JP2007200275A - データ取得及び識別のためのシステム及び方法 - Google Patents
データ取得及び識別のためのシステム及び方法 Download PDFInfo
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Abstract
【解決手段】データベース装置中の複数の対象から対象を特定するために画像の特性を使用する識別方法である。デジタル画像中の実際の対象を識別するために、データは、一つ又は複数のデータベースの対象データとの形状比較、グレースケール比較、ウェブレット比較、及びカラーキューブ比較のようなパラメータに分解される。
【選択図】図1
Description
放射量補正(Radiometric Correction)
セグメンテーション(Segmentation)
セグメントグループの生成(Segment Group Generation)
各セグメントグループについて(For each segment group)
バウンディングボックスの生成(Bounding Box Generation)
幾何正規化(Geometric Normalization)
ウェブレット分解(Wavelet Decomposition)
カラーキューブ分解(Color Cube Decomposition)
形状分解(Shape Decomposition)
低解像度のグレースケール画像の生成(Low-Resolution Grayscale Image Generation)
フォー・エンド(For END)
Iavg=位相平均
Qavg=直角位相平均
Ysigma=強度標準偏差
Isigma=位相標準偏差
Qsigma=直角位相標準偏差
Npixels=セグメント中の画素数
換言すれば、以下の通りである。
各データベースの対象について(For each database object)
この対象の各ビューについて(For each view of this object)
このデータベースの対象の視像の各セグメントグループについて(For each segment group in this view of this database object)
形状比較(Shape Comparison)
グレースケール比較(Grayscale Comparison)
ウェブレット比較(Wavelet Comparison)
カラーキューブ比較(Color Cube Comparison)
複合化したマッチスコアの計算(Calculate Combined Match Score)
エンド・フォー(END FOR)
エンド・フォー(END FOR)
エンド・フォー(END FOR)
エンド・フォー(END FOR)
このループは、「セグメント生成」のステップに格納された順番で、入力画像のセグメントグループの各組み合わせを考慮する。各セグメントグループは、それが考慮される時は、画像中の目的の対象の候補であり、種々のテストを使用してデータベースの対象と比較される。
このループでは、現在の入力セグメントグループと比較するために、データベースの各対象が考慮される。
このループでは、現在の入力セグメントグループと比較するために、現在のデータベースの対象の各視像が考慮される。データベースは、各対象について、異なる視野角からの複数の視像を含む。
このループでは、データベースの対象の現在の視像のセグメントグループの各組み合わせが考慮される。これらのセグメントグループは、入力画像のセグメントグループと同じ方法で生成されている。
入力:
入力される画像及び全てのデータベースの画像について:
I.セグメントグループの輪郭
II.セグメントグループの面積
III.セグメントグループの図心位置
IV.セグメントグループのバウンディングの楕円の楕円率
V.TBDの範囲内で入力されたセグメントグループと面積がほぼ等しいデータベースのセグメントグループを識別し、「マッチ」のそれぞれについて面積マッチングスコアを計算する。
VI.TBDの範囲内かつ、前のステップで識別されたマッチの組内で、入力されたセグメントグループと楕円率がほぼ等しいデータベースのセグメントのグループを識別し、「マッチ」のそれぞれについて楕円率位置マッチングスコアを計算する。
VII.TBDの範囲内かつ、前のステップで識別されたマッチの組内で、入力されたセグメントグループと図心位置がほぼ等しいデータベースのセグメントグループを識別し、「マッチ」のそれぞれについて図心位置マッチングスコアを計算する。
VIII.TBDの範囲内かつ、前のステップで識別されたマッチの組内で、入力されたセグメントグループと輪郭がほぼ等しいデータベースのセグメントグループを識別し、「マッチ」のそれぞれについて輪郭マッチングスコアを計算する。これは2つの輪郭を比較して、それらが一致する範囲を分析的に決定することによりなされる。
グループがステップ1を通過すれば(If the group passes Step 1)
グループがステップ2を通過すれば(If the group passes Step 2)
グループがステップ3を通過すれば(If the group passes Step 3)
グループがステップ4を通過すれば(If the group passes Step 4)
比較が成功で、結果を保存(Successful comparison, save result)
エンド・イフ(END IF)
エンド・イフ(END IF)
エンド・イフ(END IF)
エンド・イフ(END IF)
エンド・フォー(END FOR)
入力:
入力画像及びすべてのデータベースの画像について
XI.バウンディングボックス内の画素の低解像度で、正規化され、コントラスト瓶化されたグレースケールの画像。セグメントグループの外側の画素は標準の背景色に設定される。
低解像度の画像内に一連の同軸な(concentric)矩形の画素の「層(tires)」があれば、入力画像の画素値をデータベースのすべての画像の画素値と比較する。以下のように、各比較について、TBDの範囲内でマッチングスコアを有するデータベースの画像を識別する。
