27 (2015)
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1 27 (2015)
2 27 (2015)
3 SIFT HSV [ ] SIFT SIFT SURF FAST SIFT HSV SIFT HSV SIFT HSV 2 SIFT HSV 1 100% 93%
4 A b s t r a c t Title Author Advisor Key Words A study on the comic book title recognition method for automatic bookshelf sorter Masashi Barada Taichi Watanabe Comic, Book sorter, Image prosessing, SIFT, HSV histogram [summary] In recent years, comic sales of book is on the rise. In Oricon survey nearly half of the book is a comic book. Net off was carried out or questionnaire survey there is a Books of how much inside the house. Book is average 356 books. Comic is average 298 books. From this, I understand that even an individual possesses a comic book in large quantities. It takes considerable time to organize such a large quantity of books. For such a problem, a study is conducted about the book organizeing of the library mainly. Referring the human action performed when taking out the Books, was it possible to grip the tilted books or fallen Books by using a robotic arm having three fingers. They also succeeded in carrying out the rearrangement of the Books by realizing the take-out operation and the storing operation. However, pasted your own marker and self-made bar code for recognition on the back cover of the Books to in these previous studies. take a lot of trouble Given that use in the one person. In this study, focusing on the characteristics of the images were considered to recognize the back cover of cartoons. In late years, SIFT is finds a feature from in image for method of comparing. SURF and FAST was speeding up the SIFT methods. In the present study is to enable the recognition of the back cover of the comic book by using the SIFT feature amount and the HSV histogram. In SIFT feature values is improved the recognition frequency by considering the features of height for comparing. In HSV histogram improved the recognition frequency by only title comparisons. Hybrid was the geometric mean of the SIFT feature value and HSV histogram. Do not consider the color elements of the SIFT. Therefore, improved recognition frequency by combining the HSV histogram. As a result, it eliminates the need to retrofitted with markers and bar code for recognition on the back cover. It was reduce user effort when considering the application of the personal to the Books sorter. In the hybrid, the probability that the comic book of the same title can be recognized at least one book was 100%. In The hybrid, The probability of recognizing all the comic book of the same title was 93%.
5 SIFT HSV SIFT HSV
6 ( 128) SIFT SIFT HSV
7 3.7 HSV
8 SIFT HSV ISBN
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10 1.1 [1] ( 97.3%) ( 101.3%) 2 [2] [3] 3 [4] Laser Range Finder 1.1 OCR 2
11 1.1 SIFT[5][6][7] SIFT SURF[8][9] FAST[10] SURF [11][12][13] SIFT [14] SIFT [15] [16] SIFT 17 SIFT 88.7% SIFT HSV 3
12 2 SIFT HSV SIFT HSV
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14 OCR
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![Jain[17]](/docs-images/92/109590172/images/18-1.jpg "1 7 2.5 2.")
18 2.4 ( 128) Jain[17]
![適応型閾値で二値化した画像に対して輪郭線の抽出を行った 輪郭線の抽出には鈴木ら [18] の](/docs-images/92/109590172/images/19-0.jpg "輪郭抽出アルゴリズムを利用した 図 2.6 が輪郭線要素を抽出した画像である 図 2.6 抽出した輪郭線画像 2.3.")
