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Transactions of the Institute of Systems, Control and Information Engineers Vol. 34 (2021), No. 12

ISIJ International
belloff
ONLINE ISSN: 2185-811X
PRINT ISSN: 1342-5668
Publisher: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

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  1. Vol. 37 (2024)

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  2. Vol. 36 (2023)

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  3. Vol. 35 (2022)

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  4. Vol. 34 (2021)

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  5. Vol. 33 (2020)

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  6. Vol. 32 (2019)

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  7. Vol. 31 (2018)

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  12. Vol. 26 (2013)

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  14. Vol. 24 (2011)

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  15. Vol. 23 (2010)

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  17. Vol. 21 (2008)

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  18. Vol. 20 (2007)

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  19. Vol. 19 (2006)

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  20. Vol. 18 (2005)

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  22. Vol. 16 (2003)

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  24. Vol. 14 (2001)

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  25. Vol. 13 (2000)

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  26. Vol. 12 (1999)

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  27. Vol. 11 (1998)

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  28. Vol. 10 (1997)

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  29. Vol. 9 (1996)

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  30. Vol. 8 (1995)

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  31. Vol. 7 (1994)

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  32. Vol. 6 (1993)

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  33. Vol. 5 (1992)

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  34. Vol. 4 (1991)

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  35. Vol. 3 (1990)

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  36. Vol. 2 (1989)

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  37. Vol. 1 (1988)

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Transactions of the Institute of Systems, Control and Information Engineers Vol. 34 (2021), No. 12

Food Region Extraction Based on Saliency Detection Model

Ayako Kitada, Takuya Futagami, Noboru Hayasaka

pp. 311-318

DOI:

10.5687/iscie.34.311

Abstract

In this paper, we propose a method that can automatically extract food regions from food images by using the saliency detection model based on a deep neural network (DNN) and the saliency thresholding method based on the average saliency value. Our experiment, using 125 food images from a food recording tool on smartphone applications, demonstrates that the proposed method significantly increased average F-measure by 4.22% or more compared with both the conventional method using local extrema and food extraction using DNN trained with 1017 food images. Our proposed method also increased average precision and recall by 0.13% or more and 11.38% or more, respectively. We also discussed the effectiveness and the future development of food extraction using the saliency detection model and saliency thresholding method on the basis of experimental results.

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Food Region Extraction Based on Saliency Detection Model

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Obstacle Recognition with Single Camera for Unmanned Small Vessel Using Semantic Segmentation

Kota Mukai, Naoyuki Hara, Keiji Konishi

pp. 319-321

DOI:

10.5687/iscie.34.319
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Obstacle Recognition with Single Camera for Unmanned Small Vessel Using Semantic Segmentation

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Real-time Data-driven Reference Shaping based on Online Data-driven Tunings

Motoya Suzuki, Osamu Kaneko

pp. 322-324

DOI:

10.5687/iscie.34.322
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Real-time Data-driven Reference Shaping based on Online Data-driven Tunings

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