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1 27 AR
2 AR AR
3 AR AR : :
4
5 1 AR AR [1] [2, 3] 3
6 [4] [5, 6] 1.1 PC AR AR ID AR [5] ID PC 1.2 AR 4
7 1.1: AR AR ID ARToolkit[7] 2 ID ARToolkit ARTag[8] 5
![1.2.2 [9] AR ARTag ID 1.2 1.2: 1.2.3 2 2](/docs-images/94/119039503/images/8-0.jpg "LLAH(Locally Likely Arrangement Hashing )")
8 1.2.2 [9] AR ARTag ID : LLAH(Locally Likely Arrangement Hashing ) 6
![1 LLAH [10] 4 ( ) 4 1.3 n m m 4 1.](/docs-images/94/119039503/images/9-0.jpg "3 7 6 LLAH RANSAC RANSAC[11] LLAH 1.")
9 1 LLAH [10] 4 ( ) n m m LLAH RANSAC RANSAC[11] LLAH 1.3: p ([10] )
![Random Dot Markers[9] RANSAC 40 6](/docs-images/94/119039503/images/10-0.jpg "100ms RANSAC 1.4: ( 100 ) 1.")
10 Random Dot Markers[9] RANSAC ms RANSAC 1.4: ( 100 ) 1.3 AR ( ) 8
11 2 AR 2.1 AR [12] AR ARToolkit[7] 2 AR HSV 2 ARToolkit HSV [13] 2 1 9
12
13 3 1: [14] 1 2 HSV ( 2 ) 1 uchiyama/me/index_e.html 11
14 3.1: PC Panasonic CF-SX1(Core i5-2540m vpro 2.60GHz 4GB Microsoft LifeCam Studio( ) cm m 2 ( 3.2 ) 2.4m( 3.2 ) 3.4m( 3.2 ) 4.0m( 3.2 ) 5 12
15 3.3 ( 3.2) 3 ( 3.3)
16 図 3.2: 従来手法の認識結果 図 3.3: 赤色ドットマーカの認識結果 14
17 4 2: : ( 4.2) 15
![[12] HSV HSV 8](/docs-images/94/119039503/images/18-0.jpg "( 4.3) 2 4.")
18 [12] HSV HSV 8 ( 4.3) 2 4.2: 16
19 4.3: 4.3 HSV 17
20 PC Panasonic CF-SX1(Core i5-2540m vpro 2.60GHz 4GB PC ( ) A4 ( ) 1cm ( 5.1) 4 2( 5.2) 27 3( 5.3) 76 18
21 図 5.1: 環境 1 図 5.2: 環境 実験方法 実際の教室における机の位置で実験を行ったため マーカを設置する距離は環境によっ て異なる 環境 1 では 2.5m の位置に 環境 2 では 3.4m 環境 3 では 3.0m の位置に横一 列に 4 つマーカを並べた プログラムをスタートし 初めて 4 つのマーカを同時に検出 した時の検出処理時間を計測し またそのときのドットの数を記録する 19
22 5.3: : [ ]
![5.2: [ms] 1 2 3 2 2 2 1 103 75 111 86 58 91 2 98 100 108 86 142 90 3 104 99 200 74 102 80 4 111 109 120 107](/docs-images/94/119039503/images/23-0.jpg "123 93 5 106 78 111 92 126 105 104.4 92.2 130 89 110.2 91.8 5.4: 2 1 5.5 100 2.5m PC 5.1 226.8ms 2 98.")
23 5.2: [ms] : m PC ms ms 21
![図 5.5: 特徴点が多い環境での実験の様子 表 5.3: ドットが多い環境での比較結果 ドット数 [個] 5.](/docs-images/94/119039503/images/24-0.jpg "5 処理時間 [ms] 従来手法 提案手法 2 従来手法 提案手法 2 1 回目 288 129 188 99 2 回目 294 125 212 86 3 回目 291 126 229 196 4 回目 286 128 196 91 5 回目 292 126 309 110 平均 290.2 126.8 226.8 98.")
