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  1. Miharu Fuyuno, Rinko Komiya, and Takeshi Saitoh (2018). Multimodal analysis of public speaking performance by EFL learners: Applying deep learning to understanding how successful speakers use facial movement. Asian Journal of Applied Linguistics, 5(1), pp.117-129. web
  2. Warapon Chinsatit and Takeshi Saitoh (2017). CNN-Based Pupil Center Detection for Wearable Gaze Estimation System. Applied Computational Intelligence and Soft Computing, Vol.2017. web
  3. Rinko Komiya, Takeshi Saitoh, Miharu Fuyuno, Yuko Yamashita, and Yoshitaka Nakajima (2017). Head Pose Estimation and Motion Analysis of Public Speaking Videos. International Journal of Software Innovation (IJSI), Vol.5, Issue.1 pp.57-71.
  4. Takeshi Saitoh, Toshiki Shibata and Tsubasa Miyazono (2016). Feature Points based Fish Image Recognition. International Journal of Computer Information Systems and Industrial Management Applications, 8. pp.12-22. pdf
  5. Trung Hieu Bui, Takeshi Saitoh, and Eitaku Nobuyama (2014). Vanishing Point-Based Road Detection for General Road Images. IEICE Transactions on Information and Systems, E97-D(3). pp.618-621. pdf
  6. Takeshi Saitoh (2013). Development of Communication Support System Using Lip Reading. IEEJ Transactions on Electrical and Electronic Engineering. 8(6). pp.574-579. doi: 10.1002/tee.21898. pdf
  7. ꎓ¡ „ŽjC¡‘º «ŒPC•Ÿˆä K”ü i2013jD‰æ‘œˆ—‚É‚æ‚銄‚蔢Œ´Œ`‚ÌŠOŠÏŒŸ¸ƒVƒXƒeƒ€‚ÌŠJ”­C“dŽqî•ñ’ʐMŠw‰ï˜_•¶ŽCVol.J96-DCNo.10Cpp.2570-2579D pdf
  8. Trung Hieu Bui, Eitaku Nobuyama, and Takeshi Saitoh (2013). A Texture-Based Local Soft Voting Method for Vanishing Point Detection from a Single Road Image. IEICE Transactions on Information and Systems. E96-D(3). pp.690-698. pdf
  9. Takeshi Saitoh and Ryosuke Konishi (2011). Real-time word lip reading system based on trajectory feature. IEEJ Transactions on Electrical and Electronic Engineering. 6(3). pp.289-291. doi: 10.1002/tee.20658. pdf
  10. ꎓ¡ „ŽjCX‰º ˜a•qC¬¼ —º‰îi2011jD”­˜bƒV[ƒ“‚©‚ç‚̃L[ƒtƒŒ[ƒ€ŒŸo‚ƃL[ƒtƒŒ[ƒ€‚ÉŠî‚­’PŒê“ǐOC“d‹CŠw‰ï˜_•¶ŽCVol.131CNo.2Cpp.418-424D pdf
  11. –¾ŽR Š°ŽjCì‘º ®¶CùŠÔ r•FC›Œ´ ˆêECꎓ¡ „ŽjC¬¼ —º‰îi2010jDƒXƒPƒWƒ…[ƒŠƒ“ƒO‚É‚æ‚é‘Ò‹@“d—͍팸‹@”\‚ðŽ‚Â‘½‹@”\ƒRƒ“ƒZƒ“ƒg‚ÌŠJ”­Cî•ñˆ—Šw‰ï˜_•¶ŽCVol.51CNo.12Cpp.2287-2297C2010.12D
