データセットの違いが物体認識に与える影響の解析―特徴ベクトルの一致検索を用いた認識手法の場合―

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2011/11/01

タイトル(英語): Analysis on the Effect of Dataset Differences for Object Recognition ―For the Case of Recognition Methods Based on Exact Matching of Feature Vectors―

著者名: 井上　勝文（大阪府立大学大学院 工学研究科），黄瀬　浩一（大阪府立大学大学院 工学研究科）

著者名(英語): Katsufumi Inoue (Graduate School of Engineering, Osaka Prefecture University), Koichi Kise (Graduate School of Engineering, Osaka Prefecture University)

キーワード: 特徴ベクトルの一致検索，大規模三次元特定物体認識，COIL-100　　Exact Matching of Feature Vectors，Large-Scale 3D Specific Object Recognition，COIL-100

要約(英語): Specific object recognition methods based on exact matching of feature vectors are known as one of methods which can achieve high recognition performance for large-scale 3D specific object recognition. Since there are few common 3D object datasets whose size is sufficient to explore the effect of difference of object dataset composition and the effect of increasing number of objects for recognition, these effects for specific object recognition methods based on exact matching of feature vectors are discussed insufficiently. The number of objects in famous datasets (e.g., COIL-100) is around 100. Therefore, in this research, we prepare the dataset of 1002 3D objects by ourselves. In this paper, we will discuss the effect of dataset differences, which are based on object structure, texture and the number of objects, for those methods such as the method based on the Bloomier filter and the method based on a hash table with this dataset in addition to COIL-100.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.131 No.11 （2011） 特集：電気関係学会関西連合大会

本誌掲載ページ: 1915-1924 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/131/11/131_11_1915/_article/-char/ja/