Attitude estimation of space targets can reveal crucial details about payload orientation, movement intentions, and observation area, all of which are vital in space situational awareness. Till now, inverse synthetic ...
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Attitude estimation of space targets can reveal crucial details about payload orientation, movement intentions, and observation area, all of which are vital in space situational awareness. Till now, inverse synthetic aperture radar (ISAR) has become a mainstream sensor for space target observation, providing rich information for space target attitude estimation. Based on projection matrix and linearstructure extracted from ISAR images, a space target attitude estimation method is proposed in this work. The main contribution falls on two parts. On the one hand, linearstructure is derived based on the peak accumulation values of original ISAR images rather than binary images. On the other hand, the space target attitude information is effectively estimated based on the projection matrix theory and linear structure extraction results. Experimental studies with measured and simulated data demonstrate the effectiveness of the proposed method.
LiDAR is a useful technology for gathering point cloud data from its environment and has been adapted to many applications. We use a cost-efficient LiDAR system attached to a moving object to estimate the location of ...
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LiDAR is a useful technology for gathering point cloud data from its environment and has been adapted to many applications. We use a cost-efficient LiDAR system attached to a moving object to estimate the location of the moving object using referenced linearstructures. In the stationary state, the accuracy of extracting linearstructures is low given the low-cost LiDAR. We propose a merging scheme for the LiDAR data frames to improve the accuracy by using the movement of the moving object. The proposed scheme tries to find the optimal window size by means of an entropy analysis. The optimal window size is determined by finding the minimum point between the entropy indicator of the ideal result and the entropy indicator of the actual result of each window size. The proposed indicator can describe the accuracy of the entire path of the moving object at each window size using a simple single value. The experimental results show that the proposed scheme can improve the linear structure extraction accuracy.
Corneal collagen structure, which plays an important role in determining visual acuity, has drawn a lot of research attention to exploring its geometric properties. Advancement of nonlinear optical (NLO) imaging provi...
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ISBN:
(纸本)9781450347532
Corneal collagen structure, which plays an important role in determining visual acuity, has drawn a lot of research attention to exploring its geometric properties. Advancement of nonlinear optical (NLO) imaging provides a potential way for capturing fiber-level structure of cornea, however, the artifacts introduced by the NLO imaging process make image segmentation on such images a bottleneck for further analysis. Especially, the existing methods fail to preserve the branching points which are important for mechanical analysis. In this paper, we propose a hybrid image segmentation method, which integrates seeded region growing and iterative voting. Results show that our algorithm outperforms state-of-the-art techniques in segmenting fibers from background while preserving branching points. Finally, we show that, based on the segmentation result, branching points and the width of fibers can be determined more accurately than the other methods, which is critical for mechanical analysis on corneal structure.
Automatic extraction of road and linearstructure from remote sensing images is a very important problem. This paper uses a stochastic geometric model for automatic extraction of tine network (roads, rivers...) from r...
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ISBN:
(纸本)9780769535630
Automatic extraction of road and linearstructure from remote sensing images is a very important problem. This paper uses a stochastic geometric model for automatic extraction of tine network (roads, rivers...) from remotely sensing images. The line network in the observed scene Is modeled by marked point process. Firstly, the thick branches are detected using a segment process. Secondly, a polygon tree is derived from this first result to represent the main part of the line network. Finally, new branch lines are extracted using a recursive algorithm based on the modeling of the descendants of a given branch line by a polyline process in the neighborhood of this branch tine. Experimental results show the relevance of the object process models.
This paper deals with the robust extraction of line segments in noisy binary images. The principle is to find maximal geodesic arcs lying in the shape. A rigorous digital framework is first presented, then an efficien...
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ISBN:
(纸本)081941624X
This paper deals with the robust extraction of line segments in noisy binary images. The principle is to find maximal geodesic arcs lying in the shape. A rigorous digital framework is first presented, then an efficient algorithm is described and results on real data presented. Some hints for filling the gaps between disconnected arcs are finally exposed.
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