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Using angle densities for 3D recognition

机译:使用角度密度进行3D识别

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Abstract: Using computer vision to recognize 3-D objects is complicated by the fact that geometric features vary with view orientation. The key in designing recognition algorithms is therefore based on understanding and quantifying the variation of certain cardinal features. The features selected for study in the research reported in this paper are the angles between landmarks in a scene. The spatial arrangement of landmarks on an object may constitute a unique characteristic of that object. As an example the angles between the wing tips and the nose cone of an aircraft may be adequate in distinguishing amongst a given class of aircraft. In a class of polyhedral objects the angles at certain vertices may form a distinct and characteristic alignment of faces. For many other classes of objects it may be possible to identify distinctive spatial arrangements of some readily identifiable landmarks. In this paper we derive the two dimensional joint density function of two angles in a scene given an isotropic view orientation and an orthographic projection. This analytic expression is useful in deriving likelihood functions which may be used to obtain measures of the likelihood of angle combinations in images of known objects or scenes. These likelihood functions allow us to establish statistical decision schemes to recognize objects. Experiments have been conducted to evaluate the usefulness of the proposed methods. !10
机译:摘要:由于几何特征随视图方向而变化,因此使用计算机视觉识别3D对象非常复杂。因此,设计识别算法的关键是基于对某些基本特征的理解和量化。本文报道的研究中选择用于研究的特征是场景中地标之间的角度。物体上地标的空间布置可以构成该物体的独特特征。作为示例,飞机的翼尖和鼻锥之间的角度可能足以在给定类别的飞机之间进行区分。在一类多面体对象中,某些顶点处的角度可能会形成面的独特且特征性的对齐方式。对于许多其他类别的物体,可能可以识别一些易于识别的地标的独特空间布置。在本文中,我们在给定各向同性视图方向和正交投影的情况下,推导了场景中两个角度的二维联合密度函数。该分析表达式可用于导出似然函数,该似然函数可用于获得已知对象或场景的图像中角度组合的似然性的度量。这些似然函数使我们能够建立统计决策方案来识别物体。已经进行了实验以评估所提出方法的有效性。 !10

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