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1 27 A study on estimation of ring size using a palm image

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3 Abstract A study on estimation of ring size using a palm image Hideki YOSHIKAWA Recently, surprise marriage proposals are often aired in TV dramas. But actually, it is difficult to examine the ring size of girl friend without being noticed. In this study, we have proposed a system to estimate ring size by using a palm image taken from a known distance with a smartphone. An input RGB image is converted to a grayscale image, and binarized by using a fixed threshold value. The palm region is detected as largest white area. Then angle of each finger is determined by robust estimation of lines using contour of fingers. The image of a finger selected by a user is rotated to make it parallel to Y axis, and is cropped to make just one finger image. After that, length of white area is scanned in each horizontal lines, then the finger width is determined as maximum of those length. The length in the image is converted into actual finger width, and finally ring size is estimated. We have confirmed that result shows 90% match between the size estimated by our system and the size that user prefered. key words Ring, Smartphone, Photogrammetry, Robust estimation ii

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19 3.3 手首部分の除去 3.3 手首部分の除去 今回の提案システムで用いる入力画像は, 撮影領域内に開いた手が全て収まっており, かつ 撮影機器と手を並行にして写っている写真が望ましい. 入力画像の例を図 3.2 に示す. 図 3.2 入力画像例 図 3.2 のような写真を入力画像として与えることで, 精度の高い結果が得られる. しかし, 今回の提案システムで必要な部分は手のひら部分のみの為, 手首から下の部分は必要がない. また撮影状況によっては服の袖や腕時計が写ってしまい, 後の閾値指定による二値化処理の 段階で, 大量のノイズが発生してしまう場合がある. その為入力画像の底部から 1/4 を黒く 塗りつぶし, 今回のシステムに不必要な部分を入力画像中から除去する. 除去を行った結果画 像を図 3.3 に示す. 除去後, 二値化処理に移る. 11

20 3.4 二値化処理 図 3.3 手首部分除去後 3.4 二値化処理 手領域と思われる部分を検出するために, グレースケール化した入力画像の明度に対して 閾値を設け, 閾値以上だった場合を手領域の候補として白色で描画する処理を行う. 処理を行う前に, 複数の入力画像に対して適切に手領域を検出できる閾値の値を設定しな ければならない. 閾値の設定を行うにあたってまず,1 枚の入力画像にいくつかの閾値を与え 元画像との欠損具合を比較し, 欠損が軽微であり, かつ誤検出の発生量が少ない閾値を今回 の提案システムで用いる閾値として設定する. なお, 適切な閾値と判断する基準としては, 画 像を肉眼で確認した際に欠損が軽微であり, かつ誤検出が少ないという条件を満たすものと する. 比較結果を図 3.4 に示す. 図 3.4 の補足として, 欠損部分をわかりやすくする為, 手領 域として検出された部分を元画像で, 欠損部分を薄い緑で表示している. 12

21 3.4 二値化処理 図 3.4 閾値の変化による欠損度およびノイズ量の比較 比較結果より, 閾値は 程が誤検出が少なく, かつ欠損も比較的軽微であることがわ かる. 閾値 20 では黒い部分が検出されており, 手領域よりも検出領域が膨張している事が分 かる. この工程を, 後の評価実験で用いる画像の枚数分繰り返し, 誤検出が少なく欠損が軽微 な閾値を設定する. 結果として, 用いた 10 枚の内 1 枚が閾値 40 にて大量の誤検出が発生し, 後に行う誤検出部分の除去処理でも除去しきれない結果となった. 閾値 50 では誤検出の大 幅な減少が見られた為, 今回の提案システムでは, 閾値を 50 に設定し二値化処理を行う. 閾 値を 40 に設定し二値化を行った結果と,50 に設定し二値化を行った結果を図 3.5 に示す. 二 値化処理後, ラベリングによる最大白色領域検出に移る. 13

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