SIFT特征匹配技術(shù)研究與應(yīng)用
發(fā)布時(shí)間：2018-08-27 05:52
【摘要】：圖像已經(jīng)成為信息化時(shí)代下人們獲取信息的一種必要手段,如何利用圖像處理技術(shù)獲取外界信息成為國內(nèi)外研究者重點(diǎn)關(guān)注的一類問題。尺度不變特征變換(Scale Invariant Feature Transform,SIFT)算法因其在圖像尺度變化、旋轉(zhuǎn)等狀況下的魯棒性和獨(dú)特性在特征匹配中得到了廣泛的應(yīng)用,然而該算法在特征點(diǎn)生成時(shí)效性和匹配精度上仍有一定的局限性。本文針對計(jì)算機(jī)圖像處理中目標(biāo)識別和目標(biāo)跟蹤兩大研究方向,引入經(jīng)典的SIFT算法的思想并對其進(jìn)行優(yōu)化,設(shè)計(jì)了改進(jìn)的目標(biāo)匹配和運(yùn)動目標(biāo)跟蹤算法。本論文的主要研究內(nèi)容包括:(1)使用體現(xiàn)圖像信息量的圖像熵進(jìn)行關(guān)鍵點(diǎn)閾值判斷,設(shè)計(jì)了自適應(yīng)的關(guān)鍵點(diǎn)閾值調(diào)整方法;(2)引入基于直方圖距離計(jì)算的EMD距離,同時(shí)基于SIFT算法特性,將改進(jìn)EMD算法與多梯度方向SIFT特征點(diǎn)相結(jié)合進(jìn)行距離的比對和運(yùn)算的剪枝;(3)針對于多目標(biāo)識別,設(shè)計(jì)了基于SIFT特征點(diǎn)雙向匹配的改進(jìn)算法;(4)設(shè)計(jì)一種融合SIFT向量和DBSCAN聚類的方法,以替代TLD算法中的跟蹤模塊。且對TLD算法檢測模塊進(jìn)行調(diào)整。根據(jù)上述設(shè)計(jì)思路,本文實(shí)現(xiàn)了基于改進(jìn)的SIFT算法的目標(biāo)識別和目標(biāo)跟蹤算法,并通過測試數(shù)據(jù)集對所設(shè)計(jì)的算法進(jìn)行了驗(yàn)證。實(shí)驗(yàn)結(jié)果表明本文方法能夠(1)較好的解決圖像匹配中多數(shù)特征點(diǎn)無意義匹配的問題;(2)較好的解決了匹配過程中諸多場景下歐氏距離不適用的問題;(3)實(shí)現(xiàn)多目標(biāo)場景中的識別檢測;(4)較好的解決TLD算法的跟蹤模塊在運(yùn)動目標(biāo)長期跟蹤中難以保持魯棒跟蹤的問題。本文所設(shè)計(jì)的方法,對經(jīng)典的SIFT算法的不足之處做出了針對性的改進(jìn),不僅提高了圖像目標(biāo)的的匹配準(zhǔn)確度,并且運(yùn)算效率相對于原算法也有了較好的進(jìn)步,在目標(biāo)的識別和跟蹤應(yīng)用中具有更好的適用性。
[Abstract]:Image has become a necessary means for people to obtain information in the information age. How to use image processing technology to obtain external information has become the focus of attention of researchers at home and abroad. Scale invariant feature transform (Scale Invariant Feature Transform,SIFT) algorithm is widely used in feature matching because of its robustness and uniqueness in image scale change and rotation. However, the algorithm still has some limitations in feature point generation timeliness and matching accuracy. Aiming at the two research directions of target recognition and target tracking in computer image processing, this paper introduces the idea of classical SIFT algorithm and optimizes it, and designs an improved target matching and moving target tracking algorithm. The main research contents of this thesis are as follows: (1) using image entropy to judge the threshold of key points, and designing an adaptive threshold adjustment method for key points; (2) introducing the EMD distance based on histogram distance. At the same time, based on the characteristics of SIFT algorithm, the improved EMD algorithm is combined with multi-gradient direction SIFT feature points to carry out distance comparison and pruning. (3) for multi-target recognition, An improved algorithm based on bidirectional matching of SIFT feature points is designed. (4) A method of combining SIFT vector and DBSCAN clustering is designed to replace the tracking module in TLD algorithm. And the TLD algorithm detection module is adjusted. According to the above design ideas, this paper implements the target recognition and target tracking algorithm based on the improved SIFT algorithm, and verifies the designed algorithm through the test data set. The experimental results show that this method can (1) solve the problem of meaningless matching of most feature points in image matching, (2) solve the problem that Euclidean distance is not suitable for many scenes in the matching process, and (3) realize the multi-objective field. (4) solve the problem that the tracking module of TLD algorithm is difficult to keep robust tracking in the long term tracking of moving target. The method designed in this paper improves the shortcomings of the classical SIFT algorithm. It not only improves the accuracy of image target matching, but also improves the computational efficiency compared with the original algorithm. It has better applicability in target recognition and tracking applications.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
本文編號：2206297
[Abstract]:Image has become a necessary means for people to obtain information in the information age. How to use image processing technology to obtain external information has become the focus of attention of researchers at home and abroad. Scale invariant feature transform (Scale Invariant Feature Transform,SIFT) algorithm is widely used in feature matching because of its robustness and uniqueness in image scale change and rotation. However, the algorithm still has some limitations in feature point generation timeliness and matching accuracy. Aiming at the two research directions of target recognition and target tracking in computer image processing, this paper introduces the idea of classical SIFT algorithm and optimizes it, and designs an improved target matching and moving target tracking algorithm. The main research contents of this thesis are as follows: (1) using image entropy to judge the threshold of key points, and designing an adaptive threshold adjustment method for key points; (2) introducing the EMD distance based on histogram distance. At the same time, based on the characteristics of SIFT algorithm, the improved EMD algorithm is combined with multi-gradient direction SIFT feature points to carry out distance comparison and pruning. (3) for multi-target recognition, An improved algorithm based on bidirectional matching of SIFT feature points is designed. (4) A method of combining SIFT vector and DBSCAN clustering is designed to replace the tracking module in TLD algorithm. And the TLD algorithm detection module is adjusted. According to the above design ideas, this paper implements the target recognition and target tracking algorithm based on the improved SIFT algorithm, and verifies the designed algorithm through the test data set. The experimental results show that this method can (1) solve the problem of meaningless matching of most feature points in image matching, (2) solve the problem that Euclidean distance is not suitable for many scenes in the matching process, and (3) realize the multi-objective field. (4) solve the problem that the tracking module of TLD algorithm is difficult to keep robust tracking in the long term tracking of moving target. The method designed in this paper improves the shortcomings of the classical SIFT algorithm. It not only improves the accuracy of image target matching, but also improves the computational efficiency compared with the original algorithm. It has better applicability in target recognition and tracking applications.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
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