Efficient Texture-less Object Detection for Augmented Reality Guidance
Efficient Texture-less Object Detection for Augmented Reality Guidance
复制标题
用于增强现实指导的高效无纹理物体检测
DOI：
10.1109/ismarw.2015.23
复制
发表时间：
2015
期刊：
影响因子：
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通讯作者：
Jiri Matas
中科院分区：
文献类型：
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作者：
Tomás Hodan;D. Damen;W. Mayol;Jiri Matas
在2D图像中的无纹理对象的实时可扩展检测是增强现实应用（例如装配引导）的高度相关的任务。本文提出了一种基于Damen等人（2012）[5]方法的纯边缘方法。所提出的方法利用了Dollár和Zitnick（2013）[8]最近的结构化边缘检测器，该检测器使用监督示例来改进对象轮廓检测。实验结果表明，它始终比标准的Canny边缘检测器产生更好的结果。这项工作已经确定了比原始方法改进的另外两个领域;提出了一种基于Hough的跟踪，带来了超过5倍的速度提升，以及以条纹而不是楔形搜索边缘，特别是在每幅图像的误报率较低的情况下，实现了性能的改进。实验结果表明，该方法具有更快的速度和更强的鲁棒性。该方法也被证明是合适的，以支持增强现实应用程序的装配指导。
Real-time scalable detection of texture-less objects in 2D images is a highly relevant task for augmented reality applications such as assembly guidance. The paper presents a purely edge-based method based on the approach of Damen et al. (2012) [5]. The proposed method exploits the recent structured edge detector by Dollár and Zitnick (2013) [8], which uses supervised examples for improved object outline detection. It was experimentally shown to yield consistently better results than the standard Canny edge detector. The work has identified two other areas of improvement over the original method; proposing a Hough-based tracing, bringing a speed-up of more than 5 times, and a search for edgelets in stripes instead of wedges, achieving improved performance especially at lower rates of false positives per image. Experimental evaluation proves the proposed method to be faster and more robust. The method is also demonstrated to be suitable to support an augmented reality application for assembly guidance.