Contour Primitive of Interest Extraction Network Based on One-Shot Learning for Object-Agnostic Vision Measurement

TL;DR

This paper introduces CPieNet, a one-shot learning network that extracts object-specific contour primitives for vision measurement, enabling object-agnostic robotic perception with minimal training data.

Contribution

The paper presents CPieNet, a novel one-shot learning framework for extracting contour primitives of interest, facilitating object-agnostic vision measurement in robotics.

Findings

CPieNet effectively extracts contour primitives with high accuracy.

The method achieves reliable object measurement using minimal training samples.

Experimental results validate the approach on public and robotic datasets.

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.