Motion Recognition by Parts Using Affine-Invariant Spectral Signatures

该研究的总体目标是开发一种新的方法，用于识别运动物体或部件以及提取和分类其轨迹。 这项研究的一个潜在应用是识别人体的手势和身体部位的运动，如四肢，手指等。 该方法是基于图像补丁，对应于对象表面，大致平面的图标识别。 每个表面被单独识别为对其3D姿态不变，采用仿射不变 光谱特征（AISS）和 频域中的多维索引。 AISS是基于新的高斯 二维调制核函数 这 是以不同方向的子集来组织的。 一 2-层级式 索引 方案被使用 为 柔性物体的姿态不变识别。 索引方案提供每个识别的对象/部件的姿态信息。因此，完全3D运动（包括旋转）可直接从经处理的图像获得 序列的 一种新的方法被计划 为 运动识别 使用 多维索引 的 的 物体/部件的3D轨迹。 身体部位的3D轨迹被分割成运动矢量，然后使用这些运动矢量的有序子集构造一组顺序索引。 然后，这些有序子集用于对模型进行投票

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