  12. Takeshi Saitoh, Tomoyuki Osaki, Ryosuke Konishi, and Kazunori Sugahara (2010). Current Sensor based Home Appliance and State of Appliance Recognition. SICE Journal of Control, Measurement, and System Integration. 3(2). pp.86-93. pdf
  13. ꎓ¡ „ŽjC‘½“c ’¼–çC¬¼ —º‰îi2009jD’PŠáƒJƒƒ‰‚ð—p‚¢‚½’†‰›‘–sŒ^‚̉®“àˆÚ“®ƒƒ{ƒbƒgC“d‹CŠw‰ï˜_•¶Ž(C)CVol.129CNo.8Dpp.1576-1583D pdf
  14. Takeshi Saitoh and Ryosuke Konishi (2008). Japanese 45 Single Sounds Recognition Using Intraoral Shape. IEICE Transactions on Information and Systems. E91-D(11). pp.2735-2738. pdf
  15. Takeshi Saitoh, Mitsugu Hisagi, and Ryosuke Konishi (2007). Analysis of Features for Efficient Japanese Vowel Recognition. IEICE Transactions on Information and Systems. E90-D(11). pp.1889-1891. pdf
  16. ꎓ¡ „ŽjC¬¼ —º‰îi2007jDƒgƒ‰ƒWƒFƒNƒgƒŠ“Á’¥—Ê‚ÉŠî‚­’PŒê“ǐOC“dŽqî•ñ’ʐMŠw‰ï˜_•¶ŽCVol.J90-DCNo.4Cpp.1105-1114D pdf
  17. ‘哇 ’¼–çCꎓ¡ „ŽjC¬¼ —º‰îi2007jDƒIƒvƒeƒBƒJƒ‹ƒtƒ[•ª•z‚ð—˜—p‚µ‚½Mean Shift’ǐՁC“dŽqî•ñ’ʐMŠw‰ï˜_•¶ŽCVol.J90-DCNo.4Cpp.1096-1104D pdf
  18. ‘哇 ’¼–çCꎓ¡ „ŽjC¬¼ —º‰îi2006jD‰ñ“]‰Â“®ƒJƒƒ‰‚É‚æ‚郊ƒAƒ‹ƒ^ƒCƒ€l•¨ˆÊ’u„’èƒVƒXƒeƒ€C“d‹CŠw‰ï˜_•¶ŽCCVol.126CNo.8Cpp.957-962D pdf
  19. ꎓ¡ „ŽjC‹àŽq –L‹vi2005jDŽ©‘R‰æ‘œ‚ÉŠî‚­‰Ô‰æ‘œ‚ÌŽ©“®”FŽ¯C“dŽqî•ñ’ʐMŠw‰ï˜_•¶ŽCVol.J89-D-IICNo.12Cpp.2341-2349D pdf
  20. ”öè ’mKC‘哇 ’¼–çCꎓ¡ „ŽjC¬¼ —º‰îi2005jDŽ©ŒÈ‘gD‰»ƒ}ƒbƒv‚ð—p‚¢‚½ƒn[ƒuŽí‚Ì”»•ÊCŒv‘ªŽ©“®§ŒäŠw‰ï˜_•¶WCVol.41CNo.5Cpp.383-387D
  21. ꎓ¡ „ŽjC‹àŽq –L‹vi2004jD³‹K‰»ƒRƒXƒg‚ÉŠî‚­—ÖŠs’Šo–@C“dŽqî•ñ’ʐMŠw‰ï˜_•¶ŽCVol.J87-D-IICNo.12Cpp.2136-2144D pdf
    i‰p–ó˜_•¶jTakeshi Saitoh and Toyohisa Kaneko. (2005). Route Search Method by Normalized Cost. Systems and Computers in Japan. 36(14). pp.11-20. pdf
  22. ꎓ¡ „ŽjC’r“c ‰ë•qCÂ–Ø Œö–çC‹àŽq –L‹vCŠÖŒû —²ŽOi2004jDCT‰æ‘œ‚É‚¨‚¯‚é\‘¢‰ðÍ‚ÉŠî‚­ŠÌ‘Ÿ“àŒŒŠÇ‚Ì’Šo‚ÆŠÌ‘ŸŠà‚ÌŒŸoC“dŽqî•ñ’ʐMŠw‰ï˜_•¶ŽCVol.J87-D-IICNo.6Cpp.372-382D pdf
    i‰p–ó˜_•¶jTakeshi Saitoh, Masatoshi Ikeda, Kimiya Aoki, Toyohisa Kaneko, and Ryuzo Sekiguchi (2005). Optimal Threshold for Hepatic Blood Vessels based on Structural Analysis and Cancer Detection. Systems and Computers in Japan. 33(7). pp.1-12. pdf
  23. Takeshi SaitohCManabu SatoCToyohisa KanekoCand Shigeru Kiriyama (2004). Transient Texture Synthesis Based on Multiple TemplatesCMachine Graphics and VisionCVol.12CNo.4Cpp.525-537.
