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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. 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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  1. Static Image Filtering on Commodity Graphics Processors Peter Djeu May 1, 2003

  2. 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

  3. 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

  4. 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

  5. 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).

  6. 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

  7. 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.?

  8. 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

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

  10. Time (s) versus Kernel Size (elts)

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

  12. 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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