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Separable Filter Kernels ⇨ The 2D Gaussian filter is the classic separable filter Product of a Gaussian along the X-axis ...and a Gaussian along the Y-axis 24-November-2010 © Copyright Ian D. Romanick 2009, 2010
Separable Filter Kernels ⇨ Implementing on a GPU: Use first 1D filter on source image to temporary image Use second 1D filter on temporary image to window 24-November-2010 © Copyright Ian D. Romanick 2009, 2010
Page 1 and 2: VGP352 - Week 8 ⇨ Agenda: Text
Page 3 and 4: Texture Rectangle ⇨ Added feature
Page 5 and 6: Post-processing Effects ⇨ Overvie
Page 7 and 8: Post-processing Effects ⇨ Draw tw
Page 9 and 10: Post-processing Effects ⇨ Offset
Page 11 and 12: Filter Kernels ⇨ Uniform blur ove
Page 13 and 14: Filter Kernels - Edge Detection ⇨
Page 19 and 20: Sobel Edge Detection ⇨ Uses two f
Page 21 and 22: Sobel Edge Detection Images from ht
Page 23 and 24: Filter Kernels ⇨ Implement this e
Page 25 and 26: Filter Kernels ⇨ Implement this e
Page 27 and 28: Separable Filter Kernels ⇨ 2D ker
Page 29: Separable Filter Kernels ⇨ The 2D
Page 33 and 34: References http://www.archive.org/d
Page 35 and 36: Ripple Effect ⇨ Render multiple p
Page 37 and 38: Optimization ⇨ Multiple texture l
Page 39 and 40: References Tutorials for several po
Page 41 and 42: Depth-of-field ⇨ Why is DOF impor
Page 43 and 44: Depth-of-field ⇨ Basic optics:
Page 45 and 46: Depth-of-field ⇨ Basic optics:
Page 47 and 48: Depth-of-field ⇨ In most real-tim
Page 49 and 50: Depth-of-field ⇨ Straight-forward
Page 51 and 52: Depth-of-field ⇨ Problem with thi
Page 53 and 54: Depth-of-field ⇨ Simplified GPU i
Page 55 and 56: Depth-of-Field ⇨ “Gather” met
Page 57 and 58: Next week... ⇨ Quiz #4 ⇨ Beyond

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