论文标题
基于对象敏捷视觉测量的一声学习的兴趣提取网络的轮廓原始提取网络
Contour Primitive of Interest Extraction Network Based on One-Shot Learning for Object-Agnostic Vision Measurement
论文作者
论文摘要
基于图像轮廓的视力测量广泛应用于机器人操纵和工业自动化中。它很有吸引力的是实现对象不足的视觉系统，可以方便地为各种物体重复使用。我们根据单弹性学习框架提出了兴趣提取网络（CPIENET）的轮廓原始。首先，CPIENET的特征是它的轮廓原始感兴趣（CPI）输出（指定的定期轮廓部分位于指定的对象上）为视觉测量提供了必不可少的几何信息。其次，CPIENET具有单次学习能力，利用支持样本来帮助对新物体的感知。为了实现较低的成本培训，我们从未配对的在线公共图像中生成了支持 - 疑问对，这些样本涵盖了广泛的对象类别。为了获得单像素宽轮廓以进行精确测量，基于Gabor过滤器的非最大抑制作用旨在将原始轮廓变薄。对于新颖的CPI提取任务，我们使用在线公共图像构建了对象轮廓原始图数据集，以及使用安装在机器人上的摄像机的机器人对象轮廓测量数据集。提出方法的有效性通过一系列实验验证。
Image contour based vision measurement is widely applied in robot manipulation and industrial automation. It is appealing to realize object-agnostic vision system, which can be conveniently reused for various types of objects. We propose the contour primitive of interest extraction network (CPieNet) based on the one-shot learning framework. First, CPieNet is featured by that its contour primitive of interest (CPI) output, a designated regular contour part lying on a specified object, provides the essential geometric information for vision measurement. Second, CPieNet has the one-shot learning ability, utilizing a support sample to assist the perception of the novel object. To realize lower-cost training, we generate support-query sample pairs from unpaired online public images, which cover a wide range of object categories. To obtain single-pixel wide contour for precise measurement, the Gabor-filters based non-maximum suppression is designed to thin the raw contour. For the novel CPI extraction task, we built the Object Contour Primitives dataset using online public images, and the Robotic Object Contour Measurement dataset using a camera mounted on a robot. The effectiveness of the proposed methods is validated by a series of experiments.
