Flexible and Accurate Recognition for Non-Rigid Object using Graph Matching

2016 Fiscal Year Final Research Report

• Project/Area Number: 15H06009
• Research Category: Grant-in-Aid for Research Activity Start-up
• Allocation Type: Single-year Grants
• Research Field: Perceptual information processing
• Research Institution: Tohoku University
• Principal Investigator: Miyazaki Tomo 東北大学, 工学研究科, 助教 (10755101)
• Project Period (FY): 2015-08-28 – 2017-03-31
• Keywords: グラフ認識 / 非剛体認識 / グラフ確率モデル / グラフ類似度

Outline of Final Research Achievements

Graphs describe non-rigid objects which vary greatly and flexibly. However, graphs are not used for pattern recognition for image objects due to the following two problems: difficulty in extracting graphs from images and lack of a method for measuring similarity of graphs.
In this study, we propose a method for image object recognition by applying probabilistic graph model to measure similarity of graphs extracted from feature points in an image. In addition, we show the improvement of recognition performance using several probabilistic graph models. These results are significant in not only patter recognition society but also industry because a use of graphs can be facilitated by the proposed method.

Free Research Field

パターン認識