An energy minimizing snake algorithm that runs over a grid is designed and used to reconstruct high resolution 3D human faces from pairs of stereo images. The accuracy of reconstructed 3D data from stereo depends highly on how well stereo correspondences are established during the feature matching step. Establishing stereo correspondences on human faces is often ill posed and hard to achieve because of uniform texture, slow changes in depth, occlusion, and lack of gradient. We designed an energy minimizing algorithm that accurately finds correspondences on face images despite the aforementioned characteristics. The algorithm helps establish stereo correspondences unambiguously by applying a coarse-to-fine energy minimizing snake in grid format and yields a high resolution reconstruction at nearly every point of the image. Initially, the grid is stabilized using matches at a few selected high confidence edge points. The grid then gradually and consistently spreads over the low gradient regions of the image to reveal the accurate depths of object points. The grid applies its internal energy to approximate mismatches in occluded and noisy regions and to maintain smoothness of the reconstructed surfaces. The grid works in such a way that with every increment in reconstruction resolution, less time is required to establish correspondences. The snake used the curvature of the grid and gradient of image regions to automatically select its energy parameters and approximate the unmatched points using matched points from previous iterations, which also accelerates the overall matching process. The algorithm has been applied for the reconstruction of 3D human faces, and experimental results demonstrate the effectiveness and accuracy of the reconstruction.
For practical pattern recognition and tracking systems, it is often useful to have a high-speed random access memory (RAM) that complements a holographic correlator. Recently, we have demonstrated a super-parallel holographic correlator, which uniquely identifies N images from a database using only O() number of detector elements. We show how this correlator architecture, operated in reverse, may be used to realize a super-parallel holographic random access memory. We present preliminary results, establishing the feasibility of the superparallel holographic random access memory, and show that essentially the same set of hardware can be operated either as the super-parallel holographic optical correlator or as a super-parallel holographic random access memory, with minor reorientation of some of the elements in real time. This hybrid device thus eliminates the need for a separate random access memory for a holographic correlator-based target recognition and tracking system.