Contour Primitive of Interest Extraction Network Based on One-Shot
Learning for Object-Agnostic Vision Measurement
- URL: http://arxiv.org/abs/2010.03325v2
- Date: Thu, 25 Mar 2021 02:23:39 GMT
- Title: Contour Primitive of Interest Extraction Network Based on One-Shot
Learning for Object-Agnostic Vision Measurement
- Authors: Fangbo Qin, Jie Qin, Siyu Huang, De Xu
- Abstract summary: We propose the contour primitive of interest extraction network (CPieNet) based on the one-shot learning framework.
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.
- Score: 37.552192926136065
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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