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We present a method for the registration and matching of perspective surface normal maps. Registration of two maps consists of optimally aligning their normals through a 2-D warping in the image plane in conjunction with a 3-D rotation of the normals. Once aligned, the average dot-product then serves as a match metric for automatic target recognition (ATR). We conduct an ATR experiment using synthesized views of 25 commercial vehicles, and obtain perfect recognition results when the test azimuth is within [-6deg,+10deg] of the reference pose, even when the normals are corrupted by up to 20deg uniform random noise. The results suggest that needle maps are a rich yet compact representation of an object, which may be useful for exploiting information from stereo images, shape from shading algorithms, or sensors which obtain the normals from polarization information.
Image Processing, 2007. ICIP 2007. IEEE International Conference on (Volume:6 )
Date of Conference: Sept. 16 2007-Oct. 19 2007