English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

To automatic detect wood surface defects, a method based on image block percentile color histogram and eigenvector texture feature classification is proposed. Firstly, a wood surface image is divided into several same size image blocks. Secondly, for each image block, a percentile color histogram is calculated as image block color feature. Meanwhile, singular value decomposition (SVD) is adopted to extract k-max eigenvectors as image block texture feature. Then the percentile color histogram and eigenvector texture feature is combined to a feature vector for image block representation. Finally, a support vector machine (SVM) classifier is trained and used to determine which image block is sound or defect wood. The experimental results show that the proposed method can effectively detect wood surface defects, especially the knot type defects.

Wood Materials Defects Detection Using Image Block Percentile Color Histogram and Eigenvector Texture Feature