Introduction to Digital Image Processing
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Introduction Introduction to Digital to Digital Image Image Processing Processing
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Transcript of Introduction to Digital Image Processing
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
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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: [email protected]• Twitter: @jseaman76• G+: [email protected]• FB: www.facebook.com/julio.seaman
