基于相似度模型耦合角度制約規(guī)則的圖像匹配算法

宋大偉，馬鳳娟，趙 華

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基于相似度模型耦合角度制約規(guī)則的圖像匹配算法

*宋大偉1，馬鳳娟1，趙 華2

（1. 濰坊工程職業(yè)學(xué)院，山東，濰坊 262500；2. 山東科技大學(xué)，山東，青島 266590 )

圖像匹配；FAST特征檢測；SURF機(jī)制；SSIM模型；相似度模型；角度制約規(guī)則

0 引言

數(shù)字圖像給人們的生活帶來了便利，為當(dāng)代信息的傳遞提供了媒介[1]。人們通過數(shù)字圖像可以實現(xiàn)快速的信息傳遞以及便捷的信息儲存。目前，數(shù)字圖像匹配技術(shù)已被應(yīng)用到了刑事偵查、目標(biāo)追蹤以及人臉識別等多項技術(shù)范疇，是當(dāng)下熱門的研究技術(shù)之一[2]。

數(shù)字圖像匹配技術(shù)的發(fā)展對人們有著重要的影響，目前出現(xiàn)了較多的數(shù)字圖像匹配方法。如Hossain等人[3]設(shè)計了利用局部信息獲取特征描述子的方法，通過局部信息的灰度等特征實現(xiàn)匹配，實驗結(jié)果表明，這種方法能夠較好地獲取圖像的特征信息，較準(zhǔn)確地對圖像特征進(jìn)行匹配。Zhao等人[4]設(shè)計了一種利用線段方法對多模態(tài)圖像進(jìn)行匹配的技術(shù)，通過多模態(tài)魯棒線段描述符對圖像特征進(jìn)行區(qū)分，并通過描述符的相似性檢測獲取匹配結(jié)果。張煥龍等人[5]對布谷鳥算法進(jìn)行研究，將其引入圖像匹配，通過獲取圖像的HOG特征，利用布谷鳥搜索方法獲取特征匹配結(jié)果。Tsai等人[6]將不同圖像的特征描述符進(jìn)行比較，設(shè)計了分類環(huán)機(jī)制，將未校正的匹配對進(jìn)行濾除，提高匹配結(jié)果的正確性。

1 所提圖像匹配算法設(shè)計

圖1 所提算法的匹配過程

1.1 圖像特征檢測

1.2 圖像特征描述

1.3 圖像特征匹配

對于匹配正確的特征點(diǎn)而言，其與特征點(diǎn)間構(gòu)成的角度具有一定的接近度。為了進(jìn)一步提高圖像特征匹配的正確率，在此，將利用特征點(diǎn)間角度，建立角度制約規(guī)則，對特征點(diǎn)進(jìn)行精匹配。

3）將三對正確匹配點(diǎn)中的一組替換成一對新的匹配點(diǎn)，并返回步驟(1)，若新加入的匹配點(diǎn)組成的角度差值滿足步驟(2)的判斷條件，則判定新加入的匹配點(diǎn)對為正確匹配點(diǎn)對，否則為錯誤匹配點(diǎn)對給予剔除，從而實現(xiàn)特征的精匹配。

2 實驗結(jié)果

圖2 不同算法對光照度變化圖像的匹配效果

將圖5作為圖像A，將其進(jìn)行不同角度的旋轉(zhuǎn)，形成圖像B。利用不同算法對圖像A與旋轉(zhuǎn)形成的圖像B進(jìn)行匹配，并對匹配結(jié)果的正確度進(jìn)行統(tǒng)計，以測試所提算法的匹配性能。

圖5 測試目標(biāo)

圖6 匹配正確度的測試結(jié)果

3 結(jié)論

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Image Matching Method Based on Similarity Model Coupling Angle Constraint Rule

*SONG Da-wei1，MA Feng-juan1，ZHAO Hua2

(1. Weifang engineering Career Academy, Weifang, Shandong 262500, China;2. Shandong University of Science and Technology, Qingdao, Shandong 266590, China)

The current image matching methods mainly achieve image matching by measuring the distance, which neglect the similarity between images and result in more mismatches and poor robustness. In this paper, an image matching algorithm based on similarity degree model and coupling angle constraint rule is proposed. High-speed and high-accuracy feature detection method is used to detect the image features, and the feature points with high accuracy can be obtained fast, which is helpful to improve the matching accuracy of the algorithm. Using the feature description mechanism, the feature points are described by calculating the wavelet response values in the feature circle domain. The structure similarity model is introduced and combined with Euclidean distance model to construct similarity model. The feature points are roughly matched from the aspects of structure similarity and measurement distance. The cosine relation of feature points is used to calculate the angle between feature points, and the angle restriction rules are established to match the feature points accurately. Experimental results show that this matching algorithm has better matching performance and higher matching accuracy compared with the typical matching method.

image matching; FAST feature detection; SURF mechanism; SSIM model; similarity model; angle constraint rule

TP391

A

10.3969/j.issn.1674-8085.2019.02.008

1674-8085(2019)02-0039-06

2018-11-23；

2018-12-27

山東省自然科學(xué)基金項目(ZR2013FQ030)

*宋大偉(1976-)，男，山東濰坊人，副教授，主要從事圖像處理、計算機(jī)網(wǎng)絡(luò)技術(shù)、數(shù)據(jù)庫技術(shù)等方面的研究(E-mail: songdiv@sohu.com);

馬鳳娟(1975-)，女，山東濰坊人，副教授，主要從事計算機(jī)圖像、多媒體技術(shù)、數(shù)據(jù)庫等方面的研究(E-mail: juanfm@tom.com);

趙 華(1980-)，女，山東泗水人，副教授，博士，主要從事圖像處理、話題檢測與跟蹤、網(wǎng)絡(luò)輿情挖掘等方面的研究(E-mail:Zhaoh19SLK80S@163.com).