红外与激光工程
紅外與激光工程
홍외여격광공정
INFRARED AND LASER ENGINEERING
2014年
8期
2728-2732
,共5页
谢易辰%陈健%闫镔%童莉%曾磊%崔明明
謝易辰%陳健%閆鑌%童莉%曾磊%崔明明
사역신%진건%염빈%동리%증뢰%최명명
图像拼接%特征匹配%距离特征集合求交%相似特征
圖像拼接%特徵匹配%距離特徵集閤求交%相似特徵
도상병접%특정필배%거리특정집합구교%상사특정
image mosaicing%feature matching%distance feature set intersection%similar feature
对于使用锥束 CT 分区成像的物体,要得到其完整的三维图像,需要对各分区重建图像进行三维拼接。作为基于特征的三维拼接算法中重要的步骤之一,特征点匹配是要对图像重叠区域中检测到的特征点建立对应关系。针对目前三维 SIFT 特征匹配算法对于相似特征误匹配率较高的问题,提出基于三维特征点空间关系的三维特征点匹配算法:距离特征集合求交法。该算法使用求取简便的特征点三维距离特征作为特征描述符,避免了扩大特征信息统计范围时巨大的计算消耗问题,然后在匹配过程中设计了距离特征集合求交的相似性度量方法,解决了以往基于空间关系方法中特征矢量各项元素不对应的问题。实验证明:该算法在图像存在大量相似特征的前提下,能够有效提高三维特征点匹配的匹配正确率。
對于使用錐束 CT 分區成像的物體,要得到其完整的三維圖像,需要對各分區重建圖像進行三維拼接。作為基于特徵的三維拼接算法中重要的步驟之一,特徵點匹配是要對圖像重疊區域中檢測到的特徵點建立對應關繫。針對目前三維 SIFT 特徵匹配算法對于相似特徵誤匹配率較高的問題,提齣基于三維特徵點空間關繫的三維特徵點匹配算法:距離特徵集閤求交法。該算法使用求取簡便的特徵點三維距離特徵作為特徵描述符,避免瞭擴大特徵信息統計範圍時巨大的計算消耗問題,然後在匹配過程中設計瞭距離特徵集閤求交的相似性度量方法,解決瞭以往基于空間關繫方法中特徵矢量各項元素不對應的問題。實驗證明:該算法在圖像存在大量相似特徵的前提下,能夠有效提高三維特徵點匹配的匹配正確率。
대우사용추속 CT 분구성상적물체,요득도기완정적삼유도상,수요대각분구중건도상진행삼유병접。작위기우특정적삼유병접산법중중요적보취지일,특정점필배시요대도상중첩구역중검측도적특정점건립대응관계。침대목전삼유 SIFT 특정필배산법대우상사특정오필배솔교고적문제,제출기우삼유특정점공간관계적삼유특정점필배산법:거리특정집합구교법。해산법사용구취간편적특정점삼유거리특정작위특정묘술부,피면료확대특정신식통계범위시거대적계산소모문제,연후재필배과정중설계료거리특정집합구교적상사성도량방법,해결료이왕기우공간관계방법중특정시량각항원소불대응적문제。실험증명:해산법재도상존재대량상사특정적전제하,능구유효제고삼유특정점필배적필배정학솔。
To get the entire three-dimensional (3D) image of the object scanned separately by cone beam computed tomography (CBCT), it needed to process the reconstructed image of each region by 3D image mosaicing. As an important step of the mosaicing approach based on feature point, feature point matching buildt the one-to-one relationships between the points detected in the overlap regions. Aiming at the mismatch problem that caused by similar features in the feature matching process of SIFT, a 3D feature point matching method was presented based on spatial relations called Distance Feature Set Intersection (DFSI). This method firstly used easy-calculating 3D distance features to form descriptors, which avoided the large computation cost by expanding the statistical range. Then, distance feature set intersection was devised as the similarity measure, which solved the problem of feature vector elements not corresponding in previous method based on spatial relations. The experimental results show that the proposed approach improves the matching accuracy when images have multiple similar regions.