Recognizing and Finding Articulated Objects

该奖项资助了一种从真实的图像中检测和识别可变形关节形状（如人和动物）的方法的研究。 该方法涉及到指定一个“语言”表示这样的对象，基于以前的工作可变形模板模型。 这些模型以概率的方式表示对象，其中需要概率 以指定对象和成像功能的可能的几何变形。处理诸如照明更改或对象的部分遮挡等效果。 然而，这些模型是复杂的，因此在图像中检测它们构成了巨大的组合优化挑战。 本研究探讨了一种策略，它利用了少量的图像“原语”，可以被认为是关键特征。 这可能包括当地 图像结构例如图像角， 更大的结构，例如对称形状。 这些应该足够简单，以便完善的优化技术可以最佳地检测它们。 基元是desaribed概率，像对象模型，使信息理论的使用，以量化多少信息，一个特定的基元提供有关的存在或不存在，或配置的对象在图像中。 它还可以实现图元的最佳选择，目标是找到易于计算的图元，并传达有关图像中对象的最大信息。

This award funds investigation of an approach to detecting and recognizing deformable articulated shapes, such as people and animals, from real images. The approach involves specifying a "language" for representing such objects, based on previous work on deformable template models. These models represent objects probabilistically, where the probabilities are required to specify likely geometric deformations of the object and of the imaging function. To handle effects such as lighting changes or partial occlusion of the object. These models, however, are complex, and so detecting them in images poses formidable combinatorial optimization challenges. This research explores a strategy which makes use of a small number of image "primitives" which can be thought of as key features. These may include both local image structures such as image corners and larger structures such as symmetric shapes. These should be simple enough that well-established optimization techniques can detect them optimally. The primitives are desaribed probabilistically, like the object models, enabling the use of information theory to quantify how much information a particular primitive provides about the preseance or absence, or configuration of the object in an image. It also enables optimal choices of primitives to be made, with the goal of finding primitives that are easy to compute and convey maximal information about the objects in the image.