  24. ꎓ¡ „ŽjC“c‘º —Y‘¾C‹àŽq –L‹vi2003jDŒŒŠÇŒ`ó‚ÉŠî‚­ŠÌ‘Ÿ—̈æ‚ÌŽ©“®’ŠoC“dŽqî•ñ’ʐMŠw‰ï˜_•¶ŽCVol.J86-D-IICNo.5Cpp.633-641D pdf
    i‰p–ó˜_•¶jTakeshi SaitohCYuta TamuraCand Toyohisa Kaneko (2004). Automatic Segmentation of Liver Region based on Extracted Blood Vessels. Systems and Computers in Japan. 35(5). pp.1-10. pdf
  25. ꎓ¡ „ŽjC‹àŽq –L‹vi2001jD‰Ô‚Æ—t‚É‚æ‚é–쑐‚ÌŽ©“®”FŽ¯C“dŽqî•ñ’ʐMŠw‰ï˜_•¶ŽCVol.J84-D-IICNo.7Cpp.1419-1429D pdf
    i‰p–ó˜_•¶jTakeshi Saitoh and Toyohisa Kaneko (2003). Automatic Recognition of Wild Flowers. Systems and Computers in Japan. 34(10). pp.90-101. pdf

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‰ðà‹LŽ–

  1. ꎓ¡ „ŽjDl‚ÌŠç‚ðƒZƒ“ƒVƒ“ƒO‚µ‚āCl‚̃Rƒ~ƒ…ƒjƒP[ƒVƒ‡ƒ“‚ðŽx‰‡‚·‚é‰æ‘œˆ—‹ZpC”ñ”j‰óŒŸ¸CVol.67CNo.7Cpp.310-316Ci2018.7jD
  2. Takeshi Saitoh. Lip-reading technology, Impact, pp.47-49, (2018.6).Impact [+]
  3. ꎓ¡ „ŽjDƒTƒCƒŒƒ“ƒg‰¹º”FŽ¯C’m”\‚Əî•ñi“ú–{’m”\î•ñƒtƒ@ƒWƒBŠw‰ïŽjCVol.30CNo.2Cp.110Ci2018.4jD
  4. ꎓ¡ „ŽjDKinect‚ð—p‚¢‚½Žè˜b”FŽ¯CÝŒvHŠwC“ú–{ÝŒvHŠw‰ïŽCVol.51CNo.11Cpp.760-764Ci2016.11jD
  5. ꎓ¡ „ŽjD‰æ‘œˆ—‚É‚æ‚銄‚蔢Œ´Œ`‚ÌŠOŠÏŒŸ¸ƒVƒXƒeƒ€‚ÌŠJ”­C“úŠ§H‹Æo”ŁC‰æ‘œƒ‰ƒ{Cpp.1-7Ci2014.5jD
  6. ꎓ¡ „ŽjD‘•]@‚Í‚¶‚߂Ẵpƒ^[ƒ“”FŽ¯CŒv‘ª‚Ɛ§ŒäCŒv‘ªŽ©“®§ŒäŠw‰ïC‘æ52Šª7†Cp.658Ci2013.7jD
  7. ꎓ¡ „ŽjDu‰ñ˜HƒVƒ~ƒ…ƒŒ[ƒ^Qucs‚É‚æ‚é“dŽq‰ñ˜H“ü–åvi“±“ü•ÒjCŒŽŠ§I/OCHŠwŽÐC‘æ38Šª6†Cpp.60-61Ci2013.6jD
  8. ꎓ¡ „ŽjD“ǐO‹Zp‚ÌŒ»ó‚Æ“W–]C“d”g‹Zp‹¦‰ïC“d”g‹Zp‹¦‰ï•ñFORNCNo.292Cpp.32-35Ci2013.5jD
    “d”g‹Zp‹¦‰ï•ñ‚̃y[ƒW [+]
  9. ¬¼ —º‰îCꎓ¡ „ŽjC‘哇 ’¼–çDƒIƒvƒeƒBƒJƒ‹ƒtƒ[•ª•z‚ð—˜—p‚µ‚½Mean Shift’ǐՁ@Fƒ‚ƒfƒ‹‚ƃtƒ[ƒ‚ƒfƒ‹‚𓝍‡‚µ‚½Mean Shift’ǐՖ@‚Ì’ñˆÄC“úŠ§H‹Æo”ŁC‰æ‘œƒ‰ƒ{Cpp.52-56Ci2007.8jD

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ŽóÜ

  1. MVA2017 Best Poster AwardA2017”N
  2. FIT2009i‘æ8‰ñî•ñ‰ÈŠw‹ZpƒtƒH[ƒ‰ƒ€j‘DˆäƒxƒXƒgƒy[ƒp[ÜA2009”N
  3. FIT2007i‘æ6‰ñî•ñ‰ÈŠw‹ZpƒtƒH[ƒ‰ƒ€jƒ„ƒ“ƒOƒŠƒT[ƒ`ƒƒ[ÜA2008”N
  4. ‘æ17‰ñ@“d‹C’ʐM•‹yà’c@ƒeƒŒƒRƒ€ƒVƒXƒeƒ€‹ZpŠw¶ÜA2002”N3ŒŽ
  5. •½¬12”N“x@“dŽqî•ñ’ʐMŠw‰ï“ŒŠCŽx•”@Šw¶Œ¤‹†§—ãÜA2001”N6ŒŽ

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µ‘ҍu‰‰

  1. ꎓ¡ „ŽjDmµ‘ҍu‰‰nƒTƒCƒŒƒ“ƒg‰¹º”FŽ¯‚ÌŒ¤‹†“®Œü@` “ǐO‹Zp‚𒆐S‚Æ‚µ‚Ä `C“dŽqî•ñ’ʐMŠw‰ï@‰¹ºŒ¤‹†‰ïC•ŸŽƒî•ñHŠwŒ¤‹†‰ïCvol.117Cno.250CSP2017-48CWIT2017-44Cpp.77-81D‹ãBH‹Æ‘åŠwi•Ÿ‰ªjC2017.10D
  2. ꎓ¡ „ŽjDmƒI[ƒKƒiƒCƒYƒhƒZƒbƒVƒ‡ƒ“n“ǐO‹Zp‚̃T[ƒxƒCD‘æ4‰ñƒTƒCƒŒƒ“ƒg‰¹º”FŽ¯ƒ[ƒNƒVƒ‡ƒbƒvCp.7DŒv‘ªŽ©“®§ŒäŠw‰ïƒ‰ƒCƒtƒGƒ“ƒWƒjƒAƒŠƒ“ƒO•”–åƒVƒ“ƒ|ƒWƒEƒ€iLE2017jp.36DŠò•Œ‘åŠwJRŠò•Œ‰w‘OƒTƒeƒ‰ƒCƒgƒLƒƒƒ“ƒpƒXiŠò•ŒjD2017.9D
  3. ꎓ¡ „ŽjDmŠé‰æƒZƒbƒVƒ‡ƒ“n‹@ŠB“ǐO‹Zp‚Æ‚»‚̉ž—pD‘æ19‰ñ’m”\ƒƒJƒgƒƒjƒNƒXƒ[ƒNƒVƒ‡ƒbƒvCp.36D‚–ìŽRu•óé‰@vi˜a‰ÌŽRjD2014.7D
  4. ꎓ¡ „ŽjDmƒ`ƒ…[ƒgƒŠƒAƒ‹u‰‰n‹@ŠB“ǐO‹ZpC“dŽqî•ñ’ʐMŠw‰ï@ƒ}ƒ‹ƒ`ƒƒfƒBƒAî•ñƒnƒCƒfƒBƒ“ƒOEƒGƒ“ƒŠƒbƒ`ƒƒ“ƒgŒ¤‹†‰ïCvol.114Cno.33CEMM2014-6Cpp.29-34D“Œ‹ž—‰È‘åŠwi“Œ‹žjC2014.5D
  5. ꎓ¡ „ŽjDO‚¨‚æ‚ÑŒû“à—̈æŒ`ó‚ÉŠî‚­ƒgƒ‰ƒWƒFƒNƒgƒŠ“Á’¥—Ê‚É‚æ‚é“ǐOC‘æ15‰ñ“dŽqî•ñ’ʐMŠw‰ï@ƒtƒFƒ[•ƒ}ƒXƒ^[ƒY–¢—ˆ‹ZpŽžŒÀŒ¤‹†‰ïCFIT2008 ‘æ7‰ñî•ñ‹Zp‰ÈŠwƒtƒH[ƒ‰ƒ€ ƒCƒxƒ“ƒgŠé‰æCî•ñEƒVƒXƒeƒ€Œ¤‹†ŠJ”­‚̍¡Ì|ŽáŽÒ‚Ì–²‚ð‚Ç‚±‚Ü‚Å–c‚ç‚Ü‚¹‚é|Cpp.28-39D2008.9D

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’˜‘

