Real-time 3-D object recognition using Scale Invariant Feature Transform and stereo vision

Scale invariant feature transform (SIFT) and stereo vision are applied together to recognize objects in real time. This work reports the performance of a GPU (graphic processing unit) based real-time feature detector in capturing the features of 3D objects when the objects undergo rotational and translational motions in cluttered backgrounds. We have compared the performance of the feature detector implemented upon GPU to that upon CPU, and shown that GPU-based solution has substantially outperformed its CPU counterpart.