부산대학교 도서관

Based on an analysis of the information processing mechanism in the primary visual cortex of biological vision, this study proposes an integration method of bar-combination of shifted filter responses (B-COSFIRE) filter with the differential evolution (DE) algorithm for enhancing the precision of events extraction. First, the B-COSFIRE filter incorporates trainable and unsupervised features, utilizing the two-dimensional expression of the difference-of-Gaussians (DoG) model to simulate the receptive field model. By capitalizing on the blur and shift properties of the DoG response, the proposed approach enhances the continuous effective signal while attenuating discontinuous noise signal, thereby demonstrating superior noise robustness compared to conventional methods. Second, the selectivity of proposed filter is not predefined during the implementation but automatically determined based on the given prototype pattern during the configuration process, resulting in a universal solution adaptable to various target patterns. Lastly, we employ the DE algorithm to optimize the feature selection process, enabling the extraction of a minimum feature subset that maximizes the performance of events characterization. The B-COSFIRE method is widely used in the field of image processing. When applying it to seismic exploration, the seismic data used by this algorithm is in ‘sgy’ format, providing richer information than traditional image data. The proposed model can effectively detect the event in seismic data with significant data volume and substantial noise interference. The B-COSFIRE filter method outperforms conventional edge detection techniques by accurately capturing seismic events of varying widths, aligning with the principles observed in biological vision mechanisms. The extracted events exhibit enhanced continuity and accuracy compared to existing approaches.