  1. ‰ñ˜HƒVƒ~ƒ…ƒŒ[ƒ^uQucsv‚É‚æ‚éu“dŽq‰ñ˜Hv“ü–å (IEO BOOKS)Cꎓ¡ „ŽjCHŠwŽÐCISBN: 978-4777517473Ci2013.3)D
  2. Computer VisionCIndoor Mobile Robot Navigation by Center Following based on Monocular VisionCTakeshi Saitoh, Naoya Tada and Ryosuke Konishii•ª’SŽ·•MjCIN-TECHCpp.351-366CISBN 978-953-7619-21-3Ci2008.11)D
  3. ’´ŒÜŠ´ƒZƒ“ƒT‚ÌŠJ”­Å‘OüC¬¼ —º‰îCꎓ¡ „Žji•ª’SŽ·•MjC‚m‚s‚rCpp.487-494Ci2005.11)D

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“Á‹–

  1. ꎓ¡ „ŽjC‹g‰ª —R‰FF”rŸ•—\‘ª•û–@C“ÁŠè2018-024967CoŠè“úF2018”N2ŒŽ15“úD
  2. “~–ì”ü°CŽR‰º—FŽqC’†“‡ËDCꎓ¡ „ŽjFƒpƒuƒŠƒbƒNEƒXƒs[ƒLƒ“ƒOŽx‰‡‘•’uA‹y‚уvƒƒOƒ‰ƒ€C“ÁŠè2017-077706CoŠè“úF2017”N4ŒŽ10“úD
  3. ꎓ¡ „ŽjC”яÀ áÁ‹IF•¶Žš—ñ“ü—Í‘•’uC“ÁŠè2015-108708C“ÁŠJ2016-224608CoŠè“úF2015”N5ŒŽ28“úCŒöŠJ“úF2016”N12ŒŽ28“úD
  4. ꎓ¡ „ŽjFƒRƒ~ƒ…ƒjƒP[ƒVƒ‡ƒ“Žx‰‡ƒVƒXƒeƒ€C“ÁŠè2011-182594C“ÁŠJ2013-045282CoŠè“úF2011”N8ŒŽ24“úCŒöŠJ“úF2013”N3ŒŽ4“úD
  5. ꎓ¡ „ŽjFƒ[ƒhƒXƒ|ƒbƒeƒBƒ“ƒO“ǐO‘•’u‹y‚Ñ•û–@C“ÁŠè2010-201629C“ÁŠJ2012-059017CoŠè“úF2010”N9ŒŽ9“úCŒöŠJ“úF2012”N3ŒŽ22“úD
  6. ꎓ¡ „ŽjC¬¼ —º‰îF•¶Žš“ü—Í•û–@C“Á‹–oŠè2008-003472C“Á‹–ŒöŠJ2009-169464CoŠè“úF2008”N1ŒŽ10“úCŒöŠJ“úF2009”N7ŒŽ30“úD
  7. ¬¼ —º‰îCꎓ¡ „ŽjC”öè ’mKFˆÚ“®•¨‘̂̒Ǐ]‘•’u‹y‚Ñ“¯‘•’u‚ð”õ‚¦‚½“d“®ŽÔˆÖŽqC“Á‹–oŠè2006-093072C“Á‹–ŒöŠJ2007-265343CoŠè“úF2006”N3ŒŽ30“úCŒöŠJ“úF2007”N10ŒŽ11“úD
  8. ‹àŽq –L‹vCꎓ¡ „ŽjF—ÖŠs’ŠoƒVƒXƒeƒ€C“Á‹–4491714i“Á‹–oŠè2004-134642C“Á‹–ŒöŠJ2005-316776CoŠè“úF2004”N4ŒŽ28“úCŒöŠJ“úF2005”N11ŒŽ10“úC“o˜^“úF2010”N4ŒŽ16“újD
  9. ‹àŽq –L‹vCꎓ¡ „ŽjFA•¨”FŽ¯ƒVƒXƒeƒ€C“Á‹–3918143i“Á‹–oŠè2000-403201C“Á‹–ŒöŠJ2002-203242CoŠè“úF2000”N12ŒŽ28“úCŒöŠJ“úF2002”N7ŒŽ19“úC“o˜^“úF2007”N2ŒŽ23“újD

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‘ÛŠw‰ï”­•\˜_•¶

  1. Takeshi Saitoh, Iori Yamada, and Yu Yoshioka. Excretion Prediction using Nursing Record System Log Data. Proc. of SICE Annual Conference, FeB07.5, pp.1618-1623C2018.9D
  2. Naoyuki Kan, Nagisa Kondo, Warapon Chinsatit, and Takeshi Saitoh. Effectiveness of Data Augmentation for CNN-Based Pupil Center Point Detection. Proc. of SICE Annual Conference, WeA02.4, pp.41-46C2018.9D
  3. Takuma Sakai, Kyota Nakamura, Takeshi Saitoh, and Keiko Tsuchiya. Analysing a leader's eye gaze in emergency care simulation. Proc. of International Forum on Quality & Safety in Healthcare, 180, 2018.9D
  4. Takeshi Saitoh and Michiko Kubokawa. SSSD: Speech Scene Database by Smart Device for Visual Speech Recognition. 24th International Conference on Pattern Recognition (ICPR2018)Cpp.3228-3232C2018.8D
  5. Rinko Komiya, Takeshi Saitoh, and Kazutaka Shimada. Image-based Attention Level Estimation of Interaction Scene by Head Pose and Gaze Information. 17th IEEE/ACIS International Conference on Computer and Information Science (ICIS2018), pp.497-501, 2018.6.6-8D
  6. Rinko Komiya, Takeshi Saitoh, and Kazutaka Shimada. Image-based Attention Estimation for Interaction Scene. International Conference on Information and Communication Technology Robotics (ICT-ROBOT2017), 104A2, 2017.11.25-26D
