CS 559 Computer Graphics Lecture 6 Painterly Rendering

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CS 559: Computer Graphics Lecture 6: Painterly Rendering and Edges Li Zhang Spring 2010

CS 559: Computer Graphics Lecture 6: Painterly Rendering and Edges Li Zhang Spring 2010

Another type of painterly rendering • Line Drawing http: //www. cs. rutgers. edu/~decarlo/abstract. html

Another type of painterly rendering • Line Drawing http: //www. cs. rutgers. edu/~decarlo/abstract. html

Another type of painterly rendering • Line Drawing http: //www. cs. rutgers. edu/~decarlo/abstract. html

Another type of painterly rendering • Line Drawing http: //www. cs. rutgers. edu/~decarlo/abstract. html

Another type of painterly rendering • Line Drawing http: //www. cs. rutgers. edu/~decarlo/abstract. html

Another type of painterly rendering • Line Drawing http: //www. cs. rutgers. edu/~decarlo/abstract. html

Another type of painterly rendering • Line Drawing http: //www. cs. rutgers. edu/~decarlo/abstract. html

Another type of painterly rendering • Line Drawing http: //www. cs. rutgers. edu/~decarlo/abstract. html

Edge Detection • Convert a 2 D image into a set of curves –

Edge Detection • Convert a 2 D image into a set of curves – Extracts salient features of the scene

Edge detection • One of the most important uses of image processing is edge

Edge detection • One of the most important uses of image processing is edge detection: – Really easy for humans – Not that easy for computers – Fundamental in computer vision – Important in many graphics applications

What is an edge? • Q: How might you detect an edge in 1

What is an edge? • Q: How might you detect an edge in 1 D?

Gradients • The gradient is the 2 D equivalent of the derivative: gx[i, j]

Gradients • The gradient is the 2 D equivalent of the derivative: gx[i, j] = f[i+1, j] – f[i, j] and gy[i, j]=f[i, j+1]-f[i, j] Can write as mask [-1 1] and [1 – 1]’ • Properties of the gradient – It’s a vector – Points in the direction of maximum increase of f – Magnitude is rate of increase • How can we approximate the gradient in a discrete image?

Less than ideal edges

Less than ideal edges

Results of Sobel edge detection

Results of Sobel edge detection

Edge enhancement • A popular gradient magnitude computation is the Sobel operator: • We

Edge enhancement • A popular gradient magnitude computation is the Sobel operator: • We can then compute the magnitude of the vector (sx, sy).

Results of Sobel edge detection

Results of Sobel edge detection

Results of Sobel edge detection

Results of Sobel edge detection

Non-maximum Suppression • Check if pixel is local maximum along gradient direction – requires

Non-maximum Suppression • Check if pixel is local maximum along gradient direction – requires checking interpolated pixels p and r The Canny Edge Detector

Steps in edge detection • Edge detection algorithms typically proceed in three or four

Steps in edge detection • Edge detection algorithms typically proceed in three or four steps: – Filtering: cut down on noise – Enhancement: amplify the difference between edges and non-edges – Detection: use a threshold operation – Localization (optional): estimate geometry of edges, which generally pass between pixels

The Canny Edge Detector original image (Lena)

The Canny Edge Detector original image (Lena)

The Canny Edge Detector magnitude of the gradient

The Canny Edge Detector magnitude of the gradient

The Canny Edge Detector After non-maximum suppression

The Canny Edge Detector After non-maximum suppression

Canny Edge Detector original Canny with : Gaussian filter parameter • The choice of

Canny Edge Detector original Canny with : Gaussian filter parameter • The choice of – large – small depends on desired behavior detects large scale edges detects fine features Canny with

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