A new descriptor for UAV images mapping by applying discrete local radon

It has been proven that feature based image descriptors are successful in recent years. However, a light and powerful feature descriptor is still needed to match images in real time. In this paper, we presented a new discrete descriptor for areal images that are obtained from UAVs-mapping to increase the speed and accuracy of output ortho-map. This binary descriptor takes the advantage of the local Radon descriptors to create the binary codes. Our preliminary investigations have shown the local Radon transformation can be an effective method to apply in key-point descriptors, especially in aerial image mapping.

[1]  Javad Mohammadi,et al.  Novel Approach for Baseline Detection and Text Line Segmentation , 2012 .

[2]  Johannes K. Chiang,et al.  E-commerce literature trend forecasting: A study of bibliometric methodology , 2010, 4th International Conference on New Trends in Information Science and Service Science.

[3]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[4]  Michael Calonder.  Robust, High-Speed Interest Point Matching for Real-Time Applications , 2010 .

[5]  P. Toft.  The Radon Transform - Theory and Implementation , 1996 .

[6]  Scott Krig,et al.  Computer Vision Metrics: Survey, Taxonomy, and Analysis , 2014 .

[7]  Filiberto Chiabrando,et al.  UAV Deployment Exercise for Mapping Purposes: Evaluation of Emergency Response Applications , 2015, Sensors.

[8]  Jian Sun,et al.  Face recognition with learning-based descriptor , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Jessie Y. C. Chen,et al.  A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction , 2011, Hum. Factors.

[10]  Naima Kaabouch,et al.  A survey on image mosaicing techniques , 2016, J. Vis. Commun. Image Represent..

[11]  Hamid R. Tizhoosh,et al.  Representing Medical Images With Encoded Local Projections , 2018, IEEE Transactions on Biomedical Engineering.

[12]  Hamid R. Tizhoosh,et al.  Local radon descriptors for image search , 2017, 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA).

[13]  Pierre Vandergheynst,et al.  FREAK: Fast Retina Keypoint , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  J. Mohammadi,et al.  Vehicle speed estimation based on the image motion blur using RADON transform , 2010, 2010 2nd International Conference on Signal Processing Systems.

[15]  Hamid R. Tizhoosh,et al.  MinMax Radon Barcodes for Medical Image Retrieval , 2016, ISVC.

[16]  Hamid R. Tizhoosh,et al.  Barcode annotations for medical image retrieval: A preliminary investigation , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[17]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[18]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[20]  Saeid Nahavandi,et al.  Parallel deep solutions for image retrieval from imbalanced medical imaging archives , 2018, Appl. Soft Comput..

[21]  Kratika Sharma,et al.  Classification based survey of image registration methods , 2013, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[22]  Mahdi Bahaghighat,et al.  VISION INSPECTION AND MONITORING OF WIND TURBINE FARMS IN EMERGING SMART GRIDS , 2018 .

[23]  Fariborz Jolai,et al.  Integrating sequence-dependent group scheduling problem and preventive maintenance in flexible flow shops , 2015 .