Microscopic Local Binary Pattern for Texture Classification

スポンサーリンク

概要

• 論文の詳細を見る

• This paper presents a Microscopic Local Binary Pattern (MLBP) for texture classification. The conventional LBP methods which rely on the uniform patterns discard some texture information by merging the nonuniform patterns. MLBP preserves the information by classifying the nonuniform patterns using the structure similarity at microscopic level. First, the nonuniform patterns are classified into three groups using the macroscopic information. Second, the three groups are individually divided into several subgroups based on the microscopic structure information. The experiments show that MLBP achieves a better result compared with the other LBP related methods.

• The Institute of Electronics, Information and Communication Engineersの論文

著者

• Yang Xin

  Institute For Chemometrics And Chemical Sensing Technology College Of Chemistry And Chemical Enginee

• SONG Wei

  Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University

• HE Jiangping

  Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University

• JI Hongwei

  Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University

関連論文

• An Optode with a Covalently Bound Fluorescent Dye, 3-Acryloylaminobenzanthrone, for and Ethanol Assay
• Optimal Gaussian Kernel Parameter Selection for SVM Classifier
• Microscopic Local Binary Pattern for Texture Classification

スポンサーリンク