Üç Boyutlu Çözüm Ağları için K-Means Kümeleme Tabanlı Şekil Araması

Yıl 2018, Cilt: 6 Sayı: 1, 114 - 128, 01.03.2018

Mohammadhassan Rezaeı Erkan Gunpınar

https://doi.org/10.15317/Scitech.2018.119

Öz

Şirketlerin veri tabanlarında çok fazla şekil bulunmaktadır. İstenilen şekillerin bu veri tabanından bulunup getirilmesi için etkili ve güçlü şekil arama metodlarının kullanılması gerekmektedir. Araştırmacılar genelde rijit (esnemeyen) modellere uygun geometrik tanımlayıcılar üzerine odaklanmışlardır. Rijit olmayan modellerin bulunması daha zor olmakta ve bu konuda daha çok çalışılması gerekmektedir. Rijit olmayan modeller için tasarlanan geometrik tanımlayıcılar değişik model pozlarında (insan modeli için yürüme, oturma gibi pozlar) çalışabilmesi gerekmektedir. Rijit olmayan model araması için, öncelikle model jeodezik metrik kullanarak parçalara bölünür. Sonra her bir parçanın alanı toplam alana bölünerek geometrik tanımlayıcı hesaplar. İskelet-bazlı K-means metodu kullanarak model parçalara ayrılmıştır. Her parça alan-bazlı tanımlayıcılar kullanılarak tanımlanmıştır. Bu tanımlayıcıların ölçek ve oryantasyon değişikliklerine hassasiyeti yoktur. Çalışmanın testlerinde insandan hayvanlara eklemli canlılar kullanılmış, önerilen model arama metodunun performansı doğrulanmıştır.

Anahtar Kelimeler

Jeodezik uzaklık, K-means kümeleri, Çözüm ağı iskeletleri, Model bulma

A K-MEANS CLUSTERING BASED SHAPE RETRIEVAL TECHNIQUE FOR 3D MESH MODELS

Öz

Due to the large size of shape databases, importance of effective and robust method in shape retrieval has been increased. Researchers mainly focus on finding descriptors which is suitable for rigid models. Retrieval of non-rigid models is a still challenging field which needs to be studied more. For non-rigid models, descriptors that are designed should be insensitive to different poses. For non-rigid model retrieval, we propose a new method which first divides a model into clusters using geodesic distance metric and then computes its descriptor using the area of these clusters. A skeleton-based K-means clustering method is utilized for dividing the model into clusters. Each cluster is represented by an area based descriptor which is invariant to scale and orientation. Articulated objects from human to animals are employed in this study’s experiments for the validation of the proposed retrieval algorithm.

Anahtar Kelimeler

Geodesic distance, K-means clustering, Mesh skeleton, Shape retrieval

Kaynakça

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