JP2019529917A - 拡張現実の分光法 - Google Patents
拡張現実の分光法 Download PDFInfo
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- JP2019529917A JP2019529917A JP2019515527A JP2019515527A JP2019529917A JP 2019529917 A JP2019529917 A JP 2019529917A JP 2019515527 A JP2019515527 A JP 2019515527A JP 2019515527 A JP2019515527 A JP 2019515527A JP 2019529917 A JP2019529917 A JP 2019529917A
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Abstract
Description
本出願は、2016年9月22日に出願された米国仮出願第62/398,454号の優先権の利益を主張する。上記文献は、参照することによって本明細書に組み込まれる。
本発明は、参照することによって、以下の米国特許出願:米国特許出願第15/072,341号、米国特許出願第14/690,401号、米国特許出願第14/555,858号、米国出願第14/555,585号、米国特許出願第13/663,466号、米国特許出願第13/684,489号、米国特許出願第14/205,126号、米国特許出願第14/641,376号、米国特許出願第14/212,961号、米国仮特許出願第62/298,993号(米国特許出願第15/425,837号に対応する)、および米国特許出願第15/425,837号の各々の全体を組み込む。
(発明の分野)
本開示は、ウェアラブル構成部分を使用した拡張現実のためのシステムおよび方法に関し、より具体的には、反射光の性質によって材料を識別するための拡張現実システムの構成に関する。
現代のコンピューティングおよびディスプレイ技術は、いわゆる「仮想現実」または「拡張現実」体験のためのシステムの開発を促進しており、デジタル的に再現された画像またはその一部が、現実であるように見える、もしくはそのように知覚され得る様式でユーザに提示される。仮想現実、すなわち、「VR」シナリオは、典型的には、他の実際の実世界の視覚的入力に対する透過性を伴わずに、デジタルまたは仮想画像情報の提示を伴う。拡張現実または「AR」シナリオは、典型的には、依然として、ユーザが実世界を実質的に知覚および視認することを可能にしながら、ユーザの周囲の実際の世界の可視化に対する拡張としてのデジタルまたは仮想画像情報の提示を伴う。
複合現実システムは、分光を実施するように構成される。複合現実(代替として、「MR」として省略される)は、典型的には、自然世界の中に統合され、それに応答する、仮想オブジェクトを伴う。例えば、MRシナリオでは、ARコンテンツは、実世界オブジェクトによってオクルードされ、および/または実世界内の他の(仮想または実)オブジェクトと相互作用するように知覚され得る。本開示全体を通して、AR、VR、またはMRの言及は、本発明に関する限定ではなく、本技法は、任意のコンテキストに適用されてもよい。
いくつかのARおよびVRシステムは、コントローラまたはマイクロコントローラ等の処理能力と、また、種々の構成の機能に給電するための電源とを備え、ARおよびVRシステム等のウェアラブルコンピューティングシステム内のコンポーネントの少なくともいくつかが、それらを動作させるユーザの身体に近接しているという事実から、これらのシステムコンポーネントのうちのいくつかを利用して、ユーザに関連するある生理学的監視タスクを行う機会がある。例えば、生理学的監視が、吸光を測定することによって行われてもよい。
OD脈管=−log10(Iv/It)
SO2=ODR=OD第1の波長/OD第2の波長
