Feature Space Trajectory Methods for Active Computer Vision
Feature Space Trajectory Methods for Active Computer Vision
复制标题
主动计算机视觉的特征空间轨迹方法
DOI：
10.1109/tpami.2002.1114854
复制
发表时间：
2002
期刊：
影响因子：
--
通讯作者：
D. Casasent
中科院分区：
文献类型：
--
作者：
M. Sipe;D. Casasent
我们提出了一种新的主动目标识别算法，该算法对刚体目标进行分类，并根据强度图像估计其姿态。我们的算法自动检测给定图像中对象的类别或姿势是否模糊，根据需要重新定位传感器，并结合来自多个对象视图的数据来确定最终的对象类别和姿势估计。使用全局特征空间中的概率特征空间轨迹(FST)来表示对象的3D扭曲视图，并估计输入对象的类别和姿态。使用概率FST对象表示导出的类别和姿态估计的置信度测量确定何时需要额外的观测以及传感器应该放置在哪里以提供最有用的信息。我们演示了使用由计算机辅助设计模型绘制的图像构建的FST来识别真实图像中的真实对象的能力，并给出了一组金属加工零件的测试结果。
We advance new active object recognition algorithms that classify rigid objects and estimate their pose from intensity images. Our algorithms automatically detect if the class or pose of an object is ambiguous in a given image, reposition the sensor as needed, and incorporate data from multiple object views in determining the final object class and pose estimate. A probabilistic feature space trajectory (FST) in a global eigenspace is used to represent 3D distorted views of an object and to estimate the class and pose of an input object. Confidence measures for the class and pose estimates, derived using the probabilistic FST object representation, determine when additional observations are required as well as where the sensor should be positioned to provide the most useful information. We demonstrate the ability to use FSTs constructed from images rendered from computer-aided design models to recognize real objects in real images and present test results for a set of metal machined parts.