We bring out a runway extraction method based on rotating projection in this paper, which is consisted of three steps, locating the Region of interest (ROI), edge extraction and line detection. Firstly we employed tem...
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ISBN:
(纸本)9781467386449
We bring out a runway extraction method based on rotating projection in this paper, which is consisted of three steps, locating the Region of interest (ROI), edge extraction and line detection. Firstly we employed template matching to locate the ROI which contains the runway area. Then we use Sobel operator to extract edges. The rotating projection algorithm is proposed to seek the potential straights in ROI, which will be integrated into the real straights by means of improvedk-meansclustering method. Simulations are carried out in the end, and results show that the algorithm proposed in this paper can extract the four boundaries of the runway effectively, while it can reduce 50% of computing time compared with Hough transform.
The algorithm of Redundant Wavelet Transform (RWT) and laws texture measurement is proposed and applied to image segmentation. Based on the characteristics of the indentation images, this article uses texture features...
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ISBN:
(纸本)9783642112751
The algorithm of Redundant Wavelet Transform (RWT) and laws texture measurement is proposed and applied to image segmentation. Based on the characteristics of the indentation images, this article uses texture features to extract the indentation silhouette from the point view of texture segmentation. We adopt Redundant Wavelet Transform and laws texture measurement algorithm to describe the texture characteristics of the indentation image, forming a n-dimensional feature vector, introducing texture features smoothing algorithm based on quadrant to smooth the features. Finally we combine with the improved k-means clustering algorithm to get texture segmentation result. The experiment demonstrates that in the material Vickers hardness image segmentation the proposed algorithm was significantly effective and robust.
Traditional analysis processing on Vickers hardness image is that the picture edge is mainly fitted to a straight line then analyzed and *** hardness picture edge is possible to be curving,which brings a very big fitt...
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Traditional analysis processing on Vickers hardness image is that the picture edge is mainly fitted to a straight line then analyzed and *** hardness picture edge is possible to be curving,which brings a very big fitting *** this paper proposes a method based on image texture feature to do Vickers hardness image *** adopt Redundant Wavelet Transform and laws texture measurement algorithm to describe the texture characteristics of the indentation image,forming a n-dimensional feature vector,introducing texture features smoothing algorithm based on quadrant to smooth the *** we combine with the improved k-means clustering algorithm to get texture segmentation result,which avoids the uncertainty introduced by line fitting error and the measurement error of the arithmetic mean of the two impress diagonal length d1 and d2.
Considering the nonlinea r, time-varying and ripple coupling properties in the hydraulic servo system, a two-stage Radial Basis Function （RBF） neural network model is proposed to realize the failure detection and fa...
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Considering the nonlinea r, time-varying and ripple coupling properties in the hydraulic servo system, a two-stage Radial Basis Function （RBF） neural network model is proposed to realize the failure detection and fault localization. The first-stage RBF neural network is adopted as a failure observer to realize the failure detection. The trained RBF observer, working concurrently with the actual system, accepts the input voltage signal to the servo valve and the measurements of the ram displacements, rebuilds the system states, and estimates accurately the output of the system. By comparing the estimated outputs with the actual measurements, the residual signal is generated and then analyzed to report the occurrence of faults. The second-stage RBF neural network can locate the fault occurring through the residual and net parameters of the first-stage RBF observer. Considering the slow convergence speed of the k-meansclusteringalgorithm, an improved k-means clustering algorithm and a self-adaptive adjustment algorithm of learning rate arc presented, which obtain the optimum learning rate by adjusting self-adaptive factor to guarantee the stability of the process and to quicken the convergence. The experimental results demonstrate that the two-stage RBF neural network model is effective in detecting and localizing the failure of the hydraulic position servo system.
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