Lecture 3. Edge Detection, Texture

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Presentation transcript:

Lecture 3. Edge Detection, Texture Computer Vision Lecture 3. Edge Detection, Texture Oleh Tretiak © 2005

Lecture Outline Noise and Filtering, 8.1-8.2 Edge Detection 8.3, 8.3.1 Texture - what is it? Filter banks for texture detection 9.1 LaPlacian pyramid and texture analysis Oleh Tretiak © 2005

White Gaussian Noise Oleh Tretiak © 2005

Effect of Filters on Noise White noise convolved with Gaussian filter. r is a parameter proportional to the width of the filter. Noise standard deviation  decreases with increasing r. r = 2,  = 6.25 r = 4,  = 3.7 r = 5,  = 3.1 r = 3,  = 4.65 Oleh Tretiak © 2005

Gaussian Edge Detection Step change of 10 with noise,  = 25. Differentiation cannot detect the edges. Oleh Tretiak © 2005

Gaussian Edge Detection Step plus noise after Gaussian filter Laplacian filter applied to image on left Horizontal derivative applied to image on left Oleh Tretiak © 2005

Laplacian Edge Detection Oleh Tretiak © 2005

Gaussian Pyramid Oleh Tretiak © 2005

What is Texture? Oleh Tretiak © 2005

Natural Textures Oleh Tretiak © 2005

Texture Filters Oleh Tretiak © 2005

Application of Texture Filters Oleh Tretiak © 2005

Periodicity in Texture Oleh Tretiak © 2005

Periodicity? Oleh Tretiak © 2005

Gabor Filters Oleh Tretiak © 2005

Laplacian Pyramid Oleh Tretiak © 2005