A Method to Enhance Homogeneous Distribution of Matched Features for Image Matching

Year 2020, Volume: 7 Issue: 1, 102 - 107, 26.04.2020

Baran Gülmez Fatih Demirtas İrem Yıldırım Uğur Murat Leloğlu Mustafa Yaman Eylem Tuğçe Güneyi

https://doi.org/10.30897/ijegeo.710634

Abstract

Features are distinctive landmarks of an image. There are various feature detection and description algorithms. Many computer vision algorithms require matching of features from two images. Large number of correct matches with homogeneous distribution in the images is needed for robustness of the image matching. The matches are generally obtained using a feature distance threshold and ambiguous matches are rejected using a ratio test. This paper proposes a method that can be added to image matching pipeline for enhancing homogeneous distribution and increasing the number of matched feature points. After successfully matching an image pair, spatially close feature points go through an elimination process which aims to decrease ambiguity at the second matching step. Then, a coarse geometric transformation between two images is calculated, through which the detected feature points in one image (i.e. the moving image) are projected to the other image (i.e. the fixed image). Then, feature points from the moving image are matched to neighboring feature points of the fixed image within a pre-determined spatial distance. This narrows down the possible candidates and enables less correct matches being rejected because of the ratio test. The effectiveness and feasibility of our method is demonstrated with experiments on images acquired from a drone camera during flight.

Keywords

: Feature, Homogeneity, Feature Matching, Image Matching, Feature Distribution

Supporting Institution

TUBITAK-TEYDEB 1511

Project Number

1170179

Thanks

This study was partially supported in the framework of TUBITAK-TEYDEB 1511 program project no: 1170179.

References

• Alcantarilla, P., Bartoli, A., Davison, A. (2012). KAZE features. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7577 LNCS, 214-227.
• Bansal, P., Ardell, A. (1972). Average nearest-neighbor distances between uniformly distributed finite particles. Metallography, 5(2), 97-111.
• Baumberg, A. (2000). Reliable feature matching across widely separated views. Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662). 1, pp. 774-781. IEEE Comput. Soc.
• Bay, H., Ess, A., Tuytelaars, T., Van Gool, L. (2008). Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding, 110(3), 346-359.
• Bayırhan, İ., Gazioğlu, C. (2019). Use of Unmanned Aerial Vehicles (UAV) And Marine Environment Simulator in Oil Pollution Investigations, Investigations, International Symposium on Applied Geoinformatics (ISAG-2019), 1-6
• Brox, T., Malik, J. (2011). Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3), 500-513.
• Canclini, A., Cesana, M., Redondi, A., Tagliasacchi, M., Ascenso, J., Cilla, R. (2013). Evaluation of low-complexity visual feature detectors and descriptors. 2013 18th International Conference on Digital Signal Processing (DSP) (pp. 1-7). IEEE.
• Cheng, G., Han, J. (2016). A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 11-28.
• Deriche, R., Zhang, Z., Luong, Q.-T., Faugeras, O. (1994). Robust recovery of the epipolar geometry for an uncalibrated stereo rig. In R. Deriche, Z. Zhang, Q.-T. Luong, & O. Faugeras.
• Doucette, P., Antonisse, J., Braun, A., Lenihan, M., Brennan, M. (2013). Image georegistration methods: A framework for application guidelines. Proceedings - Applied Imagery Pattern Recognition Workshop. Institute of Electrical and Electronics Engineers Inc.
• Ghosh, D., Kaabouch, N. (2016). A survey on image mosaicing techniques. Journal of Visual Communication and Image Representation, 34, 1-11.
• Goshtasby, A. (1986). Piecewise linear mapping functions for image registration. Pattern Recognition, 19(6), 459-466.
• Goshtasby, A. (1988). Image registration by local approximation methods. Image and Vision Computing, 6(4), 255-261.
• Li, Y., Wang, S., Tian, Q., Ding, X. (2015). A survey of recent advances in visual feature detection. Neurocomputing, 149(PB), 736-751.
• Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision.
• Lu, Y., Xue, Z., Xia, G., Zhang, L. (2018). A survey on vision-based UAV navigation. Geo-Spatial Information Science, 21(1), 21-32.
• Matas, J., Chum, O., Urban, M., Pajdla, T. (2004). Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing. 22, pp. 761-767. Elsevier Ltd.
• Mishkin, D., Matas, J., Perdoch, M. (2015). MODS: Fast and robust method for two-view matching. Computer Vision and Image Understanding, 141, 81-93.
• Morel, J.-M., Yu, G. (2009). ASIFT: A New Framework for Fully Affine Invariant Image Comparison. SIAM Journal on Imaging Sciences, 2(2), 438-469.
• Moreno-Noguer, F. (2011). Deformation and illumination invariant feature point descriptor. CVPR 2011 (pp. 1593- 1600). IEEE.
• Mur-Artal, R., Tardos, J. (2017). ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras. IEEE Transactions on Robotics, 33(5), 1255-1262.
• Salahat, E., Qasaimeh, M. (2017). Recent advances in features extraction and description algorithms: A comprehensive survey. 2017 IEEE International Conference on Industrial Technology (ICIT) (pp. 1059- 1063). IEEE.
• Sanfourche, M., Delaune, J., Besnerais, G., Plinval, H., Israel, J., Cornic, P., Plyer, A. (2012). Perception for UAV: Vision-Based Navigation and Environment Modeling. AerospaceLab(4), p. 1-19.
• Sedaghat, A., Mohammadi, N. (2018). Uniform competency-based local feature extraction for remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 135, 142-157.
• Taketomi, T., Uchiyama, H., Ikeda, S. (2017). Visual SLAM algorithms: a survey from 2010 to 2016. IPSJ Transactions on Computer Vision and Applications, 9 (1), 16.
• Yahyanejad, S., Rinner, B. (2015). A fast and mobile system for registration of low-altitude visual and thermal aerial images using multiple small-scale UAVs. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 189-202.
• Yu, M., Yang, H., Deng, K., Yuan, K. (2018). Registrating oblique images by integrating affine and scale-invariant features. International Journal of Remote Sensing, 39(10), 3386-3405.
• Zhang, Q., Wang, Y., Wang, L. (2015). Registration of images with affine geometric distortion based on Maximally Stable Extremal Regions and phase congruency. Image and Vision Computing, 36, 23-39.
• Zhang, Z., Deriche, R., Faugeras, O., Luong, Q.-T. (1995). A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence, 78(1-2), 87-119.
• Zhu, Q., Wu, B., Xu, Z.-X. (2006). Seed Point Selection Method for Triangle Constrained Image Matching Propagation. IEEE Geoscience and Remote Sensing Letters, 3(2), 207-211.
• Zitová, B., Flusser, J. (2003). Image registration methods: a survey. Image and Vision Computing, 21(11), 977-1000.