Viewpoint detection models for sequential embodied object category recognition
Viewpoint detection models for sequential embodied object category recognition
复制标题
用于顺序具体对象类别识别的视点检测模型
DOI：
10.1109/robot.2010.5509703
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
J. Little
中科院分区：
文献类型：
--
作者：
D. Meger;Ankur Gupta;J. Little
本文提出了一种学习对象类别视点检测模型的方法，该方法有助于顺序对象类别识别和视点规划。我们已经针对几种最先进的对象检测方法检查了此类模型。我们的学习过程已经使用最近为多视图类别识别研究收集的详尽的多视图类别数据库进行了评估。我们的方法已经在基于之前收集的真实图像的模拟器上进行了评估。模拟结果验证了我们的视点规划方法需要更少的视点来进行自信的识别。最后，我们说明了我们的方法作为完全自主视觉识别平台的组成部分的适用性，该平台之前已在对象类别识别竞赛中得到了证明。
This paper proposes a method for learning viewpoint detection models for object categories that facilitate sequential object category recognition and viewpoint planning. We have examined such models for several state-of-the-art object detection methods. Our learning procedure has been evaluated using an exhaustive multiview category database recently collected for multiview category recognition research. Our approach has been evaluated on a simulator that is based on real images that have previously been collected. Simulation results verify that our viewpoint planning approach requires fewer viewpoints for confident recognition. Finally, we illustrate the applicability of our method as a component of a completely autonomous visual recognition platform that has previously been demonstrated in an object category recognition competition.