各層について最も内側から始まって最も外側に向けて(For each tier, starting with the innermost and progressing to the outermost)
入力画像とデータベースの画像間で画素値を比較(Compare the pixel values between the input and database image)
マッチングスコアの総計を計算(Calculate an aggregate matching score)
もしマッチングスコアがいくつかのTBD限度よりも大きければ(すなわち、ほとんどマッチしている。)(IF matching score is greater than some TBD limit (i.e., close match))
比較は成功で、結果を保存(Successful comparison, save result)
エンド・イフ(END IF)
エンド・イフ(END IF)
エンド・フォー(END FOR)
入力:入力画像及びデータベースのすべての画像について:
X.セグメントグループのバウンディングボックス内の高解像度のグレースケール画像からのウェブレット係数
最低位の係数から初めて最高位の係数へ向けて、入力セグメントグループの画像とデータベースの各セグメントグループの画像のウェブレット係数を連続的に比較する。各比較についてマッチングスコアを比較する。各新たな係数について、TBD限度内の前の(一つ低い位)の係数でマッチングスコアを有していたデータベースのグループのみを考慮する。
TBD限度内で入力画像C0がデータベース画像C0と等しければ(IF input image C0 equals database image C0 within TBD limit)
TBD限度内で入力画像C1がデータベース画像C1と等しければ(IF input image C1 equals database image C1 within TBD limit)
・・・
TBD限度内で入力画像CNがデータベース画像CNと等しければ(IF input image CN equals database image CN within TBD limit)
マッチを終了し、結果とマッチスコアを保存(Close match, save result and match score)
エンド・イフ(END IF)
・・・
エンド・イフ(END IF)
エンド・イフ(END IF)
エンド・フォー(END FOR)
I.「C1」は、C0が最低位の係数で、CNが最高位の係数であるウェブレット係数である。
II.係数を比較するとき、算術的差ではなく、統計学的(例えばガウシアン)基準で比較する。
III.Ci値に応じてデータベース画像に速やかにアクセスできるように、データインデックス技術が使用される。これによりアルゴリズムが最低位から最高位に進むのに伴って、目的のデータベースの部分を連続的に絞り込むことができる。
入力:
I.入力されるセグメントグループの画像
II.各データベースのセグメントグループの画像
における、各セグメントについて[Yavg,Iavg,Qavg,Ysigma,Qsigma,Npixels]のデータの組(「カラーキューブの点」)
各データベースの画像について(FOR each database image)
データベースの画像の各セグメントグループについて(FOR each segment group in the database image)
データベースのセグメントグループの各カラーキューブについてNpixelsが減少する順で(FOR each Color Cube Point in database segment group, in order of descending Npiexels value)
入力の(Y,I,Q)とデータベースの(Y,I,Q)との間でガウシアンがマッチすれば(IF Gaussian match between input (Y,I,Q) and database (Y,I,Q))
I.このセグメントについてマッチスコアを計算(Calculate match score for this segment)
II.セグメントについてのマッチスコアの総計にセグメントのマッチスコアを累計(Accumulate segment match score into aggregate match score for segment group)
III.マッチングスコアの総計があるTBD限度より大きければ(すなわち、ほとんどマッチ)(IF aggregate matching score is greater that some TBD limit (i.e., close match))
比較は成功で、結果を保存(Successful comparison, save result)
エンド・イフ(END IF)
エンド・フォー(END FOR)
エンド・フォー(END FOR)
エンド・フォー(END FOR)
終了(END FOR)
I.あらゆるY,I,Q点のガウシアンエンベロップは、その点のY、I、及びQの標準偏差のRSSにより決定される。
4つの対象の画像の比較(形状比較、グレースケール比較、ウェブレット比較、カラーキューブ比較)はそれぞれ正規化されたマッチングスコアに戻される。データベースの画像に対する入力画像の顕著な特徴のマッチの独立した複数の評価がある。あらゆる単一の比較プロセスにおける不確実性の影響を最小化し、それによって誤ったマッチを返すおそれを最小化するために、現在マッチ=SQRT(WocMoc 2+WCCCMCCC 2+WWCMWC 2+WSGCMSGC 2)という根二乗和の関係を使用して個々の比較の結果が画像のマッチスコアに組み込まれる。WsはTBDパラメータ重み付け係数でMSは4つの異なる比較の個々のマッチスコアである。
この方法では、すべてのCPUが外側のループを含むアルゴリズム全体を実行するが、1つのCPUは最小のNサイクルについてループを実行し、次のNサイクルについては残りのCPUが同時に実行する。
