Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Adaptation models (1)
- Aerial image sets (1)
- Colored noise (1)
- Computational modeling (1)
- Coreset (1)
-
- Estimation (1)
- Feature matching (1)
- Geodesic centers (1)
- Georeferencing (1)
- Histograms (1)
- Image color analysis (1)
- M-estimator sample consensus (1)
- Mosaic process (1)
- Optimiazation (1)
- Orthorectification (1)
- Polygonal domains (1)
- Shape fitting (1)
- Shortest paths (1)
- Stochastic point (1)
- Unmanned aerial vehicle (1)
- Ε-kernel (1)
Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Computer Vision–Based Orthorectification And Georeferencing Of Aerial Image Sets, Mohammadreza Faraji, Xiaojun Qi, Austin Jensen
Computer Vision–Based Orthorectification And Georeferencing Of Aerial Image Sets, Mohammadreza Faraji, Xiaojun Qi, Austin Jensen
Computer Science Faculty and Staff Publications
Generating a georeferenced mosaic map from unmanned aerial vehicle (UAV)imagery is a challenging task. Direct and indirect georeferencing methods may fail to generate an accurate mosaic map due to the erroneous exterior orientation parameters stored in the inertial measurement unit (IMU), erroneous global positioning system (GPS) data, and difficulty inlocating ground control points (GCPs) or having a sufficient number of GCPs. This paperpresents a practical framework to orthorectify and georeference aerial images using the robustfeatures-based matching method. The proposed georeferencing process is fully automatic and does not require any GCPs. It is also a near real-time process which can be …
On The Geodesic Centers Of Polygonal Domains, Haitao Wang
On The Geodesic Centers Of Polygonal Domains, Haitao Wang
Computer Science Faculty and Staff Publications
In this paper, we study the problem of computing Euclidean geodesic centers of a polygonal domain P of n vertices. We give a necessary condition for a point being a geodesic center. We show that there is at most one geodesic center among all points of P that have topologically-equivalent shortest path maps. This implies that the total number of geodesic centers is bounded by the size of the shortest path map equivalence decomposition of P, which is known to be O(n^{10}). One key observation is a pi-range property on shortest path lengths when points are moving. With these observations, …
Ε-Kernel Coresets For Stochastic Points, Haitao Wang, Lingxiao Huang, Jian Li, Jeff Mark Phillips
Ε-Kernel Coresets For Stochastic Points, Haitao Wang, Lingxiao Huang, Jian Li, Jeff Mark Phillips
Computer Science Faculty and Staff Publications
With the dramatic growth in the number of application domains that generate probabilistic, noisy and uncertain data, there has been an increasing interest in designing algorithms for geometric or combinatorial optimization problems over such data. In this paper, we initiate the study of constructing epsilon-kernel coresets for uncertain points. We consider uncertainty in the existential model where each point's location is fixed but only occurs with a certain probability, and the locational model where each point has a probability distribution describing its location. An epsilon-kernel coreset approximates the width of a point set in any direction. We consider approximating the …
Unsupervised Saliency Estimation Based On Robust Hypotheses, Fei Xu, Min Xian, H. D. Cheng, Jianrui Ding, Yingtao Zhang
Unsupervised Saliency Estimation Based On Robust Hypotheses, Fei Xu, Min Xian, H. D. Cheng, Jianrui Ding, Yingtao Zhang
Computer Science Faculty and Staff Publications
Visual saliency estimation based on optimization models is gaining increasing popularity recently. In this paper, we formulate saliency estimation as a quadratic program (QP) problem based on robust hypotheses. First, we propose an adaptive center-based bias hypothesis to replace the most common image center-based center-bias. It calculates the weighted center by utilizing local contrast which is much more robust when the objects are far away from the image center. Second, we model smoothness term on saliency statistics of each color. It forces the pixels with similar colors to have similar saliency statistics. The proposed smoothness term is more robust than …