SHREYAS PARNERKAR

MotivationTexture 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).

MotivationTuzel 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.

Algorithm: Integral Image Calculations

Dependence GraphROWS

GPU Utilization concernsSuch 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.

Memory ConcernsShared 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.

Updated Dependence GraphROWS

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Results

ResultsCPU Over-Head

Results

Yet to come….Scope to improve the speed up

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.