JP2022539281A - 検出対象物質のスペクトル情報を抽出する方法 - Google Patents
検出対象物質のスペクトル情報を抽出する方法 Download PDFInfo
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Abstract
Description
取得されたハイパースペクトル画像から検出対象物体の占有する画素領域A(x,y)を選択するステップS1と、
前記画素領域A(x,y)から前記鏡面反射領域Aq及び前記乱反射領域Arを抽出して、それぞれ鏡面反射領域Aqの代表的なスペクトルIq(ω)及び乱反射領域Arの代表的なスペクトルIr(ω)を求めるステップS2と、
鏡面反射領域Aqの代表的なスペクトルIq(ω)における各要素と乱反射領域Arの代表的なスペクトルIr(ω)における各要素を比較することにより、光源情報と物質スペクトル情報を分離して、第1スペクトル不変量C(ω)を取得するステップS3と、を含む。
第2領域選択方法で前記画素領域A(x,y)から前記鏡面反射領域Aq及び前記乱反射領域Arを抽出するS21と、
鏡面反射領域Aqに基づいて代表的なスペクトルIq(ω)を取得し、乱反射領域Arに基づいて代表的なスペクトルIr(ω)を取得するS22と、を含む。
第1スペクトル不変量C(ω)に対して標準正規変換を行って、第2スペクトル不変量R(ω)を取得し、
第2スペクトル不変量R(ω)を入力として化学計測学モデルに入力して、物質スペクトル分析を行うS42と、を含む。
取得されたハイパースペクトル画像から検出対象物体の占有する画素領域A(x,y)を選択するステップS1と、
前記画素領域A(x,y)から前記鏡面反射領域Aq及び前記乱反射領域Arを抽出して、それぞれ鏡面反射領域Aqの代表的なスペクトルIq(ω)及び乱反射領域Arの代表的なスペクトルIr(ω)を求めるステップS2と、
鏡面反射領域Aqの代表的なスペクトルIq(ω)における各要素と乱反射領域Arの代表的なスペクトルIr(ω)における各要素を比較することにより、光源情報と物質スペクトル情報を分離して、第1スペクトル不変量C(ω)を取得するステップS3と、を含む。
第2領域選択方法で画素領域A(x,y)から鏡面反射領域Aq及び乱反射領域Arを抽出するS21と、
鏡面反射領域Aqに基づいて代表的なスペクトルIq(ω)を取得し、乱反射領域Arに基づいて代表的なスペクトルIr(ω)を取得するS22と、を含む。
第1スペクトル不変量C(ω)に対して線形変換処理を行って、スペクトル分析のための第2スペクトル不変量R(ω)を取得するステップS4を更に含む。
第1スペクトル不変量C(ω)に対して標準正規変換を行って、第2スペクトル不変量R(ω)を取得し、
第2スペクトル不変量R(ω)を入力として化学計測学モデルに入力して物質スペクトル分析を行うS42と、を含む。
Claims (13)
- 検出対象物質のスペクトル情報を抽出する方法であって、
取得されたハイパースペクトル画像から検出対象物体の占有する画素領域A(x,y)を選択するステップS1と、
前記画素領域A(x,y)からそれぞれ鏡面反射領域Aq及び乱反射領域Arを抽出して、それぞれ前記鏡面反射領域Aqの代表的なスペクトルIq(ω)及び前記乱反射領域Arの代表的なスペクトルIr(ω)を求めるステップS2と、
前記鏡面反射領域Aqの代表的なスペクトルIq(ω)における各要素と前記乱反射領域Arの代表的なスペクトルIr(ω)における各要素を比較することにより、光源情報と物質スペクトル情報を分離して、第1スペクトル不変量C(ω)を取得するステップS3と、を含むことを特徴とする検出対象物質のスペクトル情報を抽出する方法。 - 前記第1スペクトル不変量C(ω)に対して線形変換処理を行って、スペクトル分析のための第2スペクトル不変量R(ω)を取得するステップS4を更に含むことを特徴とする請求項1に記載の検出対象物質のスペクトル情報を抽出する方法。
- 前記ステップS1において、第1領域選択方法で前記検出対象物体を識別して前記画素領域A(x,y)を選択し、前記第1領域選択方法は手動注釈、マシンビジョン、スペクトル角度マッピング又は深層学習アルゴリズムを含むことを特徴とする請求項1に記載の検出対象物質のスペクトル情報を抽出する方法。
- 前記ステップS2は、
第2領域選択方法で前記画素領域A(x,y)から前記鏡面反射領域Aq及び前記乱反射領域Arを抽出するS21と、
前記鏡面反射領域Aqに基づいて代表的なスペクトルIq(ω)を取得し、前記乱反射領域Arに基づいて代表的なスペクトルIr(ω)を取得するS22と、を含むことを特徴とする請求項1に記載の検出対象物質のスペクトル情報を抽出する方法。 - 前記第2領域選択方法は主成分分析、K平均値、行列直交投影又は幾何形状に基づく領域選択を含むことを特徴とする請求項4に記載の検出対象物質のスペクトル情報を抽出する方法。
- 前記鏡面反射領域Aqの代表的なスペクトルIq(ω)及び前記乱反射領域Arの代表的なスペクトルIr(ω)の求め方法は平均スペクトル、輝度重み付け平均スペクトル又はグレーワールドアルゴリズムを含むことを特徴とする請求項4に記載の検出対象物質のスペクトル情報を抽出する方法。
- 前記ステップS3において、前記第1スペクトル不変量C(ω)の求め方法は有限要素分解、スペクトル角度分離又は除算を含むことを特徴とする請求項1に記載の検出対象物質のスペクトル情報を抽出する方法。
- 前記鏡面反射領域Aqの代表的なスペクトルIq(ω)における各要素をそれぞれ前記乱反射領域Arの代表的なスペクトルIr(ω)における各要素で割って、第1スペクトル不変量C(ω)を取得し、C(ω=Iq(ω)/Ir(ω)であることを特徴とする請求項8に記載の検出対象物質のスペクトル情報を抽出する方法。
- 前記化学計測学モデルは部分的最小二乗分析、人工ニューラルネットワーク又はサポートベクターマシンを含むことを特徴とする請求項10に記載の検出対象物質のスペクトル情報を抽出する方法。
- 前記ハイパースペクトル画像は撮影時に各波長域において前記検出対象物体の占有する画素領域A(x,y)が変化しないように維持し、前記検出対象物体は前記ハイパースペクトル画像において一定の比率を占有することを特徴とする請求項1~11のいずれか1項に記載の検出対象物質のスペクトル情報を抽出する方法。
- スペクトルカメラであって、
レンズ、分光器、イメージング装置及びデータ記憶処理装置を備え、光源から発した光線は検出対象物体の表面に反射され、前記レンズ及び前記分光器を通過して前記イメージング装置に到達し、前記データ記憶処理装置により異なる波長における電気信号及びデジタル信号に変換され、前記デジタル信号はスペクトル画像データであり、前記スペクトル画像データは光源スペクトル情報及び前記検出対象物体の表面物質のスペクトル情報を含み、請求項1~12のいずれか1項に記載の検出対象物質のスペクトル情報を抽出する方法で前記スペクトル画像データを処理して、前記検出対象物体の物質特性を取得することを特徴とするスペクトルカメラ。
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WO2021195817A1 (zh) | 2021-10-07 |
US20220207856A1 (en) | 2022-06-30 |
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CN113272639A (zh) | 2021-08-17 |
EP3940370A4 (en) | 2022-11-30 |
KR20220066168A (ko) | 2022-05-23 |
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