Abstract
Scale invariant feature transform (SIFT) has limitation in extracting features accurately for the images with small gradient and weak texture caused by low contrast. In order to tackle this problems, this paper proposes an improved SIFT algorithm based on adaptive fractional differential. The method firstly construct a mathematical model of adaptive fractional differential based on local image information, therefore, the relationship between the optimal order and image local information can be built up, and the optimal order at every pixel can be calculated automatically according to the characteristics of image. And an adaptive fractional differential dynamic mask is constructed in term of the optimal order and Riemann–Liouville (R–L) fractional definition. And then it is applied to SIFT algorithm for image matching. The method proposed in this paper is an important extension of SIFT algorithm. The theoretical analysis and experiment results indicate the proposed algorithm is capable of matching image with small gradient or weak texture or weak edge.
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Al-Ayyoub M, Al-Mnayyis N, Alsmirat MA, Alawneh K, Jararweh Y, Gupta BB (2018) Sift based ROI extraction for lumbar disk herniation CAD system from MRI axial scans. J Ambient Intell Hum Comput 6:1–9
Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-up robust features (surf). Comput Vis Image Underst 110(3):346–359
Castillo-Carrión S, Guerrero-Ginel JE (2017) Sift optimization and automation for matching images from multiple temporal sources. Int J Appl Earth Obs Geoinf 57:113–122
Chen CC, Hsieh SL (2015) Using binarization and hashing for efficient SIFT matching. J Vis Commun Image Represent 30(C):86–93
Guler RA, Tari S, Unal G (2016) Landmarks inside the shape: shape matching using image descriptors. Pattern Recognit 49:79–88
Hossein-Nejad Z, Nasri M (2016) Image registration based on SIFT features and adaptive RANSAC transform. In: IEEE on international conference on communication and signal processing
Hu F, Si S, Zhang Y, Sun J (2013) Multi-bilateral filtering algorithm based on adaptive fractional order differentiation. J Image Graph (in Chinese) 18(10):1237–1246
Kelly P, Doherty AR, Hamilton A, Matthews A, Batterham AM, Nelson M et al (2012) Evaluating the feasibility of measuring travel to school using a wearable camera. Am J Prev Med 43(5):546–550
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Luo J, Gwun O (2013) A comparison of sift, PCA-SIFT and SURF. Int J Image Process 3(4):143–152
Ma W, Wen Z, Wu Y, Jiao L, Gong M, Zheng Y et al (2016) Remote sensing image registration with modified sift and enhanced feature matching. IEEE Geosci Remote Sens Lett 14(1):3–7
Mazzeo PL, Spagnolo P, Leo M, Carcagnì P, Coco MD, Distante C (2017) Dense descriptor for visual tracking and robust update model strategy. J Ambient Intell Hum Comput 47:1–11
Podlubny I (2000) Matrix approach to discrete fractional calculus. Fract Calc Appl Anal 3(4):359–386
Pu YF, Siarry P, Chatterjee A, Wang ZN, Yi Z, Liu YG et al (2017) A fractional-order variational framework for retinex: fractional-order partial differential equation based formulation for multi-scale nonlocal contrast enhancement with texture preserving. IEEE Trans Image Process 27(3):1214–1229
Ren Z (2015) Adaptive active contour model driven by fractional order fitting energy. Sig Process 117(C):138–150
Yang Q, Chen D, Zhao T, Chen YQ (2016) Fractional calculus in image processing: a review. Fract Calc Appl Anal 19(5):1222–1249
Yi C, Flores RW, Chincha R, Tian Y (2013) Finding objects for assisting blind people. Netw Model Anal Health Inform Bioinform 2(2):71
Zhang J, Wei Z, Xiao L (2012) Adaptive fractional-order multi-scale method for image denoising. J Math Imaging Vis 43(1):39–49
Zhang B, Zhao Q, Feng W, Sun M (2015a) SIFT-based indoor localization for older adults using wearable camera. In: IEEE biomedical engineering conference, pp 1–2
Zhang G, Chen B, Chen YQ (2015b) Research on image matching combining on fractional differential with scale invariant feature transform. In: ASME 2015 international design engineering technical conferences and computers and information in engineering conference, p V009T07A038
Zhang G, Xu J, Liu J (2017a) A new active contour model based on adaptive fractional order. J Comput Res Dev 54(5):1045–1056 (in Chinese)
Zhang G, Zhu Y, Liu J, Chen YQ (2017b) Image segmentation based on fractional differentiation and RSF model. In: ASME 2017 international design engineering technical conferences and computers and information in engineering conference, pp V009T07A023
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The authors acknowledge the National Natural Science Foundation of China (Grant 61462065).
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Xu, K., Liu, J., Miao, J. et al. An improved SIFT algorithm based on adaptive fractional differential. J Ambient Intell Human Comput 10, 3297–3305 (2019). https://doi.org/10.1007/s12652-018-1055-1
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DOI: https://doi.org/10.1007/s12652-018-1055-1