计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2014年
4期
12-17
,共6页
三维模型检索%非刚体%形状特征%热核特征%稀疏表示
三維模型檢索%非剛體%形狀特徵%熱覈特徵%稀疏錶示
삼유모형검색%비강체%형상특정%열핵특정%희소표시
3D model retrieval%non-rigid object%shape descriptor%heat kernel signature%sparse representation
非刚体由于姿态变化会产出多样的形变，因此非刚体的形状检索比刚体更具挑战性。形状特征提取是非刚体三维模型形状检索的关键问题。为了提高非刚体形状检索的准确度，提出了一种非刚体全局形状特征提取方法。此方法的核心思想是将稀疏表示（Sparse Representation，SR）理论用于对尺度无关的热核特征（Scale Invariant Heat Kernel Signature，SIHKS）进行稀疏编码，因此被称为SR-SIHKS。改进了SIHKS局部特征的提取方法，根据所处理的模型库来自适应地确定热扩散时间参数；采用K-SVD算法来训练字典，借助Batch-OMP算法实现局部特征的稀疏编码；将非刚体三维模型的所有局部特征的稀疏编码汇聚为全局形状特征。实验结果表明，SR-SIHKS具有比SIHKS和HKS更优的检索效果。
非剛體由于姿態變化會產齣多樣的形變，因此非剛體的形狀檢索比剛體更具挑戰性。形狀特徵提取是非剛體三維模型形狀檢索的關鍵問題。為瞭提高非剛體形狀檢索的準確度，提齣瞭一種非剛體全跼形狀特徵提取方法。此方法的覈心思想是將稀疏錶示（Sparse Representation，SR）理論用于對呎度無關的熱覈特徵（Scale Invariant Heat Kernel Signature，SIHKS）進行稀疏編碼，因此被稱為SR-SIHKS。改進瞭SIHKS跼部特徵的提取方法，根據所處理的模型庫來自適應地確定熱擴散時間參數；採用K-SVD算法來訓練字典，藉助Batch-OMP算法實現跼部特徵的稀疏編碼；將非剛體三維模型的所有跼部特徵的稀疏編碼彙聚為全跼形狀特徵。實驗結果錶明，SR-SIHKS具有比SIHKS和HKS更優的檢索效果。
비강체유우자태변화회산출다양적형변，인차비강체적형상검색비강체경구도전성。형상특정제취시비강체삼유모형형상검색적관건문제。위료제고비강체형상검색적준학도，제출료일충비강체전국형상특정제취방법。차방법적핵심사상시장희소표시（Sparse Representation，SR）이론용우대척도무관적열핵특정（Scale Invariant Heat Kernel Signature，SIHKS）진행희소편마，인차피칭위SR-SIHKS。개진료SIHKS국부특정적제취방법，근거소처리적모형고래자괄응지학정열확산시간삼수；채용K-SVD산법래훈련자전，차조Batch-OMP산법실현국부특정적희소편마；장비강체삼유모형적소유국부특정적희소편마회취위전국형상특정。실험결과표명，SR-SIHKS구유비SIHKS화HKS경우적검색효과。
Non-rigid 3D objects have plenty of shape deformations because of posture variations, so non-rigid shape retrieval is more challenging than rigid shape retrieval. Shape descriptor is especially important to non-rigid shape retrieval. In order to improve the retrieval accuracy, a new global shape descriptor for non-rigid 3D object is proposed in this paper. The key idea of the approach is to represent the SIHKS(Scale Invariant Heat Kernel Signature)local shape descriptors by means of the sparse representation theory, so it is called SR-SIHKS. The computation of SIHKS is improved by adaptively deducing the time parameters from the non-rigid benchmark. K-SVD algorithm is adopted to train a dictionary, and the sparse repre-sentations of local shape descriptors are gained by Batch-OMP algorithm. The sparse representations of all local shape descriptors are integrated over the entire shape to form a global shape descriptor. Experimental results show SR-SIHKS has obviously better retrieval performance than SIHKS and HKS on some non-rigid shape retrieval benchmarks.
