Abstract
Based on characteristics or similarity metrics, image registration aligns various images acquired at different time frames or from different points of view. This is a common issue in image processing applications where it is necessary to do a parallel or collaborative analysis of two or more images of the same scene captured by different sensors, or images captured by the same sensor at various times. In this paper, we present an intelligent framework that uses a proposed hybrid structural feature extraction technique to estimate the input image’s transformation parameters using a ground truth image. This hybrid feature extraction technique extracts a few potential structural features as well as the object’s shape. The proposed technique’s effectiveness is compared with various prior state-of-the-art methods on publicly available image databases like SAR (synthetic aperture radar), medical, and standard benchmark images. The results of the parameter estimation experiments reveal that this hybrid feature extraction technique can reduce incorrect image registration parameter predictions in terms of mean square error, peak signal-to-noise ratio, root mean square error, average absolute intensity difference, and normalised cross-correlation coefficient and construct a good intelligent predictive framework.
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Fonseca LMG, Manjunath BS (1996) Registration techniques for multisensor remotely sensed imagery. Photogramm Eng Remote Sens 562:1049–1056
Li H, Manjunath BS, Mitra SK (1995) A contour based approach to multisensor image registration. IEEE Trans Image Process 4:320–334
Brown LG (1992) A survey of image registration techniques. Comput Surv 24:325–376
Cideciyan AV, Jacobson SG, Kemp CM, Knighton RW, Nagel JH (1992) Registration of high resolution images of the retina. Proc SPIE Med Imaging VI: Image Process 1652:310–322
Cole-Rhodes AA, Johnson KL, LeMoigne J, Zavorin I (2003) Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient. IEEE Trans Image Process 12:1495–1511
Goncalves H, Corte-Real L, Goncalves JA (2011) Automatic image registration through image segmentation and sift. IEEE Trans Geosci Remote Sens 97:2589–2600
Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8:679–698
Marr D, Hildreth E (1980) Theory of edge detection. Proc R Soc Lond 207:187–217
Moss S, Hancock ER (1997) Multiple line-template matching with em algorithm. Pattern Recognit Lett 18:1283–1292
Shin D, Pollard JK, Muller JP (1997) Accurate geometric correction of atsr images. IEEE Trans Geosci Remote Sens 35:997–1006
Banerjee S, Mukherjee DDMDP (1995) Point landmarks for registration of ct and nmr images. Pattern Recognit Lett 16:1033–1042
Bhattacharya D, Sinha S (1997) Invariance of stereo images via theory of complex moments. Pattern Recognit 30:1373–1386
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection, pp 886–8931
Luo Z, Chen J, Takiguchi T, Ariki Y (2015) Rotation-invariant histograms of oriented gradients for local patch robust representation, pp 196–199. https://doi.org/10.1109/APSIPA.2015.7415502
Candes EJ, Donoho DL (2000) Curvelets–a surprisingly effective nonadaptive representation for objects with edges. Vanderbilt University Press, Nashville, pp 105–120
Candes EJ (1999) Harmonic analysis of neural networks. Appl Comput Harmonic Anal 6:197–218
Patil AA, Singhai R, Singhai J (2010) Curvelet transform based super-resolution using sub-pixel image registration. In: 2nd computer science and electronic engineering conference (CEEC)
Candes EJ, Donoho DL (1999) Ridgelets: the key to higher-dimensional intermittency. R. Soc. Lond. Philos. Trans. Ser. A Math. Phys. Eng. Sci. 357:2495–2509
Nambiar R, Desai U, Shetty V (2014) Medical image fusion analysis using curvelet transform. In: International conference on advanced computing, communication and information sciences, pp 1–12
Veerasundari R, Umamaheswari S (2016) Enhanced satellite image registration and fusion using 2d curvelet transform. In: 7th Annual international conference on computer science education innovation and technology
Deepali B, Dimple C (2016) Nsct based spine image fusion. Int J Ind Electron Electr Eng 4
Tomasi C, Kanade T (1991) Detection and tracking of point features. Technical Report CMU, pp 91–132
Nalina S, Mal A, Vani KS, Subhalakshmi K (2014) Image based velocity estimation by feature extraction and sub-pixel image matching. Int J Eng Res Technol 3
Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. In: European conference on computer vision, vol 1
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 5:91–110
Bay H, Tuytelaars T, Gool LV (2006) Surf: speeded up robust features. In: European conference on computer vision, pp 404–417
Calonder M, Lepetit V, Strecha C, Fua P (2010) Brief: Binary robust independent elementary features. In: European conference on computer vision
Rublee E, Rabaud V, Konolige K, Bradski G (2011) Orb: and efficient alternative to sift or surf. In: IEEE international conference on computer vision
Lu Y, Gao K, Zhang T, Xu T (2018) A novel image registration approach via combining local features and geometric invariants. PLoS ONE 13:e0190383
Wang K, Wang H, Wu M, Wang Z, Liu J (2018) A method for spectral image registration based on feature maximum submatr. EURASIP J Image Video Process 140:1
Dellinger F, Delon J, Gousseau Y, Michel J, Tupin F (2015) Sift: a sift-like algorithm for sar images. IEEE Trans Geosci Remote Sens 53:453–466
Dong J, Soatto S (2015) Domain-size pooling in local descriptors: Dsp-sift. In: IEEE conference on computer vision and pattern recognition (CVPR)
Ke Y, Sukthankar R (2004) Pca-sift: a more distinctive representation for local image descriptors. In: IEEE conference on computer vision and pattern recognition (CVPR)
Sedaghat A, Mokhtarzade M, Ebadi H (2015) Uniform robust scale-invariant feature matching for optical remote sensing images. IEEE Trans Geosci Remote Sens 53:5283–5293
Teke M, Temizel A (2010) Multi-spectral satellite image registration using scale-restricted surf ICPR 2010, pp 2310–2313
Gesto-Diaz M, Tombari F, Gonzalez-Aguilera D, Lopez-Fernandez L, Rodriguez-Gonzalvez P (2017) Feature matching evaluation for multimodal correspondence. ISPRS J Photogramm Remote Sens 129:179–188
Kelman A, Sofka M, Stewart CV (2007) Keypoint descriptors for matching across multiple image modalities and non-linear intensity variations. CVPR 2007:3257–3263
Murphy JM, Le Moigne J, Harding DJ (2016) Automatic image registration of multimodal remotely sensed data with global shearlet features. IEEE Trans Geosci Remote Sens 54:1685–1704
Rodriguez Salas R, Dokladal P, Dokladalova E (2021) Rotation invariant networks for image classification for hpc and embedded systems. Electronics 10:139. https://doi.org/10.3390/electronics10020139
Sifre L, Mallat S (2013) Rotation, scaling and deformation invariant scattering for texture discrimination, pp 1233–1240. https://doi.org/10.1109/CVPR.2013.163
Marcos D, Volpi M, Komodakis N, Tuia D (2017) Rotation equivariant vector field networks, pp 5058–5067. https://doi.org/10.1109/ICCV.2017.540
Zhou Y, Ye Q, Qiu Q, Jiao J (2017) Oriented response networks, pp 4961–4970. https://doi.org/10.1109/CVPR.2017.527
Ravanbakhsh M, Fraser CS (2013) A comparative study of dem registration approaches. J Spatial Sci 58:79–89
Zavorin I, Le Moigne J (2005) Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery. IEEE Trans Image Process 14:770–782
Ahmed DT, Singh D, Singh D, Raman B, Subramanian R (2014) Application of klt (kanade-lucas-tomasi) tracker for hotspot observation
Xia GS, Hu J, Hu F, Shi B, Bai X, Zhong Y, Zhang L, Lu X (2017) Aid: a benchmark data set for performance evaluation of aerial scene classification. IEEE Trans Geosci Remote Sens 55:3965–3981
http://www.med.harvard.edu/aanlib/home.html. (2004)
Willmott CJ, Matsuura K (2005) of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance. Clim Res 30:79–82
Mousavi Kahaki SM, Nordin MJ, Ashtari AH, Zahra SJ (2016) Invariant feature matching for image registration application based on new dissimilarity of spatial features. PLoS ONE 11:e0149710
Daneshvar S, Ghassemian H (2005) A hybrid algorithm for medical image registration. Conf Proc IEEE Eng Med Biol Soc 2005:3272
Chakravarti IM, Laha RG, Roy J (1967) Handbook of methods of applied statistics, vol I. Wiley, New York
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Hazra, J., Chowdhury, A.R., Dasgupta, K. et al. A hybrid structural feature extraction-based intelligent predictive approach for image registration. Innovations Syst Softw Eng 20, 643–651 (2024). https://doi.org/10.1007/s11334-022-00436-8
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DOI: https://doi.org/10.1007/s11334-022-00436-8