The general objective of the research is to develop a new approach for recognition of moving objects or parts as well as extracting and classifying their trajectories. A prospective application of this research is recognition of human gestures and motion of body parts, such as limbs, fingers, etc. The approach is based on iconic recognition of image patches that correspond to object surfaces that are roughly planar. Each surface is recognized separately invariant to its 3D pose, employing affine invariant spectral signatures (AISS) and multi-dimensional indexing in the frequency domain. The AISS are based on novel Gaussian kernels with 2-dimensional modulation which are organized in subsets of distinct orientations. A 2-level hierarchical indexing scheme is used for pose-invariant recognition of flexible objects. The indexing scheme provides pose information of each recognized object/part. Thus, full 3D motion (including rotations) is directly available from processed image sequences. A novel method is planned for motion recognition using multi-dimensional indexing of the 3D trajectories of objects/parts. The 3D trajectories of body parts are segmented into motion vectors and then a set of sequential indices is constructed using ordered subsets of these motion vectors. These ordered subsets are then used to vote for model
