Faculty Profiles - NIIMI Michiharu

Conference Prsentations (Oral, Poster) -

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［ポスター講演］スパースコーディングを用いたAI生成画像の識別

田中大志,新見道治

電子情報通信学会マルチメディア情報ハイディング・エンリッチメント研究会 2024.03 電子情報通信学会

［ポスター講演］射影変換用マーカを必要としない改良型アルティメイトリンク

山寺慧司,新見道治

電子情報通信学会マルチメディア情報ハイディング・エンリッチメント研究会 2024.03 電子情報通信学会

［ポスター講演］英語発音学習のためのアクセント変換技術を応用した学習者の話者性を持つお手本音声の生成

浜川冷,新見道治

電子情報通信学会マルチメディア情報ハイディング・エンリッチメント研究会 2023.11 電子情報通信学会

StyleGAN2を用いたクラスラベルに基づくフォント生成

島村優希,新見道治

電子情報通信学会マルチメディア情報ハイディング・エンリッチメント研究会 2023.09 電子情報通信学会

Validation of aerosol concentration of satellite remote sensing with citizens' camera-driven measurements

Tomohiro Sato, Michiharu Niimi, Hisahiro Takashima, Shoken Ishii, Masanori Takabayashi, Kazunori Yamamoto, Takeshi Murata, Hitoshi Yoshida, Tomoki Nakayama, Mayuko Yoshikawa, Yasuko Kasai

The 14th International GEMS workshop 2023.09

LiDARと物体検出を用いた駅ホームにおける視覚障害者向け危険回避誘導スマホアプリの開発

白井達也,島川学,大隈千春,清田公保,新見道治

日本知能情報ファジィ学会ファジィシステムシンポジウム 2023.09 日本知能情報ファジィ学会

視覚障害者向け危険回避誘導アプリの検証のための混雑度指標

中原太陽,島川学,大隈千春,清田公保,新見道治

日本知能情報ファジィ学会ファジィシステムシンポジウム 2023.09 日本知能情報ファジィ学会

SNAP-CII: Algorithm to estimate aerosol concentration using commonly used camera

Tomohiro Sato,Michiharu Niimi,Hisahiro Takashima,Masanori Takabayashi,Kazunori Yamamoto,Ken T. Murata,Hitoshi Yoshida,Yasuko Kasai

2023.05

Event date： 2023.05.21 - 2023.05.26 Language：English Country：Japan

Aerosols play an important role for the radiative forcing, such as scattering and absorption of sunlight and cloud condensation, and they, especially PM2.5, are also suggested to increase morbidity and mortality. In this study, we develop an algorithm, named SNAP-CII, to estimate aerosol concentration from sky image data. The SNAP-CII algorithm enables us to measure aerosol concentration with a commonly used camera even in places where it is difficult to install specialized equipment such as a sky radiometer.
We installed cameras on the rooftops of Fukuoka University (FU) and Kyushu Institute of Technology (KT) in December 2020. The captured image data were automatically sent via the Internet using the video transmission system developed by the National Institute of Information and Communications Technology and stored at 1 minute intervals. The image data from 9:00 to 18:00 were used for this study.
We used the data of concentration of suspended particle matter (SPM) measured by the Atmospheric Environmental Regional Observation System (AEROS) <https://soramame.env.go.jp/, Last access February 2, 2022>. The data measured at Sohara and Tobata monitoring station were used for analyzing the data taken by FU and KT, respectively. The solar radiation data observed by Himawari 8 satellite were used for selection of weather condition. In this study, we used the data with global solar irradiance (GSI) and diffuse solar irradiance (DSI) to select clear sunny conditions. The criteria were GSI > 550 W/m2 and DSI/GSI < 0.15.
A machine learning model was developed to classify SPM concentration into three classes (Low: 0 – 10, Middle: 10 – 25, High: > 25 [g/m3]) using the sky image data. The reflectance (gray-scale, B, G, R) and pixel value ratio (B/G, G/R, R/B) were calculated to extract the difference in scattering by aerosols depending on wavelength. The reflectance was derived by dividing the luminance, which is the pixel value, by the illuminance. The illuminance was derived by applying a bilateral filter to the average image data from 11:00 – 14:00 on December 8 when the SPM averages were low (Sohara: 8.5 g/m3, Tobata: 6.3 g/m3) and the solar irradiance satisfied the criteria. The average values over eight image ranges (entire area, upper, middle and lower parts of sky, left, center and right of boundary parts of sky and buildings, and buildings area) were calculated.
We tested three types of classification models, K-nearest neighbor (KN), support vector machine (SVM) and random forest (RF). The data were randomly divided into training and test data in a ratio of 6:4. The accuracy, number of correct predictions divided by total number of samples, for each model was calculated. The RF had the highest accuracy of 70%, and the KN and SVM had 65% for the data taken by FU. The KT camera data also showed the highest accuracy by the RF model, and the accuracy was 66%.
In this study, we developed three-class classification model of aerosol concentration based on sky image data in order to measure aerosol concentration more conveniently. The classification algorithm, named SNAP-CII, was developed for clear sunny conditions in Fukuoka and Kitakyushu cities, and the accuracies were 66 – 70%.

複雑な複数漢字⽂字を対象とした印刷耐性を有するアルティメイトリンク

⼭寺慧司,新見道治

火の国シンポジウム2023 2023.03 情報処理学会九州支部

[ポスター講演] 深度画像での床面推定を利用した人流計測精度改善

永利拓也,新見道治

電子情報通信学会マルチメディア情報ハイディング・エンリッチメント研究会 2023.03 電子情報通信学会

[ポスター講演] 服画像ベクトル化により抽出される潜在的興味に基づく衣服推薦システム

田中大志,新見道治

電子情報通信学会マルチメディア情報ハイディング・エンリッチメント研究会 2023.03 電子情報通信学会

SNAP-CII: スマホ等カメラ撮影空画像によるエアロゾル濃度観測

佐藤知紘,新見道治,高島久洋,石井昌憲,高林正典,山本和憲,村田健史,吉田一志,笠井康子

第27回日本大気化学討論会 2022.11

zi2zi に基づくクラスラベル付きフォント生成に関する考察

島村優希,新見道治

電子情報通信学会九州支部　2022年度（第30回）学生会講演会

Estimation of aerosol concentration using image analysis techniques(SNAP-CII)

Tomohiro Sato,Michiharu Niimi,Hisahiro Takashima,Masanori Takabayashi,Kazunori Yamamoto,Ken T. Murata,Hitoshi Yoshida,Yasuko Kasai

2022.05

Event date： 2022.05.22 - 2022.06.03 Language：Japanese Country：Japan

[ポスター講演] デジタルイラスト描画補助システムの提案

樋口叡,新見道治