A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
CVGIP: Image Understanding
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Hough transform neural network for seismic pattern detection
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Hough transform network: learning conoidal structures in a connectionist framework
IEEE Transactions on Neural Networks
Simulated annealing for pattern detection and seismic analysis
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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Hough transform neural network is adopted to detect the line pattern of direct wave and the hyperbolic pattern of reflection wave in a one-shot seismogram. We use time difference from point to hyperbola and line as the distance in the pattern detection of seismic direct and reflection waves. This distance calculation makes the parameter learning feasible. One set of parameters represents one pattern. Many sets of parameters represent many patterns. The neural network can calculate the distances from point to many patterns as total error. The parameter learning rule is derived by gradient descent method to minimize the total error. The network is applied to three kinds of data in the experiments. One is the line and hyperbolic pattern in the image data. The second is the simulated one-shot seismic data. And the last is the real one-shot seismic data. Experimental results show that lines and hyperbolas can be detected correctly in three kinds of data. The method can also tolerate certain level of noise data. The detection results in the one-shot seismogram can improve the seismic interpretation and further seismic data processing.