JP4098087B2 - 画像を分析するための方法及び装置 - Google Patents
画像を分析するための方法及び装置 Download PDFInfo
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Images
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Description
ライン126(白閾値)を超える輝度を有する白背景、
勾配領域または垂直線領域128として特徴付けられた遷移セクション、
ライン130(グレー閾値)未満の輝度を有する内部黒部分、及び、
ライン126とライン130の間の輝度を有するグレーセクション
である。比較的小さなフォントサイズ(例えば画質およびスキャン解像度に応じて、8ポイント未満など)のテキストは、内部黒部分がわずかであるか又はまったくない状態である遷移のみの領域として特徴付けることができる。好ましい実施形態では、Kittler−Illingworthのヒストグラムベースの閾値アルゴリズム(J.KittlerおよびJ.Illingworth著「Minimum Error Thresholding」(Pattern Recognition, Vol.19,No.1, 41〜47, 1986)に記載されている)を用いて適当な白閾値を見つけ、その白閾値を半分にしたものでグレー閾値を表わす。遷移エリアは、閾値を約30に設定して、勾配ノルム(図4の場合|s(i+1)−s(i−1)|で計算される)によって識別することができる。図4の例では、白閾値が約230に設定され、グレー閾値が約115に設定されている。
(A)テキスト文字は大抵、同種類のピクセルのクラスタから構成されている。これは、テキスト文字のHCC分析の結果が一般に、1つの優勢サブブロッブとその他のエッジサブブロッブとから構成されているという結果になるであろうことを意味する。他のサブブロッブも多数存在し得るが、テキスト文字の場合、それらのサイズは一般に小さいものである。
(B)図のオブジェクトは通常、同種類から構成されていない。従って、図のオブジェクトの一般的なHCC分析の結果は大抵、画像が異なるタイプの複数のそれほど大きくないサブブロッブから構成されているという結果になる。
(A)ブロッブ内に所与の閾値(ピクセルカウント)Tbig(例えば150dpiの場合は15、300dpiの場合は約60など。他の解像度の場合の閾値も、経験的に選択したり、150dpiおよび300dpiの値から適当にスケーリングしたりすることで得られる)よりも大きなサイズの異なる非エッジサブブロッブが複数存在する場合、そのブロッブは図として特徴付けることができる。これは、2レベル階層分析方法の利点を例示している。「背景」/「非背景」および各サブクラスについてのピクセルカウンタに基づく最上位レベルだけの結合度分析を行なうことも可能であったが、それらのサブクラスピクセルの空間情報が失われる可能性があった。
(B)すべての他の非背景ブロッブは1つしか優勢サブブロッブを持たず、それらの非背景ブロッブをその優勢サブブロッブで特徴付けることができることが好ましい。例えば、黒テキストとして特徴付けられるブロッブでは、黒のサブブロッブが優勢であることが好ましい。
Claims (9)
- デジタル画像を分析するための方法(146)であって、
画像ソースから画像の複数の要素を表わすデータをプロセッサ (32) により受信するステップ(112)と、
前記複数の要素の各要素を、背景、カラー、及びエッジを含む少なくとも3つの異なるタイプの知覚される特徴に従って前記プロセッサ (32) により特徴付けるステップ(118)と、
前記特徴のうちの1つを所与の特徴(158,160A-D,162)として有する要素であって、該所与の特徴とほぼ同一の特徴を有する要素に隣接する各要素を前記プロセッサ (32) により識別するステップ(120)と、
を含み、前記ほぼ同一の特徴を有する要素が結合され、一意のラベルが付され、前記少なくとも3つの特徴にしたがって付されたラベルが、2以上の階層に配置され、それによって、それらの結合要素に対し、異なる階層に分類される結合要素のグループ間における空間的関係を特徴付けるための階層的結合要素分析を実施することを可能にすることからなる方法。 - 前記プロセッサ (32) は、第1レベルの結合要素のような要素をブロッブと呼ばれる第1のタイプの領域に分類し、第2レベルの結合要素のような要素をサブブロッブと呼ばれる第2のタイプの領域に分類する、請求項1の方法。
- 前記第1レベルの要素は、背景(158)を表わすピクセルである、請求項2の方法。
- 前記プロセッサ (32) は、前記画像中の他のピクセルを非背景に分類し、背景ピクセルと非背景ピクセルの両方を複数のサブブロッブにさらに分類する、請求項3の方法。
- 前記第2レベルの要素は、黒、白、グレー、及びカラーを含む色情報を表わすピクセルである、請求項2の方法。
- 前記プロセッサ (32) は、ブロッブ、及びサブブロッブを分類し、該分類にしたがって前記ブロッブ、及びサブブロッブを処理する、請求項2の方法。
- 前記画像の要素のうちの、エッジを表わす隣接要素を識別するステップと、
エッジ(160B)を表わす第1のピクセルを選択するステップと、
エッジ(160B)を表わす複数の隣接ピクセルのうちのいずれかを識別するステップと、
をさらに含み、
これらのステップが、前記プロセッサ (32) により実施される、請求項1の方法。 - 前記エッジを表わす複数の隣接ピクセルのうちのいずれかを識別するステップが、該エッジを表わす隣接ピクセルに一意のラベルを付けるステップを含む、請求項7の方法。
- 前記同一ラベルを有し結合しているピクセルを識別するステップが、同一ラベルを有する結合ピクセルにサブラベル(152)を付けるステップをさらに含む、請求項8の方法。
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
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US09/709,685 US7672022B1 (en) | 2000-04-07 | 2000-11-09 | Methods and apparatus for analyzing an image |
PCT/US2001/045584 WO2002056255A2 (en) | 2000-11-09 | 2001-10-25 | Methods and apparatus for analyzing an image |
Publications (3)
Publication Number | Publication Date |
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JP2004529404A JP2004529404A (ja) | 2004-09-24 |
JP2004529404A5 JP2004529404A5 (ja) | 2005-12-22 |
JP4098087B2 true JP4098087B2 (ja) | 2008-06-11 |
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JP2002556841A Expired - Fee Related JP4098087B2 (ja) | 2000-11-09 | 2001-10-25 | 画像を分析するための方法及び装置 |
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Country | Link |
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US (1) | US7672022B1 (ja) |
EP (1) | EP1334462B1 (ja) |
JP (1) | JP4098087B2 (ja) |
AU (1) | AU2002239437A1 (ja) |
DE (1) | DE60118240T2 (ja) |
WO (1) | WO2002056255A2 (ja) |
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- 2001-10-25 EP EP01987198A patent/EP1334462B1/en not_active Expired - Lifetime
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- 2001-10-25 JP JP2002556841A patent/JP4098087B2/ja not_active Expired - Fee Related
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US7672022B1 (en) | 2010-03-02 |
DE60118240T2 (de) | 2006-09-07 |
AU2002239437A1 (en) | 2002-07-24 |
WO2002056255A2 (en) | 2002-07-18 |
JP2004529404A (ja) | 2004-09-24 |
WO2002056255A3 (en) | 2002-10-31 |
EP1334462A2 (en) | 2003-08-13 |
EP1334462B1 (en) | 2006-03-22 |
DE60118240D1 (de) | 2006-05-11 |
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