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Dense Image Over-segmentation on a GPU

Alex Rodionov 4/24/2009. Dense Image Over-segmentation on a GPU. Outline. 1) Problem/Motivation 2) Goals 3) Overview of Algorithm 4) Implementation 5) Results 6) Future work & Conclusions. Problem. Applications. Computer vision (object recognition, intelligent segmentation, ...)

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Dense Image Over-segmentation on a GPU

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  1. Alex Rodionov 4/24/2009 Dense Image Over-segmentation on a GPU

  2. Outline • 1) Problem/Motivation • 2) Goals • 3) Overview of Algorithm • 4) Implementation • 5) Results • 6) Future work & Conclusions

  3. Problem

  4. Applications • Computer vision (object recognition, intelligent segmentation, ...) • Apply Algorithm X to a grid of superpixels vs. a grid of pixels • Image compression

  5. Goals • Run at interactive framerates • Fast enough for interactive use (video) • Segment a 640x480 image in 200ms or less (5+ FPS)

  6. The Algorithm • 0) Preprocess image (to grayscale, smooth) • 1) Calculate speed map • 2) Place N seed points throughout the image • 3) Initialize a distance function to these seeds • 4) Evolve distance function one timestep • 5) Superpixel boundaries: pixels where distance func == 0

  7. 0) Preprocess

  8. 1) Calculate Speed - Function of image gradient magnitude (edge strength)

  9. 2) Place seeds

  10. 3) Init Distance Function Pixel value = distance to closest seed

  11. 4) Evolve the function - Superpixels grow over time - Evolution done by partial differential equation - d(pixel)/dt function of spatial derivatives, speed, and proximity to other superpixels

  12. 5) Extract boundary Zero-crossings of distance function define boundary

  13. Result

  14. GPU Implementation • Original implementation of TurboPixels done in MATLAB, parts accelerated with C • GPU Implementation is C++ accelerated with CUDA • Not all parts of original algorithm mappable to GPU (algorithms not parallel!)

  15. GPU Implementation • Example: Distance Transform • Foreach pixel, get distance to nearest pixel-of-importance • Used in initializing distance function • Used during evolution to get nearest superpixel boundary point • Original algorithm used Fast Marching Method to calculate (uses global data structure, not parallel) • Replace with a GPU-friendly substitute

  16. GPU Implementation • Assignment map • Array of superpixel IDs • Keeps track of superpixel coverage, ownership • Prevents merging of superpixels

  17. Performance Optimizations • Use CUDA arrays,textures when kernel performs random or neighbor accesses • Little use of shared memory • Kernels either read once+write once, or have complex access patterns not easily done with shmem • Loop unrolling, aligning image array sizes, ...

  18. Results

  19. Sample Result • Platform: NVIDIA GTX280 • Image size: 640x480 • Time: 443ms (2.25FPS) • Timesteps: 122 • Superpixels: 1000 • Software implementation: 30 sec on 481x321 image

  20. Conclusions • Implemented a TurboPixels-like image oversegmentation algorithm on a GPU • Performance goal of 5fps on 640x480 not quite attained • Achieved significant speedup over software implementation (although one written mostly in MATLAB...)

  21. Future Work • Algorithmic optimizations: • Evolve area around the expanding boundary, instead of evolving everything • Use variable-length timesteps to reduce number of timesteps and amount of work • Application to video: • Once it runs fast enough, then what? • Modify algorithm to take advantage of inter-frame coherence

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