【摘要】：目的剛體碎塊匹配已經(jīng)在考古、生物工程以及遙感數(shù)據(jù)處理等領(lǐng)域得到了較為廣泛的應(yīng)用,為了進(jìn)一步提高碎塊匹配的精度、速度和算法的抗噪性,提出一種先粗配再細(xì)配的剛體碎塊匹配方法。方法首先采用基于顯著性區(qū)域的碎塊斷裂面匹配方法實現(xiàn)碎塊的粗匹配,然后通過加入高斯概率模型、角度約束和動態(tài)迭代系數(shù)的方式來改進(jìn)迭代最近點(ICP)算法,并采用該算法來實現(xiàn)兩個剛體碎塊斷裂面的細(xì)匹配,從而完成兩個碎塊的最終精確匹配。結(jié)果通過分別對公共碎塊數(shù)據(jù)集和帶有噪聲的秦俑碎塊數(shù)據(jù)模型的匹配實驗結(jié)果表明,與ICP(iterative closest point)算法和概率迭代最近點(PICP)算法相比,提出的改進(jìn)ICP算法在精度方面分別提高了約50%和15%,在速度方面分別提高了約65%和50%,是一種精度更高、速度更快、抗噪性更強的點集匹配算法。結(jié)論該方法不僅能夠?qū)崿F(xiàn)公共碎塊數(shù)據(jù)集的完美匹配,而且對于秦俑這種特殊的剛體碎塊也具有良好的匹配效果,會有更加廣闊的應(yīng)用領(lǐng)域和發(fā)展前景。
[Abstract]:Objective rigid fragment matching has been widely used in archaeology, bioengineering and remote sensing data processing. This paper presents a matching method of rigid body fragments with coarse matching and fine matching. Methods first of all, the coarse matching of fragments is realized by using the method of fault surface matching based on significant region, and then the iterative nearest point algorithm is improved by adding Gao Si probabilistic model, angle constraint and dynamic iteration coefficient. The algorithm is used to realize the fine matching of the fracture surface of the two rigid body fragments, so as to complete the final accurate matching of the two fragments. Results by matching the common data sets and the noisy terracotta Warriors data model, the experimental results show that, compared with the ICP (iterative closest point) algorithm and the probabilistic iterative nearest Point (PICP) algorithm, The improved ICP algorithm improves the accuracy by about 50% and 15%, and increases the speed by 65% and 50% respectively. It is a point set matching algorithm with higher accuracy, faster speed and stronger anti-noise. Conclusion this method can not only achieve the perfect matching of common fragment data sets, but also has a good matching effect for the special rigid body fragments such as the terracotta warriors, and will have a wider application field and development prospect.
【作者單位】： 咸陽師范學(xué)院教育科學(xué)學(xué)院;西北大學(xué)信息科學(xué)與技術(shù)學(xué)院;北京師范大學(xué)信息科學(xué)與技術(shù)學(xué)院;
【基金】：國家自然科學(xué)基金項目(61373117) 陜西省教育廳科學(xué)研究項目(16jk2178)~~
【分類號】：TP391.41

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