基于二进制特征描述器的图像匹配算法
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摘要
图像特征点匹配在视觉系统中有广泛的应用。针对加速分割测试特征FAST和二进制稳健基元独立特征BRIEF算法中存在的问题进行改进。首先,在FAST算法中使用简化模板提取图像特征点,通过构建图像金字塔实现尺度不变性。接着,根据人类视觉系统原理改进BRIEF算法的点对采样模式,并通过特征点方向的计算实现图像的旋转不变性。最后,使用易于计算的海明距离度量各特征点的相似度实现特征匹配。实验表明,提出的图像匹配算法性能优于其他算法,而且运行速度更快。
A large number of vision applications rely on matching keypoints across images.To overcome the shortcomings of the Features from Accelerated Segment Test(FAST)and the Binary Robust Independent Elementary Features(BRIEF),we propose an improved image matching algorithm.Firstly,simple mask is applied in the FAST algorithm to extract image keypoints,and scale invariance is achieved by the image pyramid.The sampling pattern in the BRIEF is modified according to the principles of the human visual system,and the keypoints with rotation invariance are achieved by the orientation estimation.Furthermore,the keypoints descriptor similarities are evaluated by using the Hamming distance,which is very efficient to compute.Experimental results show that the proposed algorithm is not only faster to compute and also more robust than other algorithms.
A large number of vision applications rely on matching keypoints across images.To overcome the shortcomings of the Features from Accelerated Segment Test(FAST)and the Binary Robust Independent Elementary Features(BRIEF),we propose an improved image matching algorithm.Firstly,simple mask is applied in the FAST algorithm to extract image keypoints,and scale invariance is achieved by the image pyramid.The sampling pattern in the BRIEF is modified according to the principles of the human visual system,and the keypoints with rotation invariance are achieved by the orientation estimation.Furthermore,the keypoints descriptor similarities are evaluated by using the Hamming distance,which is very efficient to compute.Experimental results show that the proposed algorithm is not only faster to compute and also more robust than other algorithms.
引文
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[7]Mikolajajczyk K,Schmid C.A performance evaluation of local descriptors[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1615-1630.
[8]Calonder M,Leptit V,Strecha C.BRIEF:Binary robust independent elementary features[C]∥Proc of European Conference on Computer Vision,2010:778-792.
[9]Rublee E,Rabaud V,Konolige K,et al.ORB:An efficient alternative to SIFT or SURF[C]∥Proc of International Conference on Computer Vision,2011:2564-2571.
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[14]Vandergheynst P,Ortiz R,Alahi A.FREAK:Fast retina keypoint[C]∥Proc of Computer Vision and Pattern Recognition,2012:510-517.
[2]Harris C,Stephens M.A combined corner and edge detector[C]∥Proc of the 4th Alvey Vision Conference,1988:147-151.
[3]Lowe D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[4]Bay H,Tuytelaars T,Gool L V.SURF:Speeded up robust features[C]∥Proc of European Conference on Computer Vision,2006:404-417.
[5]Rosten E,Porter R,Drummond T.Faster and better:A machine learning approach to corner detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(1):105-119.
[6]Ke Y,Sukthankar R.Pca-sift:A more distinctive representation for local image descriptors[C]∥Proc of Computer Vision and Pattern Recognition,2004:506-513.
[7]Mikolajajczyk K,Schmid C.A performance evaluation of local descriptors[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1615-1630.
[8]Calonder M,Leptit V,Strecha C.BRIEF:Binary robust independent elementary features[C]∥Proc of European Conference on Computer Vision,2010:778-792.
[9]Rublee E,Rabaud V,Konolige K,et al.ORB:An efficient alternative to SIFT or SURF[C]∥Proc of International Conference on Computer Vision,2011:2564-2571.
[10]Trzcinski T,Christoudias M,Fua P,et al.Boosting binary keypoint descriptors[C]∥Proc of Computer Vision and Pattern Recognition,2013:510-517.
[11]Leutenegger S,Chli M,Siegwart R.BRISK:Binary Robust Invariant Scalable Keypoints[C]∥Proc of International Conference on Computer Vision,2011:2548-2555.
[12]Mair E,Hager G,Burschka D,et al.Adaptive and generic corner detection based on the accelerated segment test[C]∥Proc of European Conference on Computer Vision,2010:183-196.
[13]Smith S M,Brady J M.SUSAN—a new approach to low level image processing[J].International Journal of Computer Vision,1997,23(1):45-78.
[14]Vandergheynst P,Ortiz R,Alahi A.FREAK:Fast retina keypoint[C]∥Proc of Computer Vision and Pattern Recognition,2012:510-517.