Matrix of Pixel ValuesImage: Matrix of Pixel Values

Image: Matrix of Pixel ValuesImage: Matrix of Pixel Values

Image: Matrix of Pixel ValuesImage: Matrix of Pixel Values

Image: Matrix of Pixel ValuesImage: Matrix of Pixel Values

Lab: Gamma CorrectLab: Gamma Correct

function output = gcorrect ( i, g )i = double ( i );output = ( i / 255).^g * 255;output = uint8 ( output );end;

imshow ( gcorrect (img, 2 ) );imshow ( gcorrect (img, 0.5 ) );

Guess the PixelGuess the Pixel

Image: Matrix of Pixel ValuesImage: Matrix of Pixel Values

Imagen: Matriz de PixelesImagen: Matriz de Pixeles

Image: Matrix of Pixel ValuesImage: Matrix of Pixel Values

Image: Pixel NeighborhoodImage: Pixel Neighborhood

• Every pixel has neighbors

Image: Pixel NeighborhoodImage: Pixel Neighborhood

Image: Pixel NeighborhoodImage: Pixel Neighborhood

Image: Pixel NeighborhoodImage: Pixel Neighborhood

5 8 7

4 1 6

3 2 9

1 2 3 4 5 6 7 8 95

Lab: Median FilterLab: Median Filter

img_noise = imnoise (img, 'salt & pepper', 0.4);imshow ( img_noise );img_filt = medfilt2 ( img_noise );imshow ( img_filt );

Image: Pixel NeighborhoodImage: Pixel Neighborhood

Image: Pixel NeighborhoodImage: Pixel Neighborhood

5x5 9x9 13x13 17x17 21x21 25x25 29x29

Image: Pixel NeighborhoodImage: Pixel Neighborhood

Image: Pixel NeighborhoodImage: Pixel Neighborhood

Image: Pixel NeighborhoodImage: Pixel Neighborhood

Image: f(x,y)Image: f(x,y)

Imagen: f(x,y)Imagen: f(x,y)

Image: f(x,y)Image: f(x,y)

Image: f(x,y)Image: f(x,y)

Image: f(x,y)Image: f(x,y)

Image: f(x,y)Image: f(x,y)

Image: f(x,y)Image: f(x,y)

Laplacian Derivative

Imagen: f(x,y)Imagen: f(x,y)

Image: f(x,y)Image: f(x,y)

Image: f(x,y)Image: f(x,y)

Image: f(x,y), Gradients, SobelImage: f(x,y), Gradients, Sobel

Image: f(x,y), Gradients, SobelImage: f(x,y), Gradients, Sobel

Image: f(x,y), Gradients, SobelImage: f(x,y), Gradients, Sobel

Image: f(x,y), Gradients, SobelImage: f(x,y), Gradients, Sobel

Image: f(x,y), Gradients, SobelImage: f(x,y), Gradients, Sobel

Image: f(x,y), Gradients, SobelImage: f(x,y), Gradients, Sobel

Lab: Edge DetectionLab: Edge Detection

Step 1: Create a Gauss filtergauss_fil = [2 4 5 4 24 9 12 9 45 12 15 12 54 9 12 9 42 4 5 4 2 ]sum ( sum (gauss_fil ) )gauss_fil = gauss_fil / 159

Lab: Edge DetectionLab: Edge Detection

Step 2: Apply Gauss filterimg_blur = conv2 ( double ( img ), gauss_fil );img_blur = uint8 ( img_blur );figure, imshow ( img ), figure, imshow ( img_blur );

Lab: Edge DetectionLab: Edge Detection

Step 3: Create kernels to calculate gradientker_gx = [-1 0 1-2 0 2-1 0 1 ]ker_gy = [1 2 10 0 0-1 -2 -1]

Lab: Edge DetectionLab: Edge Detection

Step 4: Calculate gradientgx = conv2 ( double ( img ), ker_gx );gy = conv2 ( double ( img ), ker_gy );img_g = abs( gx ) + abs( gy );img_g = uint8 ( img_g );imshow ( img_g );

Lab: Edge DetectionLab: Edge Detection

Step 5: Suppression of non-maxima

Lab: Edge DetectionLab: Edge Detection

Step 6: Two thresholdsStep 7: Hysteresis

img_c = edge (img, 'canny' );figure, imshow(img_g), figure, imshow ( img_c );

Image: f(x,y), Hough TransformImage: f(x,y), Hough Transform

Image: f(x,y), Hough TransformImage: f(x,y), Hough Transform

0 45 90 135

1

2

3

4

5

6

7

Imagen: f(x,y), Transformada de Imagen: f(x,y), Transformada de HoughHough

Image: f(x,y), Hough TransformImage: f(x,y), Hough Transform

Lab: Segment DetectionLab: Segment Detection

• Visit: www.ipol.im• Segmentation and Edges• LDS: a Line Segment Detector

Lab: SegmentationLab: Segmentation

• Visit: www.ipol.im• Segmentation and Edges• Chan-Vese Segmentation• Consider: A Real Time Morphological Snakes

Algorithm

Things to ConsiderThings to Consider

• Quadtrees• Review Graph Theory (Laplace, Gradiantes,

Watersheds)– Strongly Connected Components– Minimum Cut Algorithm

• Gather feedback from the user (make the user select areas or faces)

• Sparse Modeling• Machine Learning

WatershedsWatersheds

Solving a Maze with Image Solving a Maze with Image Processing?Processing?

Solving a Maze with Image Solving a Maze with Image Processing?Processing?

Solving a Maze with Image Solving a Maze with Image Processing?Processing?

Usefool ToolsUsefool Tools

• Matlab / Octave• Mathematica• OpenCV• ipol.im• Python• Photoshop

Thanks for watching.Thanks for watching.

• Email: jseaman@squadventure.com• Twitter: @jseaman76• G+: jseaman76@gmail.com• FB: www.facebook.com/julio.seaman