Unified Object and Image Representation in 3D Frequency Domain

摘要IIS-9876904 Ben-Arie，JezekielUniversity of Illinois $76，672 - 12 mos. 3D频域中的统一对象和图像表示拟议的研究是开发一个频域对象表示和相关的方法，用于几个主要的视觉问题。 具体而言，体积频域对象表示（VFR）引入的PI将被使用，它封装观众为中心和对象为中心的表示。VFR包括作为原始表示的2-D切片的对象的连续视图。该视图表示基于傅立叶切片定理。先前频率 域表示是针对二维图像开发的，并且没有考虑三维对象结构。用于视觉任务的传统2-D频域或空间域图像表示限于2-D解释。另一方面，所提出的方法考虑了完整的三维物体结构，因此可以处理不同的问题，如视点或照明独立的识别，铰接的3-D运动等。VFR不仅包括一个连续的对象外观，但也3-D结构信息。因此，可以从单个图像中推导出3D对象结构。 姿态不变和光照不变的目标识别可以使用VFR通过直接索引到复杂的（真实的和虚的）体积特征向量空间来实现。提出了两种运动估计方法。 结果将是明确定义运动的3D姿态的时间序列或3D运动流场。这种3-D方法避免了2-D流场的问题，特别是在3-D旋转运动的估计中，其中实际运动矢量将其自身表现为图像平面上的投影。 还计划将VFR模型扩展到几个视觉应用中，例如基于3-D结构的图像分割、姿态不变和光照不变的目标和人脸识别、从单个图像恢复3-D表面和关节运动识别。

Abstract IIS-9876904Ben-Arie, JezekielUniversity of Illinois$76,672 - 12 mos.Unified Object and Image Representation in 3D Frequency DomainThe proposed research is to develop a frequency domain object representation and related methodology for several major vision problems. Specifically, a volumetric frequency domain object representation (VFR) introduced by the PI will be used, which encapsulates both viewer centered and object centered representations. The VFR includes a continuum of the views of the objects as 2-D slices of the original representation. This view representation is based on the Fourier Slice Theorem. Previous frequency domain representations were developed for two dimensional images and did not consider the three dimensional object structure. Conventional 2-D frequency domain or spatial domain image representations for vision tasks is limited to 2-D interpretation. On the other hand, the proposed approach considers the full three dimensional object structure and therefore can handle diverse problems such as viewpoint orillumination independent recognition, articulated 3-D motion, etc. The VFR includes not only a continuum of object appearances but also 3-D structural information. Thus, 3-D object structure can be derived from single images. Pose invariant and illumination invariant object recognition can be implemented using the VFR by directly indexing into a complex (real and imaginary) volumetric eigenvector space. Two approaches are proposed for motion estimation. The result would be either a temporal sequence of 3-D poses which explicitly define the motion or a 3-D motion flow field. This 3-D approach avoids the problems of 2-D flow fields, especially in the estimation of 3-D rotational motions where the actual motion vectors exhibit themselves as projections onto the image plane. It is also planned to extend the VFR model towards several vision applications, such as image segmentation based on 3-D structure, pose invariant and illumination invariant object and face recognition, 3-D surface recovery from single image and articulated motion recognition.