The normalized central moments are widely used in pattern recognition because of scale and translation invariance. The moduli of normalized central moments of the 1-dimensional complex range profiles are used here as ...The normalized central moments are widely used in pattern recognition because of scale and translation invariance. The moduli of normalized central moments of the 1-dimensional complex range profiles are used here as feature vector for radar target recognition. The common feature extraction method for high resolution range profile obtained by using Fourier-modified direct Mellin transform is inefficient and unsatisfactory in recognition rate And. generally speaking, the automatic target recognition method based on inverse synthetic aperture radar 2-dimensional imaging is not competent for real time object identification task because it needs complicated motion compensation which is sometimes too difficult to carry out. While the method applied here is competent for real-time recognition because of its computational efficiency. The result of processing experimental data indicates that this method is good at recognition.展开更多
The physical properties of pattern characteristics for typical Acoustic Sea-bed Profiling Records (ASPRs) in the area of Changjiang Estuary and the East China Sea are analyzed in this paper. Nine pattern characterist...The physical properties of pattern characteristics for typical Acoustic Sea-bed Profiling Records (ASPRs) in the area of Changjiang Estuary and the East China Sea are analyzed in this paper. Nine pattern characteristics are summarized and it was shown that 9 geological categories can be determined by 4 pattern characteristics. Based on the above analysis, a Bayes-based pattern characteristics classifier for interpretation of ASPRs is developed.展开更多
An expert system based on the fuzzy set theory has been developed for geological interpretation of Acoustic Seabed Profiling Records(ASPR). After successively extracting each state of several main pattern characterist...An expert system based on the fuzzy set theory has been developed for geological interpretation of Acoustic Seabed Profiling Records(ASPR). After successively extracting each state of several main pattern characteristics shown on the ASPRs, the similarities between this pattern characteristic-state set and the standard ones corresponding to different geological categories of marine sediments are computed respectively By comparillg these values of sidrilarities, the conclusion of geological classification to the ASPR can be derived.展开更多
A computer-based pattern recognition systems has been developed for geological interpretation of Acoustic Sea-bed Profiling Records. Based on practical experience accumu- lated by specialists, the main pattern charact...A computer-based pattern recognition systems has been developed for geological interpretation of Acoustic Sea-bed Profiling Records. Based on practical experience accumu- lated by specialists, the main pattern characteristics of Acoustic Sea-bed Profiling Records (ASPRs) corresponding to typical geological categories of marine sediment layers in the area of the East China Sea have been expressed altogether in 9 aspects, and a dynamic reasoning expert system designed correspondingly. Starting from an initial premise Characteristic and makes the next step reasoning until the final conclusion (i.e. which geological category the sediment layer belongs to.) is derived, in the mean time, for quantitatively estimating the correctness of the final conclusions, the so-called certainty factor is calculated.展开更多
文摘The normalized central moments are widely used in pattern recognition because of scale and translation invariance. The moduli of normalized central moments of the 1-dimensional complex range profiles are used here as feature vector for radar target recognition. The common feature extraction method for high resolution range profile obtained by using Fourier-modified direct Mellin transform is inefficient and unsatisfactory in recognition rate And. generally speaking, the automatic target recognition method based on inverse synthetic aperture radar 2-dimensional imaging is not competent for real time object identification task because it needs complicated motion compensation which is sometimes too difficult to carry out. While the method applied here is competent for real-time recognition because of its computational efficiency. The result of processing experimental data indicates that this method is good at recognition.
基金The work was supported by the National 863 plan Youth Foundation (820-Q-09).
文摘The physical properties of pattern characteristics for typical Acoustic Sea-bed Profiling Records (ASPRs) in the area of Changjiang Estuary and the East China Sea are analyzed in this paper. Nine pattern characteristics are summarized and it was shown that 9 geological categories can be determined by 4 pattern characteristics. Based on the above analysis, a Bayes-based pattern characteristics classifier for interpretation of ASPRs is developed.
文摘An expert system based on the fuzzy set theory has been developed for geological interpretation of Acoustic Seabed Profiling Records(ASPR). After successively extracting each state of several main pattern characteristics shown on the ASPRs, the similarities between this pattern characteristic-state set and the standard ones corresponding to different geological categories of marine sediments are computed respectively By comparillg these values of sidrilarities, the conclusion of geological classification to the ASPR can be derived.
基金the National 863 Plan Youth Foundation of China !(820-Q-09).
文摘A computer-based pattern recognition systems has been developed for geological interpretation of Acoustic Sea-bed Profiling Records. Based on practical experience accumu- lated by specialists, the main pattern characteristics of Acoustic Sea-bed Profiling Records (ASPRs) corresponding to typical geological categories of marine sediment layers in the area of the East China Sea have been expressed altogether in 9 aspects, and a dynamic reasoning expert system designed correspondingly. Starting from an initial premise Characteristic and makes the next step reasoning until the final conclusion (i.e. which geological category the sediment layer belongs to.) is derived, in the mean time, for quantitatively estimating the correctness of the final conclusions, the so-called certainty factor is calculated.
