NSF Young Investigator

形状是认知图片中物体的一个重要线索。 然而，形状很难在计算机中描述和使用 物体识别的程序，因为物体看起来不同 从不同的角度拍摄的图像。 这种效果是 对于弯曲的三维物体尤其明显。 最近的研究表明，对于某些表面， 计算描述，仅利用来自单个 图像，并且对于给定图像的每个可能视图都是相同的。 面 这些不变的描述符立即识别对象， 达到已知的模糊度。 本研究探讨了 使用不变量组合的对象识别系统 描述符和颜色信息来识别弯曲的三个- 三维对象，从大量已知的例子中得出， 从一个未知的观察位置拍摄的照片。

Shape is an important cue in rcognizing objects in pictures. Shape is, however, difficult to describe and use in computer programs for object recognition, because objects appear different in images taken from different viewpoints. This effect is particularly pronounced for curved three-dimensional objects. Recent work has shown that, for some surfaces, it is possible to compute descriptions that draw only on information from a single picture, and are the same for every possible view of a given surface. These invariant descriptors instantly identify an object, up to a known ambiguity. This research investigates advance systems for object recognition that use a combination of invariant descriptors and color information to recognize curved three- dimensional objects, drawn from a large number of known examples, in pictures taken from an unknown viewing position.//