【摘要】：圖像已經(jīng)成為信息化時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獲取外界信息成為國內外研究者重點關注的一類問題。尺度不變特征變換(Scale Invariant Feature Transform,SIFT)算法因其在圖像尺度變化、旋轉等狀況下的魯棒性和獨特性在特征匹配中得到了廣泛的應用,然而該算法在特征點生成時效性和匹配精度上仍有一定的局限性。本文針對計算機圖像處理中目標識別和目標跟蹤兩大研究方向,引入經(jīng)典的SIFT算法的思想并對其進行優(yōu)化,設計了改進的目標匹配和運動目標跟蹤算法。本論文的主要研究內容包括:(1)使用體現(xiàn)圖像信息量的圖像熵進行關鍵點閾值判斷,設計了自適應的關鍵點閾值調整方法;(2)引入基于直方圖距離計算的EMD距離,同時基于SIFT算法特性,將改進EMD算法與多梯度方向SIFT特征點相結合進行距離的比對和運算的剪枝;(3)針對于多目標識別,設計了基于SIFT特征點雙向匹配的改進算法;(4)設計一種融合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算法檢測模塊進行調整。根據(jù)上述設計思路,本文實現(xiàn)了基于改進的SIFT算法的目標識別和目標跟蹤算法,并通過測試數(shù)據(jù)集對所設計的算法進行了驗證。實驗結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)實現(xiàn)多目標場景中的識別檢測;(4)較好的解決TLD算法的跟蹤模塊在運動目標長期跟蹤中難以保持魯棒跟蹤的問題。本文所設計的方法,對經(jīng)典的SIFT算法的不足之處做出了針對性的改進,不僅提高了圖像目標的的匹配準確度,并且運算效率相對于原算法也有了較好的進步,在目標的識別和跟蹤應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.
【學位授予單位】：南京理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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