この方法では、1つのCPUがループ機能を実行する。比較が実行されるときには、各比較は別個のCPUに渡されて並列に実行される。
この方法では、CPU間でのデータベース検索の分割を伴い、各CPUがデータベースの1つのセクションの検索を担当し、複数のCPUでセクションが並列に検索される。これは本質的には前記「外側のアルゴリズムの共用」技術の一形態である。
14 装置
18 デジタル画像
20 サーバ
Claims (21)
- 画像を取り込むカメラと、
前記画像の少なくとも一部にデータ処理操作を行ってデータを生成し、このデータをサービスに送信する、ネットワーク対応装置と
を備え、
前記サービスは、前記データを受信し、前記画像内の対象を識別し、種々の種類の目標対象を含む目標対象のデータ特性を記憶しているデータベースを使用して前記画像に存在する対象を他のものから区別し、前記対象を情報のアドレスと関連付け、かつ前記アドレスを前記ネットワーク対応装置に返信するようにプログラムされ、かつ
前記ネットワーク対応装置はさらに、前記対象に関連する情報のアイテムを検索するために前記アドレスを使用するようにプログラムされているシステム。 - 前記ネットワーク対応装置はハンドヘルドコンピュータ又は携帯電話を備える、請求項1に記載のシステム。
- 前記サービスは第2のデータ処理操作を前記画像の少なくとも一部に実行する、請求項1に記載のシステム。
- 前記サービスはさらに、前記画像又は前記データの少なくとも一部に対して多数のアルゴリズムを適用することで前記対象を識別し、かつ前記多数のアルゴリズムの結果と関連付けられた信頼度の関数として前記画像を識別するようにプログラムされている、請求項1に記載のシステム。
- 前記対象はいかなるデータとも符号化されていない、請求項1に記載のシステム。
- 前記画像内に含まれるバーコード又は他の記号の識別を含み、前記サーバは前記バーコード又は他の記号からの情報のアイテムを第2の装置に送信する、請求項1に記載のシステム。
- 前記ネットワーク対応装置に対して遠隔のコンピュータが前記サービスのホストとして機能する、請求項1に記載のシステム。
- 前記サービスはさらに、前記ネットワーク対応装置での表示に適したフォーマットのコンテンツを規定する複数のアドレス間からアドレスを選択するようにプログラムされている、請求項1に記載のシステム。
- 前記情報のアイテムは前記対象に関する商取引を実行するために使用できるデータを含む、請求項1に記載のシステム。
- 前記ネットワーク対応装置はさらに、前記対象に関する商取引を実行するために前記アドレスを使用するようにプログラムされている、請求項1に記載のシステム。
- 前記ネットワーク対応装置はさらに、遠隔のコンピュータのソフトウエアプロセスを開始するために、前記アドレスと前記情報のアイテムのうち少なくとも一つを使用するようにプログラムされている、請求項1に記載のシステム。
- 前記ネットワーク対応装置はさらに、電話通話を開始するために、前記アドレスと前記情報のアイテムのうち少なくとも一つを使用するようにプログラムされている、請求項1に記載のシステム。
- 前記ネットワーク対応装置はさらに、無線通信を開始するために、前記アドレスと前記情報のアイテムのうち少なくとも一つを使用するようにプログラムされている、請求項1に記載のシステム。
- 前記ネットワーク対応装置はさらに、前記サービスから受信したデータをウェブサイトへ送信するために、前記アドレスと前記情報のアイテムのうち少なくとも一つを使用するようにプログラムされている、請求項1に記載のシステム。
- 前記ネットワーク対応装置はさらに、グラフィックスを表示するために前記アドレスの少なくとも一つと前記情報のアイテムを使用するようにプログラムされている、請求項1に記載のシステム。
- 前記ネットワーク対応装置はさらに、可聴音を生成するために、前記アドレスの少なくとも一つと前記情報のアイテムを使用するようにプログラムされている、請求項1に記載のシステム。
- 前記末端のサービスはさらに、前記アドレスの少なくとも一つと前記情報のアイテムから電話通話を開始するようにプログラムされている、請求項1に記載のシステム。
- (a)前記データのプロセスと(b)前記データ中の対象の認識のうちの少なくとも一方が前記装置と前記サーバの間で分配される、請求項1に記載のシステム。
- 前記ネットワーク対応装置は、前記サーバへのビデオ情報を含む動画像を送信する、請求項1に記載のシステム。
- 前記データプロセス動作は、コントラスト強調、ノイズ除去、及びぼけ鮮鋭化からなる群から選択される、請求項1に記載のシステム。
- 前記データプロセス動作が前記対象の識別の前に前記画像に対して適用される、請求項1に記載のシステム。
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WO2007089533A2 (en) | 2007-08-09 |
US9288271B2 (en) | 2016-03-15 |
US8582817B2 (en) | 2013-11-12 |
US8824738B2 (en) | 2014-09-02 |
US8494264B2 (en) | 2013-07-23 |
US9578107B2 (en) | 2017-02-21 |
US20190042599A1 (en) | 2019-02-07 |
WO2007089533A3 (en) | 2008-01-10 |
EP1814060A3 (en) | 2008-12-10 |
US20170124119A1 (en) | 2017-05-04 |
JP2012194984A (ja) | 2012-10-11 |
US20110268317A1 (en) | 2011-11-03 |
US20100011058A1 (en) | 2010-01-14 |
US20130034267A1 (en) | 2013-02-07 |
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