コントローラ(844)を利用して、計算を実施し、および/または判定を行うことは、コントローラ(844)内のプロセッサ上でローカルで計算を実施することを伴い得ることを理解されたい。いくつかの他の実施形態では、コントローラ(844)を用いて、計算を実施し、および/または判定を行うことは、コントローラを利用して、外部コンピューティングリソース、例えば、サーバ(110)等のクラウド(46)内のリソースとインターフェースをとることを伴ってもよい。
上記に議論されるように、分光システムは、ユーザを囲繞する環境内のオブジェクトまたはオブジェクトの特徴(例えば、性質)を検出するように構成されてもよい。いくつかの実施形態では、環境内に存在するオブジェクトまたはオブジェクトの性質は、コンピュータビジョン技法を使用して検出されてもよい。例えば、本明細書に開示されるように、分光システムの前向きに面したカメラは、オブジェクトを結像するように構成されてもよく、本システムは、画像分析を画像上で実施し、オブジェクト上の特徴の存在を判定するように構成されてもよい。本システムは、外向きに面した結像システムによって入手された画像、吸収判定、および/または反射ならびに/もしくは散乱された光の測定を分析し、オブジェクト認識、オブジェクト姿勢推定、学習、インデックス化、運動推定、または画像復元等を行ってもよい。1つ以上のコンピュータビジョンアルゴリズムが、必要に応じて選択され、これらのタスクを実施するために使用されてもよい。コンピュータビジョンアルゴリズムの非限定的実施例は、スケール不変特徴変換(SIFT)、スピードアップロバスト特徴(SURF)、配向FASTおよび回転BRIEF(ORB)、バイナリロバスト不変スケーラブルキー点(BRISK)、高速網膜キー点(FREAK)、Viola−Jonesアルゴリズム、Eigenfacesアプローチ、Lucas−Kanadeアルゴリズム、Horn−Schunkアルゴリズム、Mean−shiftアルゴリズム、視覚的同時位置推定およびマッピング(vSLAM)技法、シーケンシャルベイズ推定器(例えば、カルマンフィルタ、拡張カルマンフィルタ等)、バンドル調節、適応閾値化(および他の閾値化技法)、反復最近傍点(ICP)、セミグローバルマッチング(SGM)、セミグローバルブロックマッチング(SGBM)、特徴点ヒストグラム、種々の機械学習アルゴリズム(例えば、サポートベクトルマシン、k最近傍アルゴリズム、単純ベイズ、ニューラルネットワーク(畳み込みまたは深層ニューラルネットワークを含む)、または他の教師あり/教師なしモデル等)等を含む。
種々の機械学習アルゴリズムが、オブジェクトまたはオブジェクトの特徴の存在を識別するように学習するために使用されてもよい。いったん訓練されると、機械学習アルゴリズムは、分光システムによって記憶されてもよい。機械学習アルゴリズムのいくつかの実施例は、教師ありまたは教師なし機械学習アルゴリズムを含むことができ、回帰アルゴリズム(例えば、通常の最小2乗回帰等)、インスタンスベースのアルゴリズム(例えば、学習ベクトル量子化等)、決定ツリーアルゴリズム(例えば、分類および回帰ツリー等)、ベイズアルゴリズム(例えば、単純ベイズ等)、クラスタリングアルゴリズム(例えば、k−平均クラスタリング等)、関連付けルール学習アルゴリズム(例えば、アプリオリアルゴリズム等)、人工ニューラルネットワークアルゴリズム(例えば、Perceptron等)、深層学習アルゴリズム(例えば、Deep Boltzmann Machine、すなわち、深層ニューラルネットワーク等)、次元削減アルゴリズム(例えば、主成分分析等)、アンサンブルアルゴリズム(例えば、Stacked Generalization等)、および/または他の機械学習アルゴリズムを含む。いくつかの実施形態では、個々のモデルは、個々のデータセットのためにカスタマイズされることができる。例えば、ウェアラブルデバイスは、ベースモデルを生成または記憶することができる。ベースモデルは、開始点として使用され、データタイプ(例えば、特定のユーザ)、データセット(例えば、1つ以上の波長において取得された吸収率、光反射率、および/または光散乱値のセット)、条件付き状況、または他の変形例に特有の付加的モデルを生成してもよい。いくつかの実施形態では、分光システムは、複数の技法を利用して、集約されたデータの分析のためのモデルを生成するように構成されることができる。他の技法は、事前に定義された閾値またはデータ値を使用することを含んでもよい。