24 図 5.5: 特徴点が多い環境での実験の様子 表 5.3: ドットが多い環境での比較結果 ドット数 [個] 5.5 処理時間 [ms] 従来手法 提案手法 2 従来手法 提案手法 2 1 回目 回目 回目 回目 回目 平均 認識距離について 認識距離において 従来手法と提案手法 2 の比較実験についても行った 提案手法 1 の評価実験と同じように カメラからマーカの距離が 1.5m の位置に 2 つ 2.4m に 1 つ 3.4m に 1 つ 4.0m に 1 つの計 5 つのマーカが認識可能であるか両手法で実験を行った 機材については本実験と同じものを用いた 照明環境について 日光は遮断していて蛍 光灯を点灯させた室内で実験を行った 結果は 従来手法については図 5.6 提案手法 2 については図 5.7 のようになった 従来手法では 3.4m までの位置にある 4 つのマーカを 認識できた 提案手法 2 では 3.4m のマーカまで認識できているが 1.5m 位置の右側の 22
25 マーカが認識されなかった 図 5.8 から 赤色領域の抽出はできているため 2 値化処理 の際に特徴点の抽出に失敗していることが原因であると考える 今回はマーカを設置し た台の影がドットの抽出を邪魔していて 解決策としては厚紙に印刷して裏写りしない ようにすることが挙げられる したがって 提案手法 2 の認識距離は従来手法と同等で あると考える 図 5.6: 従来手法の認識距離 図 5.7: 提案手法 2 の認識距離 23
26 5.8: ms 24
27 6 6.1 AR 2 HSV AR 25
28 [15] AR 26
29 図 6.1: 置き型マーカの例 27
30 28
31 [1] Jen-Kai Liang Huang, Chi-Wei and Hsu-Yie Wang. Educlick: A computer-supported formative evaluation system with wireless devices in ordinary classroom. In Proceedings of ICCE 2001 (2001): [2] Jane E. Caldwell. Clickers in the large classroom: Current research and best-practice tips. In CBE-Life Sciences Education 6.1 (2007): [3],. ( ) :.. (2012): [4],,,,,,,,,,,,,.., 28(5), , [5] Andrew Cross, Edward Cutrell, and William Thies. Low-cost audience polling using computer vision. Proceedings of the 25th Annual ACM Symposium on User Interface Software and Technology, UIST 12, pp. 4554, New York, NY, USA, ACM. [6] Motoki Miura and Toyohisa Nakada. Device-Free Personal Response System based on Fiducial Markers. In Proceedings of the 7th IEEE International Conference on Wireless, Mobile, and Ubiquitous Technologies in Education (WMUTE2012), Takamatsu, Kagawa, pp , [7] Hirokazu Kato and Mark Billinghurst. Marker tracking and hmd calibration for a videobased augmented reality conferencing system. In Augmented Reality, 1999.(IWAR 99) Proceedings. 2nd IEEE and ACM International Workshop on (pp ). IEEE,
32 [8] Mark Fiala. ARTag, a fiducial marker system using digital techniques. In Computer Vision and Pattern Recognition, CVPR IEEE Computer Society Conference on. (Vol. 2, pp ). IEEE, [9] Hideaki Uchiyama and Hideo Saito. Random Dot Markers. In Virtual Reality Conference (VR), 2011 IEEE (pp ). [10] Tomohiro Nakai, Koichi Kise, and Masakazu Iwamura. Use of affine invariants in locally likely arrangement hashing for camera-based document image retrieval. In Document Analysis Systems VII. Springer Berlin Heidelberg, [11] Martin A. Fischler and Robert C. Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. In Communications of the ACM 24.6 (1981): [12],. AR. Vol [13]. 2., [14],,.. (MIRU2011) 2011 (2011): [15], (to appear). 30
33 2016 (to appear) Manabu Ito, Motoki Miura: Portable Vision-based Response Analyzer with Sheet Bending Recognition, Proceedings of GCCE 2015, Osaka, Japan, pp , October
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258 5) GPS 1 GPS 6) GPS DP 7) 8) 10) GPS GPS 2 3 4 5 2. 2.1 3 1) GPS Global Positioning System
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1,a) 2 2 1. 1 College of Information Science, School of Informatics, University of Tsukuba 2 Faculty of Engineering, Information and Systems, University of Tsukuba a) oharada@iplab.cs.tsukuba.ac.jp 2.
18 1 1 1.1...................................... 1 1.2................................... 1 1.3................................... 1 2 2 2.1......................... 2 2.1.1...........................
3 webui [1] 3 3 3D e- 3D 1 1a 1b 3 2. AR 3 3 AR Autodesk 123D Catch [3] Autodesk 3 Martin [4] Shape From Sillhouette 3 [5] 3 3 Watanabe [6]
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Fig Measurement data combination. 2 Fig. 2. Ray vector. Fig (12) 1 2 R 1 r t 1 3 p 1,i i 2 3 Fig.2 R 2 t 2 p 2,i [u, v] T (1)(2) r R 1 R 2
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24 21 21115025 i 1 1 2 5 2.1.................................. 6 2.1.1........................... 6 2.1.2........................... 7 2.2...................................... 8 2.3............................
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VOL. J97-B NO. 7 JULY 2014 本 PDF の扱いは 電子情報通信学会著作権規定に従うこと なお 本 PDF は研究教育目的 ( 非営利 ) に限り 著者が第三者に直接配布することができる 著者以外からの配布は禁じられている LED a) A New LED Array Acquisition Method Focusing on Time-Gradient and Space-
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修士論文の和文要旨 研究科 専攻大学院情報理工学研究科情報 通信工学専攻博士前期課程 氏名春田英和学籍番号 1231074 論文題目 さわれる拡張現実感システムの検討 要 旨 本研究では,AR(Augmented Reality,AR) と様々な入力デバイスを用いた 3DCG モデリングシステムを実装し, さらに物理エンジンと組み合わせることで, さわれる拡張現実感 (AR) システムの有効性を確認した.
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