  7. Seungsu Lee and Takeshi Saitoh. Head Pose Estimation Using Convolutional Neural Network. Proc. of 7th iCatse International Conference on IT Convergence and Security (ICITCS2017), Lecture Notes in Electrical Engineering 449, pp.164-171C2017.9D
  8. Tsubasa Miyazono and Takeshi Saitoh. Fish Species Recognition Based on CNN Using Annotated Image. Proc. of 7th iCatse International Conference on IT Convergence and Security (ICITCS2017), Lecture Notes in Electrical Engineering 449, pp.156-163C2017.9D
  9. Nagisa Kondo, Warapon Chinsatit, and Takeshi Saitoh. Pupil Center Detection for Infrared Irradiation Eye Image Using CNN. Proc. of SICE Annual Conference, WeA05.2, pp.100-105C2017.9D
  10. Tomoya Kodama, Tomoki Koyama, and Takeshi Saitoh. Kinect Sensor Based Sign Language Word Recognition by Mutli-Stream HMM. Proc. of SICE Annual Conference, WeA05.1, pp.94-99C2017.9D
  11. Michiko Kubokawa and Takeshi Saitoh. Intensity Correction Effect for Lip Reading. 28th British Machine Vision Conference (BMVC2017) workshop on Lip-Reading using Deep Learning Methods (LRDLM2017), 2017.9.7D
  12. Miharu Fuyuno, Rinko Komiya, and Takeshi Saitoh. Analyzing Public Speaking for EAP Pedagogy: Factors of Better Performance. CAES International Conference Faces of English 2, p.53, 2017.6.1-3
  13. Masaya Iwasaki, Michiko Kubokawa, and Takeshi Saitoh. Two Features Combination with Gated Recurrent Unit for Visual Speech Recognition. IAPR International Conference on Machine Vision Applications (MVA2017), 09-24, pp.300-303, 2017.5.8-12D
    • Best Poster Award
  14. Takeshi Saitoh, Ziheng Zhou, Guoying Zhao, and Matti Pietikainen. Concatenated Frame Image based CNN for Visual Speech Recognition. ACCV2016 workshop: Multi-view Lip-reading/Audio-visual Challenges (MLAC2016), 2016.11.20D
  15. Masaya Iwasaki and Takeshi Saitoh. LBP-TOP based Facial Expression Recognition using Non Rectangular ROI. International Conference on Information and Communication Technology Robotics (ICT-ROBOT2016), ThBT2.2, 2016.9.7-9D
  16. Warapon Chinsatit and Takeshi Saitoh. CNN for pupil center detection. International Conference on Information and Communication Technology Robotics (ICT-ROBOT2016), ThBT2.1, 2016.9.7-9D
  17. Warapon Chinsatit and Takeshi Saitoh. Improve the Performance of Eye Detection Method for Inside-Out Camera. 15th IEEE/ACIS International Conference on Computer and Information Science (ICIS2016), pp.415-420, 2016.6.27-29D
  18. Rinko Komiya, Takeshi Saitoh, Miharu Fuyuno, Yuko Yamashita, and Yoshitaka Nakajima. Head Pose Estimation and Movement Analysis for Speech Scene. 15th IEEE/ACIS International Conference on Computer and Information Science (ICIS2016), pp.409-413, 2016.6.27-29D
  19. Warapon Chinsatit, Masataka Shibuya, Kenji Kawada, and Takeshi Saitoh. Character Input System using Gaze Estimation. 2nd International Conference on Communication Systems and Computing Application Science (CSCAS2016), #21, 2016.3.19-20D
