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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

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Object-Agnostic Vision Measurement Framework Based on One-Shot Learning and Behavior Tree.

Fangbo Qin, De Xu, Blake Hannaford

IEEE Transactions on Cybernetics

|July 7, 2022

Summary

This summary is machine-generated.

This study introduces an object-agnostic vision measurement framework using deep learning and behavior trees. It extracts contour primitives of interest (CPIs) for versatile geometric information extraction in intelligent systems.

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Area of Science:

Computer Vision
Machine Learning
Geometric Measurement

Background:

Intelligent systems require precise vision measurement for structural and spatial information.
Object-specific vision measurement lacks adaptability for novel objects.
Object-agnostic vision measurement offers reconfigurable and adaptable solutions.

Purpose of the Study:

To propose a novel framework for object-agnostic vision measurement mimicking human visual behavior.
To develop a method for extracting contour primitives of interest (CPIs) from images.
To enable flexible and interpretable geometric information calculation for various measurement requirements.

Main Methods:

A deep convolutional neural network (CNN), CPieNet+, is proposed for one-shot extraction of pixel-level object CPIs.
CPI prototypes are generated by sampling feature maps, guided by shape descriptors and geometric attribute prediction (direction, size).
A measurement behavior tree (BT) models hierarchical geometric calculations, ensuring configurability and interpretability.

Main Results:

CPieNet+ successfully extracts fine-grained CPIs from query images using annotated support images.
The behavior tree effectively converts pixel-level CPIs into key geometric data.
Experimental validation confirms the effectiveness of the proposed object-agnostic vision measurement framework.

Conclusions:

The proposed framework enables versatile and adaptable object-agnostic vision measurement.
The integration of CNNs and behavior trees provides a robust solution for geometric information extraction.
This approach advances intelligent systems' ability to measure and understand novel objects.