Recently, various illegal constructions occur significantly in our surroundings, which seriously restrict the orderly development of urban modernization. The 3D point cloud data technology is used to identify the ille...
Recently, various illegal constructions occur significantly in our surroundings, which seriously restrict the orderly development of urban modernization. The 3D point cloud data technology is used to identify the illegal buildings, which could address the problem above effectively. This paper proposes an outdoor illegal construction identification algorithm based on 3D point cloud segmentation. Initially, in order to save memory space and reduce processing time, a lossless point cloud compression method based on minimum spanning tree is proposed. Then, a ground point removing method based on the multi-scale filtering is introduced to increase accuracy. Finally, building clusters on the ground can be obtained using a region growing method, as a result, the illegal construction can be marked. The effectiveness of the proposed algorithm is verified using a publicly data set collected from the International Society for Photogrammetry and Remote Sensing (ISPRS).
In this paper, a distributed extended Kalman filtering algorithm is developed for a class of discrete-time nonlinear systems subject to stochastic disturbances and randomly occurring deception attacks. In order to uti...
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In this paper, a distributed extended Kalman filtering algorithm is developed for a class of discrete-time nonlinear systems subject to stochastic disturbances and randomly occurring deception attacks. In order to utilize the limited communication and computation resources efficiently, the event-triggered communication scheme is introduced such that data transmission is executed only when the predefined condition is violated. Furthermore, a set of independent Bernoulli random variables with known statistical properties is defined to characterize the phenomenon of randomly occurring deception attacks. An upper bound for the estimation error covariance considering the event-triggered meachanism and linearization errors is derived via the varianceconstrained approach. The filter gain for each node can be calculated recursively by solving two Raccati-like difference equations to minimize such an upper bound, which is suitable for online application. Finally, an illustrative example is presented to verify the feasibility and effectiveness of the proposed algorithm.
Dimensionality reduction (DR) is one of the most important tasks to improve the performance of hyperspectral images classification. Recently, a sparse and low-rank graph embedding based method (SLGE) has been proposed...
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Dimensionality reduction (DR) is one of the most important tasks to improve the performance of hyperspectral images classification. Recently, a sparse and low-rank graph embedding based method (SLGE) has been proposed to describe the intrinsic structure of data combined with the local and global constraint simultaneously, which is effective to reduce the dimension of hyperspectral data and obtain a better classification accuracy. However, SLGE is based on an assumption that low-dimensional feature can be obtained utilizing a linear projection. Its performance may degrade under nonlinearly distributed data. Moreover, spatial prior of HSI is not considered in the framework. In this paper, we proposed a novel dimensionality reduction method named spatial-spectral graph-based non-linear embedding (SSGNE). To generate a new graph-trained data, the segmentation strategy based on superpixel is adopted. The spatial-spectral graph is constructed by constraining the sparsity and low-rankness simultaneously on graph-trained data set. Finally, the kernel trick is adopted to extend the general graph embedding framework to nonlinearly space, which fully considers the complexity of real data. Experimental results show that the proposed method outperforms the state-of-the-art methods in terms of the classification accuracy.
We aim to compare functionality of symport/antiport with embedded rewriting to that of symport/antiport accompanied by rewriting, by two-way simulation, in case of tissue P systems with parallel communication. A simul...
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Nowadays,many researches study the negotiation-based order allocation problem in the supply chain environment,aiming to improve the efficiency of the allocation and the profits of the supply chain *** competition and ...
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ISBN:
(纸本)9781538629185
Nowadays,many researches study the negotiation-based order allocation problem in the supply chain environment,aiming to improve the efficiency of the allocation and the profits of the supply chain *** competition and cooperation in supplier’s side are mainly addressed because of independent *** this paper,we analyze an interdependent order allocation problem in a two-echelon supply *** supply chain consists of a manufacturer echelon and a supplier *** to the interdependence,both the competition and cooperation in the manufacturer echelon are *** agent-based negotiation algorithm is developed to support the order allocation process and the conflicts *** show in experiments that orders under various supply chain contexts can be successfully allocated through the algorithm.
This paper develops a coarse-to-fine framework for single-image super-resolution (SR) reconstruction. The coarse-to-fine approach achieves high-quality SR recovery based on the complementary properties of both example...
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In order to receive and stitch sequential images from Unmanned Aerial Vehicle (UAV) synchronously, an improved approach based on feature points matching is proposed. Firstly, global images with overlapping regions are...
In order to receive and stitch sequential images from Unmanned Aerial Vehicle (UAV) synchronously, an improved approach based on feature points matching is proposed. Firstly, global images with overlapping regions are described by Oriented FAST and Rotated BRIEF (ORB) feature. Then Grid-based Motion Statistics (GMS) method is employed to obtain robust feature correspondence from primary matching pairs, these matching pairs are further validated by neighborhood support and used to get transformation matrix. The performance of the proposed algorithm is demonstrated through computer simulated experiments. Experimental results show that the improved method can efficiently solve the problem of smaller-overlapping and less-textured images with real time capability.
The Frequency-tuned method (FT) detection does not work very well when the saliency maps is large and the background environment is complex. Aiming to solve the above problems, this paper improves the traditional FT m...
The Frequency-tuned method (FT) detection does not work very well when the saliency maps is large and the background environment is complex. Aiming to solve the above problems, this paper improves the traditional FT method, which introduces centre enhancement and eigenvalue normalization. The improvements are implemented to three aspects: enhancing the central area, normalizing the LAB colour feature values, and weighting the LAB three-channel information. Experimental results show that the algorithm is superior to the origin FT in the performance of significant detection, accuracy, recall and other performance.
In order to improve the ability of target tracking effectively, an improved MeanShift algorithm is proposed in this paper. The algorithm combines the saliency map from the computer and from the human vision through Ki...
In order to improve the ability of target tracking effectively, an improved MeanShift algorithm is proposed in this paper. The algorithm combines the saliency map from the computer and from the human vision through Kinect, so that it can obtain the fusion saliency. Then an image segmentation algorithm is adopted to get the target, which is modelled for an improved MeanShift tracking framework. Experimental results show that the algorithm can significantly improve the accuracy.
Infrared imaging equipment can work at night and in complex environments, but the resolution of infrared image is not good. Super-resolution (SR) is a very important technology in image processing. It can reconstruct ...
Infrared imaging equipment can work at night and in complex environments, but the resolution of infrared image is not good. Super-resolution (SR) is a very important technology in image processing. It can reconstruct the low-resolution image by signal processing without changing the hardware device. This paper presents a novel infrared super-resolution reconstruction algorithm, which is based on sparse representation. Experiment results show that, compared with interpolation based approach, the proposed algorithm shows a better performance in infrared scenarios.
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