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      저자 : Takashi Takiguchi (검색결과 3 건)
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      • SCIESCOPUSKCI등재

        GLOBAL UNIQUENESS FOR THE RADON TRANSFORM

        Takiguchi, Takashi Korean Mathematical Society 2020 대한수학회보 Vol.57 No.3

        In this article, we discuss the global uniqueness problem for the Radon transform. It is not sufficient for the global uniqueness for the Radon transform to assume that the Radon transform Rf for a function f absolutely converges on any hyperplane. It is also known that it is sufficient to assume that f ∈ L<sup>1</sup> for the global uniqueness to hold. There exists a big gap between the above two conditions, to fill which is our purpose in this paper. We shall give a better sufficient condition for the global uniqueness of the Radon transform.

      • KCI등재

        Global uniqueness for the Radon transform

        Takashi Takiguchi 대한수학회 2020 대한수학회보 Vol.57 No.3

        In this article, we discuss the global uniqueness problem for the Radon transform. It is not sufficient for the global uniqueness for the Radon transform to assume that the Radon transform $Rf$ for a function $f$ absolutely converges on any hyperplane. It is also known that it is sufficient to assume that $f \in L^1$ for the global uniqueness to hold. There exists a big gap between the above two conditions, to fill which is our purpose in this paper. We shall give a better sufficient condition for the global uniqueness of the Radon transform.

      • Speaker Independent Phoneme Recognition Based on Fisher Weight Map

        Takashi Muroi,Tetsuya Takiguchi,Yasuo Ariki 보안공학연구지원센터 2008 International Journal of Hybrid Information Techno Vol.1 No.3

        We have already proposed a new feature extraction method based on higher-order local auto-correlation and Fisher weight map (FWM) at Interspeech2006. This paper shows effectiveness of the proposed FWM in speaker dependent and speaker independent phoneme recognition. Widely used MFCC features lack temporal dynamics. To solve this problem, local auto-correlation features are computed and accumulated by weighting high scores on the discriminative areas. This score map is called Fisher weight map. From the speaker dependent phoneme recognition, the proposed FWM showed 79.5% recognition rate, by 5.0 points higher than the result by MFCC. Furhermore by combing FWM with MFCC and ¢MFCC, the recognition rate improved to 88.3%. In the speaker independent phoneme recognition, it showed 84.2% recognition rate, by 11.0 points higher than the result by MFCC. By combining FWM with MFCC and ¢MFCC, the reecognition rate improved to 89.0%.

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