  20. Miharu Fuyuno, Yuko Yamashita, Takeshi Saitoh, and Yoshitaka Nakajima. Semantic Structure, Spech Units and Facial Movements: Multimodal Corpus Analysis of English Public Speaking. 8th International Conference on Corpus Linguistics (CILC2016), #100, p.19, 2016.3D
    EPiC Series in Language and Linguistics, Vol.1, pp.447-461, 2016.11. Easychair
  21. Ryoji Fukuda, Yuki Ogino, Kenji Kawada, and Takeshi Saitoh. Area Type Judgment of Mathematical Document Using Movements of Gaze Point. 20th Asian Technology Conference in Mathematics, pp.346-353, 2014.11D
  22. Takeshi Saitoh, Toshiki Shibata, and Tsubasa Miyazono. Image-based fish recognition. 7th International Conference on Soft Computing and Pattern Recognition (SoCPaR2015), pp.260-263, 2015.11D
  23. Takeshi Saitoh, Toshihiro Iwata, and Kentaro Wakisaka. OKIRAKU Search: Leaf Images based Visual Tree Search System. IAPR International Conference on Machine Vision Applications (MVA2015), 10-2, pp.242-245, 2015.5D
  24. Jun Shiraishi and Takeshi Saitoh. Optical Flow based Lip Reading using Non Rectangular ROI and Head Motion Reduction. 11th IEEE International Conference on Automatic Face and Gesture Recognition (FG2015), 2015.5D
  25. Ryoji Fukuda, Junki Iwagami, and Takeshi Saitoh. Applicability of Gaze Points for Analyzing Priorities of Explanatory Elements in Mathematical Documents. 19th Asian Technology Conference in Mathematics, pp.203-210, 2014.11D
  26. Junki Iwagami and Takeshi Saitoh. Easy Calibration for Gaze Estimation using Inside-Out Camera. 20th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2014), 57, pp.292-297C2014.2D
  27. Yuki Takasaki, Takeshi Saitoh and Tomoki Koyama. Eigenlips using RGB-D Cameras for Lip Reading. 20th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2014), 34, pp.180-183C2014.2D
  28. Junki Iwagami and Takeshi Saitoh. Gaze Estimation using Inside-Out Camera for First Person Vision. 45th ISCIE International Symposium on Stochastic Systems Theory and Its Applications, FB3-2, pp.75-76C2013.11D
  29. Hiroki Nishino and Takeshi Saitoh. Automatic Moving Person Detection in Lecture Scene. 45th ISCIE International Symposium on Stochastic Systems Theory and Its Applications, FB1-3, pp.15-16C2013.11D
  30. Toshiriro Iwata and Takeshi Saitoh. Tree Recognition based on Leaf Images. Proc. of SICE Annual Conference, pp.2489-2494C2013.9D
  31. Trung Hieu Bui, Takeshi Saitoh and Eitaku Nobuyama. Road Area Detection Based on Texture Orientations Estimation and Vanishing Point Detection. Proc. of SICE Annual Conference, pp.1138-1143C2013.9D
  32. Takeshi Saitoh. Efficient Face Model for Lip Reading. 12th International Conference on Auditory-Visual Speech Processing (AVSP2013)Cpp.227-232C2013.8D
  33. Takeshi Saitoh, Masanori Imamura and Yukimi Fukui. Development of Initial Inspection System for Wooden Chopsticks by Image Processing. Proc. of 11th International Conference on Quality Control by Artificial Vision (QCAV2013), pp.75-80C2013.5D
