The Experts below are selected from a list of 45420 Experts worldwide ranked by ideXlab platform
Yoshikazu Nishikawa - One of the best experts on this subject based on the ideXlab platform.
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Rotation-invariant neural pattern recognition system estimating a Rotation Angle
IEEE Transactions on Neural Networks, 1997Co-Authors: Minoru Fukumi, Sigeru Omatu, Yoshikazu NishikawaAbstract:A Rotation-invariant neural pattern recognition system, which can recognize a rotated pattern and estimate its Rotation Angle, is considered. It is well-known that humans sometimes recognize a rotated form by means of mental Rotation. The occurrence of mental Rotation can be explained in terms of the theory of information types. Therefore, we first examine the applicability of the theory to a Rotation-invariant neural pattern recognition system. Next, we present a Rotation-invariant neural network which can estimate a Rotation Angle. The neural network consists of a preprocessing network to detect the edge features of input patterns and a trainable multilayered network. Furthermore, a Rotation-invariant neural pattern recognition system which includes the Rotation-invariant neural network is proposed. This system is constructed on the basis of the above-mentioned theory. Finally, it is shown that, by means of computer simulations of a binary pattern and a coin recognition problem, the system is able to recognize rotated patterns and estimate their Rotation Angle.
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Rotation invariant neural pattern recognition system which can estimate a Rotation Angle
Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 1994Co-Authors: Minoru Fukumi, Sigeru Omatu, Yoshikazu NishikawaAbstract:This paper presents a Rotation invariant neural pattern recognition system, which can recognize a rotated pattern and estimate a Rotation Angle. The system is very effective for a rotated coin recognition problem, but is poor compared with human performance. It is well known that human sometimes recognizes a rotated pattern by means of the mental Rotation. Such a fact, however, has never been considered and used in neural pattern recognition systems, especially in Rotation invariant systems. Therefore, we examine the principle of mental Rotation and apply it to a Rotation invariant pattern recognition system. The system with such a principle could recognize a rotated pattern and estimate a Rotation Angle. It is shown that the system is effective to recognize a rotated pattern from results of computer simulation for a coin recognition problem. >
Minoru Fukumi - One of the best experts on this subject based on the ideXlab platform.
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Rotation-invariant neural pattern recognition system estimating a Rotation Angle
IEEE Transactions on Neural Networks, 1997Co-Authors: Minoru Fukumi, Sigeru Omatu, Yoshikazu NishikawaAbstract:A Rotation-invariant neural pattern recognition system, which can recognize a rotated pattern and estimate its Rotation Angle, is considered. It is well-known that humans sometimes recognize a rotated form by means of mental Rotation. The occurrence of mental Rotation can be explained in terms of the theory of information types. Therefore, we first examine the applicability of the theory to a Rotation-invariant neural pattern recognition system. Next, we present a Rotation-invariant neural network which can estimate a Rotation Angle. The neural network consists of a preprocessing network to detect the edge features of input patterns and a trainable multilayered network. Furthermore, a Rotation-invariant neural pattern recognition system which includes the Rotation-invariant neural network is proposed. This system is constructed on the basis of the above-mentioned theory. Finally, it is shown that, by means of computer simulations of a binary pattern and a coin recognition problem, the system is able to recognize rotated patterns and estimate their Rotation Angle.
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Rotation invariant neural pattern recognition system which can estimate a Rotation Angle
Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 1994Co-Authors: Minoru Fukumi, Sigeru Omatu, Yoshikazu NishikawaAbstract:This paper presents a Rotation invariant neural pattern recognition system, which can recognize a rotated pattern and estimate a Rotation Angle. The system is very effective for a rotated coin recognition problem, but is poor compared with human performance. It is well known that human sometimes recognizes a rotated pattern by means of the mental Rotation. Such a fact, however, has never been considered and used in neural pattern recognition systems, especially in Rotation invariant systems. Therefore, we examine the principle of mental Rotation and apply it to a Rotation invariant pattern recognition system. The system with such a principle could recognize a rotated pattern and estimate a Rotation Angle. It is shown that the system is effective to recognize a rotated pattern from results of computer simulation for a coin recognition problem. >
Sigeru Omatu - One of the best experts on this subject based on the ideXlab platform.
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Rotation-invariant neural pattern recognition system estimating a Rotation Angle
IEEE Transactions on Neural Networks, 1997Co-Authors: Minoru Fukumi, Sigeru Omatu, Yoshikazu NishikawaAbstract:A Rotation-invariant neural pattern recognition system, which can recognize a rotated pattern and estimate its Rotation Angle, is considered. It is well-known that humans sometimes recognize a rotated form by means of mental Rotation. The occurrence of mental Rotation can be explained in terms of the theory of information types. Therefore, we first examine the applicability of the theory to a Rotation-invariant neural pattern recognition system. Next, we present a Rotation-invariant neural network which can estimate a Rotation Angle. The neural network consists of a preprocessing network to detect the edge features of input patterns and a trainable multilayered network. Furthermore, a Rotation-invariant neural pattern recognition system which includes the Rotation-invariant neural network is proposed. This system is constructed on the basis of the above-mentioned theory. Finally, it is shown that, by means of computer simulations of a binary pattern and a coin recognition problem, the system is able to recognize rotated patterns and estimate their Rotation Angle.
