Static image filtering on commodity graphics processors
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Static Image Filtering on Commodity Graphics Processors. Peter Djeu May 1, 2003. Filters from Computer Vision. Mean (a.k.a. average) filter each element in a neighborhood is given equal weight; a simple image smoother Gaussian

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Static Image Filtering on Commodity Graphics Processors

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Static image filtering on commodity graphics processors

Static Image Filtering on Commodity Graphics Processors

Peter Djeu

May 1, 2003


Filters from computer vision

Filters from Computer Vision

  • Mean (a.k.a. average) filter

    • each element in a neighborhood is given equal weight; a simple image smoother

  • Gaussian

    • a neighborhood is weighted by a 2-D Gaussian, with the peak at the center; a better image smoother

  • Laplacian of Gaussian

    • The Gaussian filter is applied, and then the Laplacian (spatial derivative is applied); good for edge detection


The convolution kernel

The Convolution Kernel

  • We want to transmit pixel information from neighbors to a central pixel

  • Use the convolution kernel as a window to frame the work that needs to be done

1

161


Filtering on a cpu vs a gpu

Filtering on a CPU vs. a GPU

  • CPU

    • sequential and straightforward

  • GPU

    • not so straightforward if the goal is to exploit parallelism and maintain good locality

    • a pixel’s output value depends on the weighted value of it’s neighbors, so there is dependency across various elements


Pixel buffers in gpus

Pixel Buffers in GPUS

  • GPU’s do not have indirect addressing to memory, so results have to be stored in pixel buffers. The card is really rendering to an off-screen frame (writing).

  • The GPU can then treat the Pixel Buffer as a texture for rendering (reading).


Proposal for the gpu algorithm

Proposal for the GPU Algorithm

1. Store original into pb1.

2. For each element ki in the convolution kernel {

3. Copy pb1 into pb2, scaling by ki

in the process (use Cg shader).

4. Based on the location of ki,

render pb2 into pb3 with a

certain offset. The blending is

a single add.

}

5. return pb3


The ups and downs

The Ups and Downs

  • This technique may be fast because…

    • parallelism is completely possible during the scaling stage and the blending

    • since most convolution kernels have symmetry, a little bit of preprocess could mean

  • On the other hand…

    • as image size grows, cache misses may become more prominent, since we manipulate the whole image

    • when translating, coords. are interpolated, not mapped

  • Tiling? Can a good size be determined in exp.?


Current progress

Current Progress

  • P-Buffer’s are frustrating

    • wglReleasePbufferDCARB() returning type PFNWGLRELEASEPBUFFERDCARBPROC

  • Lot’s of low level implementation / debugging, very much on a hardware level

  • (Naïve) CPU implementation is complete and working, and P-Buffers are almost done


Results in real time sec s cpu gaussian filter w rgb 24 bit targa s

Results (in real-time sec’s)CPU, Gaussian Filter, w/ RGB, 24 bit targa’s


Time s versus kernel size elts

Time (s) versus Kernel Size (elts)


Time s versus image size x y using a 31 x 31 kernel

Time(s) versus Image Size (x*y)using a (31 x 31) kernel


Applications

Applications?

  • Super fast filtering techniques on 2-D images may provide tools or insight for traditionally more complex problems involving 2-D images, like categorization / classification


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