  34. Trung Hieu Bui, Eitaku Nobuyama and Takeshi Saitoh. Road Detection Based on an Estimated Vanishing Point and Color Information. Proc. of International Conference on Computer Vision, Image and Signal ProcessingiICCVISP2012j, World Academy of Science, Engineering and TechnologyCIssue 71, pp.1748-1753C2012.11D
  35. Takeshi Saitoh and Toshihiro Iwata. Leaf Recognition using Shape and Color Features. Proc. of International Conference on Computer Vision, Image and Signal ProcessingiICCVISP2012j, World Academy of Science, Engineering and TechnologyCIssue 71, pp.1734-1739C2012.11D
  36. Takeshi Saitoh. Real-time Lip Reading System for Fixed Phrase and Its Combination. 1st Asian Conference on Pattern Recognition (ACPR2011)Cpp.461-464C2011.11D
  37. Takeshi Saitoh. Development of Communication Support System using Lip Reading. 10th International Conference on Auditory-Visual Speech Processing (AVSP2011)Cpp.117-122C2011.9D
  38. Takeshi Saitoh. Development of Inspection System for Wooden Chopsticks. IAPR Conference on Machine Vision Applications (MVA 2011)C9-5Cpp.247-250C2011.6D
  39. Takeshi Saitoh and Ryosuke Konishi. A study of influence of word lip reading by change of frame rate. 9th International Conference on Auditory-Visual Speech Processing (AVSP2010)Cpp.131-136C2010.10D
  40. Takeshi Saitoh and Ryosuke Konishi. Profile Lip Reading for Vowel and Word Recognition. 20th International Conference on Pattern Recognition (ICPR2010)Cpp.1356-1359C2010.8D
  41. Takeshi SaitohCHiroyuki Ishikura and Ryosuke Konishi. Word Lip Reading in Various Tones. 16th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2010)CO6-4Cpp.304-308C2010.2D
  42. Naoki TsudaCShuji HarimotoCTakeshi Saitoh and Ryosuke Konishi. Mobile Robot with Following and Returning Mode. 18th IEEE International Symposium on Robot and Human Interactive Communication (IEEE RO-MAN2009)CThB1.3Cpp.933-938C2009.9D
  43. Akira MuraiCMasaharu MizuguchiCTakeshi SaitohCTomoyuki Osaki and Ryosuke Konishi. Elevator Available Voice Activated Wheelchair. 18th IEEE International Symposium on Robot and Human Interactive Communication (IEEE RO-MAN2009)CWeC1.2Cpp.730-735C2009.9D
  44. Akira MuraiCMasaharu MizuguchiCMasato NishimoriCTakeshi SaitohCTomoyuki Osaki and Ryosuke Konishi. Voice Activated Wheelchair with Collision Avoidance Using Sensor Information. ICROS-SICE International Joint Conference 2009 (ICCAS-SICE09)C4A08-1Cpp.4232-4237C2009.8D
  45. Kohei MaruyamaCTakeshi Saitoh and Ryosuke Konishi. Bird Tracking and Flapping Motion Recognition for Monitoring System. ICROS-SICE International Joint Conference 2009 (ICCAS-SICE09)C3B17-1Cpp.3613-3618C2009.8D
  46. Naoki TsudaCShuji HarimotoCTakeshi Saitoh and Ryosuke Konishi. Mobile Robot with Following Function and Autonomous Return Function. ICROS-SICE International Joint Conference 2009 (ICCAS-SICE09)C1B09-3Cpp.635-640C2009.8D
  47. Takeshi SaitohCTomoya Kato and Ryosuke Konishi. A Novel Transducer: From Lip Motion To Voice Message. IAPR Conference on Machine Vision Applications (MVA 2009)C13-7Cpp.410-413C2009.5D