19 適応型閾値で二値化した画像に対して輪郭線の抽出を行った 輪郭線の抽出には鈴木ら [18] の 輪郭抽出アルゴリズムを利用した 図 2.6 が輪郭線要素を抽出した画像である 図 2.6 抽出した輪郭線画像 境界の分割方法 境界線の分割は様々な漫画本の特徴を利用することで実現する 2.1 項で説明したように漫画本 のページ数はある程度決まっているため 本棚の幅とカメラから写真を撮る位置を考慮すること で 本棚に入るおおよその漫画本の数が推測できる 図 2.7 は推測した漫画本の幅である このような推測した幅のことを以後スロットと呼ぶ 図 2.8 はスロットの間隔を狭めた図である これにより 境界線候補の量を増やし 取りこぼしをな くす処理を行った 図 2.7 推測した幅 図 2.8 スロットの間隔を狭くする 11
20 図 2.9 は 節で検出した輪郭線の要素をスロット毎に計測する方法である 計測は横方向 1 ピクセル毎に縦方向にどれだけ輪郭線の要素があるか測っている 図 2.10 はスロット内で最も輪 郭要素が多かった場所に輪郭線候補を 1 本引いた画像である 図 2.10 輪郭要素の多い部分に境界線候補描画 図 2.9 スロット毎に輪郭要素測定 すべてのスロットに対し 境界線候補を引いたのが 図 2.11 である 漫画本の境界に線が引け ているが スロットを狭くしたため 無駄な境界線も多い 図 2.11 すべてのスロットに境界線候補を描画 余分な境界線の除去を行うことで漫画本を 1 冊単位に分割する 先ほど検出した境界線候補間 12
21 A,B,C,D y x A x B x C x D x H (2.1) B x C x < H 2 (2.1) BC (2.2) A x C x B x D x (2.2) AC BD B BD AC C
22 SIFT HSV 2.5 SIFT 2.6 HSV 2.7 SIFT HSV 14
23 SIFT Lowe[5] SIFT SIFT SIFT SIFT n L 1,,L n M M = n k=1 L k n (2.3) n ( ) 15
24 (2.4) E y o y y p y h 1 E = y o y p h (2.4) 1.0 y o y p E < G (2.5) G (2.5) 2.6 HSV HSV[19] (Hue) (Saturation) (Value)
25 [20][21][22] (2.6) d H 1 m i m d(h 1, H 2 ) = log( H1i H 2i ) (2.6) i= ( ) SIFT J 2.6 HSV K (2.7) I I = JK (2.7) ( ) JAN [23] 2.15 JAN 17
26 ISBN 10 2 ISBN ISBN10 ISBN13 ISBN ISBN ISBN10 ISBN
27 2 [24] ISBN ISBN 19
28 としたバーコードの書名コードより大きいもののうち最小のものを右側にする これにより 対 象としたバーコードに最も近い巻数を求めることができる ただし 書名コードは発行日順に割り振られているため 同タイトルの本か判断は出来ない そのため データベース内で同出版者コードの最も近い書名コードの背表紙画像とハイブリッド を用いて比較することで 同タイトルの本かどうか特定する 図 2.16 から図 2.18 はスパイラル各巻のバーコードの上段である 4 桁の出版者コードが 1 巻 から 3 巻で同じことが確認できる また 4 桁の書名コードは巻数が増えるごとに増加している 図 巻のバーコード 図 巻のバーコード 20 図 巻のバーコード
29 3
30 OpenCV2.49[25][26][27] C
31 SIFT (2.3) n
32 GTO GTO GTO !! !! !!
33 1 3 3 SIFT 3.2 (2.3) (2.4) (2.5) 3 25
34 3.2 3 SIFT ( ) ( ) GTO GTO GTO !! !! !!
35 (2.4) (2.5) SIFT (2.4) (2.5) SIFT SIFT
36 SIFT SIFT 1 28
37 SIFT % % SIFT 0.2 SIFT SIFT % % HSV 3 HSV 3.3 HSV 3 29
38 3.3 3 HSV H S V GTO GTO GTO !! !! !!
39 HSV
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41 28 79% % SIFT SIFT( 0.05) HSV SIFT 3 33
42 3.4 3 ( ) ( ) GTO GTO GTO !! !!2 1.86!! SIFT 34
43 HSV SIFT HSV 3.9 SIFT HSV
44 SIFT HSV SIFT SIFT HSV SIFT HSV SIFT 4 36
45 HSV % % SIFT SIFT SIFT( ) SIFT HSV HSV SIFT HSV
46 1 SIFT 100% HSV 79% SIFT 79% SIFT 82% HSV 57% 93% HSV SIFT ISBN ISBN X ISBN ISBN
47 ISBN (2.7)
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49 SIFT HSV 2 SIFT HSV SIFT HSV SIFT HSV CAD 161 [28] 41
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SUMMARY Although the situation where sufficient food was not supplied for the victims occurred in the Great East Japan Earthquake, this is a serious problem at the time of catastrophic disasters like the
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