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Implementing Memory & Run Time Efficient Image Texture Classification using NVIDIA GPU

Implementing Memory & Run Time Efficient Image Texture Classification using NVIDIA GPU. SHREYAS PARNERKAR. Motivation.

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Implementing Memory & Run Time Efficient Image Texture Classification using NVIDIA GPU

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  1. Implementing Memory & Run Time Efficient Image Texture Classification using NVIDIA GPU SHREYAS PARNERKAR

  2. Motivation • Texture analysis is important in many applications of computer image analysis for classification or segmentation of images based on local spatial variations of intensity or color. • Applications include industrial and biomedical surface inspection, for example for defects and disease, segmentation of satellite or aerial imagery, segmentation of textured regions in document analysis. • Most texture classification methods derive features based on output of large filter banks (13 – 48 dimensional feature space).

  3. Motivation • Tuzel et al. use image intensities and first and second order derivatives of intensities in both x and y direction for texture classification which results in a 5 dimensional feature space. • These features are used to calculate co-variance matrices using Integral images (P & Q). • Calculation of integral images is computationally intensive because of highly nested loops.

  4. Algorithm: Integral Image Calculations

  5. Dependence Graph ROWS COLUMNS

  6. GPU Utilization concerns • Such scheduling results in a maximum of W or H elements to be executed in parallel. • But at other instances, it is always less than the maximum. • GPU utilization drops down resulting in slow-down since plenty of threads are idle. • Such scheduling is hence not good for GPU implementation.

  7. Memory Concerns • Shared Memory Limited to 4kB . Cannot put entire image in shared memory. • Global memory is slow compared to shared memory. • Uploading entire image in global memory causes interference with the graphic display (??). • Put just the required data in shared memory. • Required data can be entire image.

  8. Updated Dependence Graph ROWS ROWS COLUMNS COLUMNS + =

  9. Results

  10. Results CPU Over-Head

  11. Results

  12. Yet to come…. Scope to improve the speed up

  13. In Conclusion… • Implement parallel reduction for even more speed up. (In progress) • Use calculated P-Q integral images to calculate covariance. ( Can be done on CPU ) • Read Data from actual images (Currently sample random data is generated). • Compare Memory Usage for CPU vs GPU implementation.

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