SIFT DESCRIPTOR

K Wasif 200807007Mrityunjay 200807009

INTRODUCTION

SIFT features are local and based on the appearance of the object at interesting points, and are invariant to image scale and rotation

Aim: Evaluate descriptors for each key point

Achieve fast computation performance on GPU

INPUT OUTPUT

Input: Key point, Orientation, Sigma, Gradient Magnitude (16*16), Gradient Orientation(16*16)

Output: Key point, Orientation, Sigma, Descriptors

GPU ImplementationEach key point's descriptor evaluation is independent of other key points

Pass gradient magnitude, gradient orientation, sigma for all key points in one pass

Every row of blocks in a grid corresponds to a key point

Every block in a row corresponds to 4*4 subregion of that row key point

FLOW

1) Grid Dimension (1024*16)2) Block Dimension (4*4)3) Gaussian Weight evaluation for each thread4) Product of Gradient magnitude with weights5) For each block create 8 orientation bins6) Add the weighted magnitudes to bins based on gradient orientation.7) Normalize the descriptor values when every block on the grid has finished its execution

ISSUES

Divergence of threads

Less Threads per block are being utilized

Generalizing will effect the performance

Shared Memory

Expensive Operations used

RESULTS

Key Points Time (ms) V340 Our Code

1) 790 10.782 4.523 2)1085 12.57 6.23 3) 4096 25.15 11.87 4) 8232 42.23 20.567

GRAPHS

REFRENCES

1) http://vlfeat.org/download.html 2) http://www.cs.unc.edu/~ccwu/siftgpu/ 3) http://forums.nvidia.com/index.php?act=idx 4) http://wikipedia.org/sift

THANK YOU