ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Dynamic timber cell recognition using two-dimensional image measurement machine | JoVE Visualize

Home
Research Domains
Engineering

Dynamic timber cell recognition using two-dimensional image measurement machine

¹Information and Computer Engineering Institute, Northeast Forestry University, Harbin, China. bit_zhao@yahoo.com.cn

The Review of Scientific Instruments|September 8, 2011

View abstract on PubMed

Summary

This study introduces a robust timber cell recognition method for blurry images, improving accuracy by utilizing image blur information. The technique aids in identifying timber species for accurate property and value assessment.

Area of Science:

Wood science
Image processing
Computer vision

Background:

Image motion blur and defocus blur are common in imaging systems with relative camera-object motion.
Low-quality timber cell images with blurs pose challenges for accurate species identification.
Accurate timber species identification is crucial for assessing physical properties and economic value.

Purpose of the Study:

To develop a robust timber cell recognition scheme for low-quality, blurred color images.
To leverage image blur information to enhance recognition accuracy.
To determine the maximum translation speed for moving gallery imaging.

Main Methods:

A novel two-dimensional image measurement machine was developed using a color camera.
Image-moment-based blur invariant features were calculated.
Timber cell recognition was performed using Euclidean distance based on moment invariants.

Main Results:

The proposed scheme effectively utilizes image blur information to improve timber cell recognition accuracy.
Experimental and theoretical analysis confirmed the effectiveness of the blur invariant features.
The maximum translation speed for the moving gallery was investigated.

Conclusions:

The developed scheme provides robust timber cell recognition from blurred images.
Utilizing image blur information significantly enhances the accuracy of timber species identification.
This method supports accurate assessment of timber properties and economic value.

Civil Engineering

Timber Engineering

Dynamic Timber Cell Recognition Using Two-dimensional Image Measurement Machine