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Rotation invariant neural pattern recognition system which can estimate a Rotation Angle
Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 1994Co-Authors: Minoru Fukumi, Sigeru Omatu, Yoshikazu NishikawaAbstract:This paper presents a Rotation invariant neural pattern recognition system, which can recognize a rotated pattern and estimate a Rotation Angle. The system is very effective for a rotated coin recognition problem, but is poor compared with human performance. It is well known that human sometimes recognizes a rotated pattern by means of the mental Rotation. Such a fact, however, has never been considered and used in neural pattern recognition systems, especially in Rotation invariant systems. Therefore, we examine the principle of mental Rotation and apply it to a Rotation invariant pattern recognition system. The system with such a principle could recognize a rotated pattern and estimate a Rotation Angle. It is shown that the system is effective to recognize a rotated pattern from results of computer simulation for a coin recognition problem. >
Raheleh Sadat Mirfasihi - One of the best experts on this subject based on the ideXlab platform.
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Is there any association between aortic root Rotation Angle and aortic dissection
Indian journal of thoracic and cardiovascular surgery, 2019Co-Authors: Maryam Moradi, Raheleh Sadat MirfasihiAbstract:Thoracic aortic dissection is a probable fatal condition that requires early diagnosis and management. The underlying etiology of this disorder is an important issue that has not been completely responded yet. In the current study, the association between aortic root Rotation and ascending aortic dissection has been assessed. This is a non-randomized retrospective case-control study conducted on twenty-five cases referring with ascending aortic dissection and seventy-five controls that underwent computed tomography (CT) angiography for reasons other than aortic dissection. Aortic root Rotation Angle and aortic diameter for both cases and controls were measured and then compared. There was no significant difference regarding age and gender distribution (P value = 0.22 and 0.38 respectively) between patients in case and control groups. The mean values of aortic root Rotation Angle and aortic diameter in cases were 22.5 ± 10.5° and 43.1 ± 12.5 mm versus 15.7 ± 10.7° and 30.7 ± 5.3 mm in controls (P value = 0.007 and 0.001 respectively). Direct relation was found between aortic root Rotation Angle and aortic diameter (P value = 0.007, r = 0.276). Mean of aortic root Rotation Angle was significantly higher in females (P value = 0.02). No association between cases’ age with either aortic root Rotation Angle or aortic diameter was found (P value = 0.33, r = 0.098, and P value = 0.085, r = 0.173 respectively). Based on the findings of the current study, aortic root Rotation Angle was independently in direct association with thoracic aortic dissection. In addition, females had higher aortic root Rotation Angles.
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Is there any association between aortic root Rotation Angle and aortic dissection?
Indian Journal of Thoracic and Cardiovascular Surgery, 2019Co-Authors: Maryam Moradi, Raheleh Sadat MirfasihiAbstract:Introduction Thoracic aortic dissection is a probable fatal condition that requires early diagnosis and management. The underlying etiology of this disorder is an important issue that has not been completely responded yet. In the current study, the association between aortic root Rotation and ascending aortic dissection has been assessed. Methods This is a non-randomized retrospective case-control study conducted on twenty-five cases referring with ascending aortic dissection and seventy-five controls that underwent computed tomography (CT) angiography for reasons other than aortic dissection. Aortic root Rotation Angle and aortic diameter for both cases and controls were measured and then compared. Results There was no significant difference regarding age and gender distribution ( P value = 0.22 and 0.38 respectively) between patients in case and control groups. The mean values of aortic root Rotation Angle and aortic diameter in cases were 22.5 ± 10.5° and 43.1 ± 12.5 mm versus 15.7 ± 10.7° and 30.7 ± 5.3 mm in controls ( P value = 0.007 and 0.001 respectively). Direct relation was found between aortic root Rotation Angle and aortic diameter ( P value = 0.007, r = 0.276). Mean of aortic root Rotation Angle was significantly higher in females ( P value = 0.02). No association between cases’ age with either aortic root Rotation Angle or aortic diameter was found ( P value = 0.33, r = 0.098, and P value = 0.085, r = 0.173 respectively). Conclusion Based on the findings of the current study, aortic root Rotation Angle was independently in direct association with thoracic aortic dissection. In addition, females had higher aortic root Rotation Angles.
Youngsung Kim - One of the best experts on this subject based on the ideXlab platform.
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Robust Rotation Angle estimator
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999Co-Authors: Whoi-yul Kim, Youngsung KimAbstract:The conventional method of estimating the Rotation Angle of a pattern using the principal axes is not suitable for circular symmetric patterns since their eigenvalues are similar in both directions. In the paper, a robust method of estimating a Rotation Angle using the phase information of Zernike moments is presented. The experimental results show that the proposed method estimates the Rotation Angle of the circular symmetric patterns more accurately than the principal axes method, even in the presence of noise.
