Groupwise Registration of Aerial Images

This paper addresses the task of time separated aerial image registration. The ability to solve this problem accurately and reliably is important for a variety of subsequent image understanding applications. The principal challenge lies in the extent and nature of transient appearance variation that a land area can undergo, such as that caused by the change in illumination conditions, seasonal variations, or the occlusion by non-persistent objects (people, cars). Our work introduces several novelties: (i) unlike all previous work on aerial image registration, we approach the problem using a set-based paradigm; (ii) we show how local, pairwise constraints can be used to enforce a globally good registration using a constraints graph structure; (iii) we show how a simple holistic representation derived from raw aerial images can be used as a basic building block of the constraints graph in a manner which achieves both high registration accuracy and speed. We demonstrate: (i) that the proposed method outperforms the state-of-the-art for pair-wise registration already, achieving greater accuracy and reliability, while at the same time reducing the computational cost of the task; and (ii) that the increase in the number of available images in a set consistently reduces the average registration error.

[1]  Hui.  A Contour-Based Approach to Multisensor Image Registration , 1995 .

[2]  Ognjen Arandjelovic,et al.  Making the most of the self-quotient image in face recognition , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[3]  B. N. Chatterji,et al.  An FFT-based technique for translation, rotation, and scale-invariant image registration , 1996, IEEE Trans. Image Process..

[4]  Hassan Foroosh,et al.  Extension of phase correlation to subpixel registration , 2002, IEEE Trans. Image Process..

[5]  Hossein Mobahi,et al.  Toward a Practical Face Recognition System: Robust Alignment and Illumination by Sparse Representation , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[7]  Hui.  A Contour-Based Approach to Multisensor Image Registration , 2009 .

[8]  Michael Unser,et al.  A pyramid approach to subpixel registration based on intensity , 1998, IEEE Trans. Image Process..

[9]  Ting Chen,et al.  Group-Wise Point-Set Registration Using a Novel CDF-Based Havrda-Charvát Divergence , 2009, International Journal of Computer Vision.

[10]  D. Holcomb,et al.  Optimizing the High-Pass Filter Addition Technique for Image Fusion , 2007 .

[11]  Geoffrey E. Hinton,et al.  Learning to Label Aerial Images from Noisy Data , 2012, ICML.

[12]  Stefan Klein,et al.  Nonrigid registration of dynamic medical imaging data using nD + t B-splines and a groupwise optimization approach , 2011, Medical Image Anal..

[13]  Ognjen Arandjelovic,et al.  Gradient Edge Map Features for Frontal Face Recognition under Extreme Illumination Changes , 2012, BMVC.

[14]  Ognjen Arandjelovic,et al.  Colour invariants under a non-linear photometric camera model and their application to face recognition from video , 2012, Pattern Recognit..

[15]  Chengjun Liu,et al.  A Hybrid Color and Frequency Features Method for Face Recognition , 2008, IEEE Transactions on Image Processing.

[16]  David A. Clausi,et al.  ARRSI: Automatic Registration of Remote-Sensing Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Andrew W. Fitzgibbon,et al.  Bundle Adjustment - A Modern Synthesis , 1999, Workshop on Vision Algorithms.

[18]  Ognjen Arandjelovic.  Computationally efficient application of the generic shape-illumination invariant to face recognition from video , 2012, Pattern Recognit..

[19]  Geoffrey E. Hinton,et al.  Learning to Detect Roads in High-Resolution Aerial Images , 2010, ECCV.

[20]  Svetha Venkatesh,et al.  Efficient and accurate set-based registration of time-separated aerial images , 2015, Pattern Recognit..

[21]  Ramakant Nevatia,et al.  Detection and Modeling of Buildings from Multiple Aerial Images , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[23]  Ognjen Arandjelovic,et al.  Reading Ancient Coins: Automatically Identifying Denarii Using Obverse Legend Seeded Retrieval , 2012, ECCV.

[24]  Nassir Navab,et al.  Simultaneous Registration of Multiple Images: Similarity Metrics and Efficient Optimization , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Pratim Ghosh,et al.  Robust Simultaneous Registration and Segmentation with Sparse Error Reconstruction , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Roberto Cipolla,et al.  Achieving robust face recognition from video by combining a weak photometric model and a learnt generic face invariant , 2013, Pattern Recognit..

[27]  Baba C. Vemuri,et al.  USSR: A Unified Framework for Simultaneous Smoothing, Segmentation, and Registration of Multiple Images , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[28]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[29]  Roberto Cipolla,et al.  A new look at filtering techniques for illumination invariance in automatic face recognition , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[30]  Hongliang Li,et al.  Soft-Change Detection in Optical Satellite Images , 2011, IEEE Geoscience and Remote Sensing Letters.

[31]  Ognjen Arandjelovic.  Unfolding a Face: From Singular to Manifold , 2009, ACCV.

[32]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..