100326_セミナー資料_物体認識.pptx

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4 物体認識 検出 について '()*++,-./#,0121#3)+, ":; 物体検出は簡単か 多様な変動要素が存在して難しい 変動要素に対応する特徴量 学習手法がキー カメラの角度 姿勢 向き 形状が多様 照明変動 オクルージョン

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6 特定物体認識の処理の流れ 画像中の部分領域が検出対象かどうかを比較 部分領域 比較 物体情報 特徴量 画像サイズを 変えながら比 較 学習手法 認識処理 特定物体認識を実現する要素 特徴量 学習手法 認識する物体の情報を抽出 特徴量を汎化性のあるモデルへ 多様な変化が生じても 共通な情報に変換 多様な変化が生じても 共通な情報を選択 Edge, Color, haar-like, HOG Neural Network Support Vector Machine AdaBoost 学習手法の発展とともに特徴量も発展

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66 物体の探索方法 スライディングウィンドウ 物体の探索方法 スライディングウィンドウ 検出ウィンドウをずらしながら 位置 大きさを変えて探索 すべての位置 大きさを探索 するのは計算コスト大

67 物体の探索方法 一定間隔ごとに荒く探索! 物体らしい領域のみ詳細に探索 計算コストを削減することができる 浅い階層はo205;.探索 深い階層はQ61.探索 非物体 物体 非物体 物体 物体 さらに計算コストを削減することができる 非物体

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73 ! MIT+CMU Frontal Face Image! CMU Profile Face Image! MIT CBCL Face Data! Yale Face Database B! PIE! UIUC Image Database for Car Detection! MIT CBCL Car Data! Testing Images for Car Detection!

74 ! INRIA Person Dataset! USC Pedestrian Detection Test Set! MIT CBCL Pedestrian Data! Gavrila s Benchmark Dataset! NICTA! computer_vision_datasets/! OTCBVS! Open CV! INRIA Object Detection Toolkit (HOG)!

IPSJ SIG Technical Report Vol.2009-CVIM-167 No /6/10 Real AdaBoost HOG 1 1 1, 2 1 Real AdaBoost HOG HOG Real AdaBoost HOG A Method for Reducing

IPSJ SIG Technical Report Vol.2009-CVIM-167 No /6/10 Real AdaBoost HOG 1 1 1, 2 1 Real AdaBoost HOG HOG Real AdaBoost HOG A Method for Reducing Real AdaBoost HOG 1 1 1, 2 1 Real AdaBoost HOG HOG Real AdaBoost HOG A Method for Reducing number of HOG Features based on Real AdaBoost Chika Matsushima, 1 Yuji Yamauchi, 1 Takayoshi Yamashita 1, 2 and

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