Claims (17)
- ウェアラブル分光システムであって、
ユーザの頭部に取り外し可能に結合可能な頭部搭載型ディスプレイシステムと、
前記ユーザの注視を検出するように構成された少なくとも1つの眼追跡カメラと、
前記頭部搭載型ディスプレイシステムに結合され、前記検出された注視と実質的に同一方向に照射される視野内に少なくとも2つの異なる波長を伴う光を放出するように構成されている、1つ以上の光源と、
頭部搭載型部材に結合され、前記照射される視野内の標的オブジェクトから反射された光を受光するように構成されている、1つ以上の電磁放射検出器と、
前記1つ以上の光源および前記1つ以上の電磁放射検出器に動作可能に結合されたコントローラであって、前記コントローラは、前記1つ以上の光源に、光のパルスを放出させながら、また、前記1つ以上の電磁放射検出器に、前記放出された光のパルスおよび前記標的オブジェクトから反射された光に関連する吸光のレベルを検出させるように構成されている、コントローラと、
少なくとも1つの材料の吸光性質の吸収データベースと、
出力を前記ユーザに表示するためのグラフィックプロセッサユニットと
を備える、システム。 - 前記1つ以上の光源は、複数の発光ダイオードを備える、請求項1に記載のシステム。
- 前記1つ以上の光源は、2つ以上の所定の波長において電磁放射を放出するように構成されている、請求項1に記載のシステム。
- 前記1つ以上の光源は、約660ナノメートルの第1の波長と、約940ナノメートルの第2の波長とにおいて、電磁放射を放出するように構成されている、請求項3に記載のシステム。
- 前記1つ以上の光源は、前記2つの所定の波長において連続して電磁放射を放出するように構成されている、請求項3に記載のシステム。
- 前記1つ以上の光源は、前記2つの所定の波長において同時に電磁放射を放出するように構成されている、請求項3に記載のシステム。
- 前記コントローラはさらに、前記1つ以上の電磁放射検出器が第1および第2の波長を別個に検出するように、前記1つ以上の光源に、前記第1の波長をオンにし、次いで、前記第2の波長をオンにし、次いで、第1および第2の波長の両方をオフにする、巡回パターンを放出させるように構成されている、請求項1に記載のシステム。
- 前記コントローラは、第1の波長光測定と第2の波長光測定の比率を計算するように構成され、前記システムは、前記吸収データベースに基づいて、前記比率を組織の性質に変換するように構成される、請求項1に記載のシステム。
- 前記コントローラは、前記頭部搭載型部材に結合され、前記ユーザによって視認可能である、光学要素に動作可能に結合され、前記システムは、前記組織の性質に基づいて、出力を提供するように構成され、前記出力は、前記光学要素を通して、前記ユーザによって視認可能である、請求項8に記載のシステム。
- 前記1つ以上の電磁放射検出器は、フォトダイオード、光検出器から成る群から選択される、デバイスを備える、請求項1に記載のシステム。
- 前記1つ以上の電磁放射検出器は、デジタル画像センサを備える、請求項1に記載のシステム。
- 前記デジタル画像センサは、複数のピクセルを備え、前記コントローラは、所定の組織の性質に遭遇した後に反射された前記光を受光する、ピクセルのサブセットを自動的に検出し、前記所定の組織の性質を示す前記ピクセルのサブセットの場所を表示する出力を生産するように構成されている、請求項11に記載のシステム。
- 前記頭部搭載型部材はさらに、慣性測定ユニット測位システムを備える、請求項1に記載のシステム。
- 前記慣性測定システムは、前記ユーザの頭部の姿勢配向を判定する、請求項13に記載のシステム。
- 前記照射される視野は、少なくとも、前記姿勢配向と同程度の広さである、請求項14に記載のシステム。
- 前記頭部搭載型ディスプレイシステムは、選択的に可変レベルの波面発散を伴う光を出力するように構成されている、導波管スタックを備える、請求項1に記載のシステム。
- 前記導波管スタックは、屈折力を有する導波管を備える、請求項16に記載のシステム。
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