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Makale detayı · 2009

3D Model Retrieval Using Probability Density Based Shape Descriptors

YÖKSİS OpenAlex SJR Q1 JCR Q1 Atıf 127 Üst %1 Yüzdelik 99.3% FWCI 13.18
Yıl
2009
Tür
article

Veri kaynağı ayrımı

  • YÖKSİS YÖKSİS makale kaydı
  • YÖKSİS dergi adı IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
  • Katalog eşleşmesi (ISSN) IEEE Transactions on Pattern Analysis and Machine Intelligence
  • OpenAlex OpenAlex zenginleştirmesi (özet, atıf, konular)

Özet

OpenAlex · İngilizce

We address content-based retrieval of complete 3D object models by a probabilistic generative description of local shape properties. The proposed shape description framework characterizes a 3D object with sampled multivariate probability density functions of its local surface features. This density-based descriptor can be efficiently computed via kernel density estimation (KDE) coupled with fast Gauss transform. The non-parametric KDE technique allows reliable characterization of a diverse set of shapes and yields descriptors which remain relatively insensitive to small shape perturbations and mesh resolution. Density-based characterization also induces a permutation property which can be used to guarantee invariance at the shape matching stage. As proven by extensive retrieval experiments on several 3D databases, our framework provides state-of-the-art discrimination over a broad and heterogeneous set of shape categories.

Konular

Atıflar

OpenAlex cited_by_count. WoS veya Scopus atıf sayısı değildir; o kaynaklar için ayrı kolon yoktur.

127 atıf

OpenAlex cited_by_count (önbellek / veritabanı)

Yazarlar

  1. Akgul Ceyhun Burak
  2. Sankur Buelent
  3. Yemez Yuecel
  4. Schmitt Francis
  5. YÜCEL YEMEZ KOÇ ÜNİVERSİTESİ