  48. Takeshi SaitohCKazutoshi MorishitaCand Ryosuke Konishi. Analysis of Efficient Lip Reading Method for Various Languages. 19th International Conference on Pattern Recognition (ICPR2008)CMoBT9.10CTampa-Florida-USAC2008.12D
  49. Takeshi SaitohCYuuki AotaCTomoyuki OsakiCRyosuke Konishi and Kazunori Sugahara. Current Sensor based Non-intrusive Appliance Recognition for Intelligent Outlet. The 23rd International Technical Conference on Circuits/Systems, Computers and CommunicationsiITC-CSCC2008jCG1-5Cpp.349-351CShimonoseki-JapanC2008.7D
  50. Naoya TadaCKeisuke MurataCTakeshi SaitohCTomoyuki Osaki and Ryosuke Konishi. Monocular Vision based Indoor Mobile Robot. The 23rd International Technical Conference on Circuits/Systems, Computers and CommunicationsiITC-CSCC2008jCG1-5Cpp.41-44CShimonoseki-JapanC2008.7D
  51. Takeshi SaitohCNoriyuki Takahashi and Ryosuke Konishi. Oral Motion Controlled Intelligent Wheelchair. SICE Annual Conference 2007CInternational Conference on Instrumentation, Control and Information TechnologyC1A16-5Cpp.341-346CTakamatsu-JapanC2007.9D
  52. Masato NishimoriCTakeshi Saitoh and Ryosuke Konishi. Voice Controlled Intelligent Wheelchair. SICE Annual Conference 2007CInternational Conference on Instrumentation, Control and Information TechnologyC1A16-4Cpp.336-340CTakamatsu-JapanC2007.9D
  53. Naoya TadaCTakeshi Saitoh and Ryosuke Konishi. Mobile Robot Navigation by Center Following using Monocular Vision. SICE Annual Conference 2007CInternational Conference on Instrumentation, Control and Information TechnologyC1A16-3Cpp.331-335CTakamatsu-JapanC2007.9D
  54. Takeshi SaitohCNoriyuki Takahashi and Ryosuke Konishi. Development of an Intelligent Wheelchair with Visual Oral Motion. 16th IEEE International Symposium on Robot and Human Interactive Communication (IEEE RO-MAN2007)CMP-07Cpp.145-150CJeju Island-KoreaC2007.8D
  55. Takeshi SaitohCMitsugu Hisagi and Ryosuke Konishi. Japanese Phone Recognition using Lip Image Information. IAPR Conference on Machine Vision Applications (MVA 2007)C3-27Cpp.134-137CTokyo-JapanC2007.5D
  56. Naoya OshimaCTakeshi Saitoh and Ryosuke Konishi. Automatic Moving Object Detection and Tracking with Mean Shift for Surveillance System. 2006 IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2006)Cpp.578-581CYonago-JapanC2006.12D
  57. Takeshi Saitoh and Ryosuke Konishi. Word Recognition based on Two Dimensional Lip Motion Trajectory. 2006 IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2006)Cpp.287-290CYonago-JapanC2006.12D
  58. Mitsugu HisagiCTakeshi Saitoh and Ryosuke Konishi. Analysis of Efficient Feature for Japanese Vowel Recognition. 2006 IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2006)Cpp.33-36CYonago-JapanC2006.12D
  59. Takeshi SaitohCTomoyuki Osaki and Ryosuke Konishi. Monocular Autonomy Following Vehicle. SICE-ICASE International Joint Conference 2006 (SICE-ICCAS2006)Cpp.5963-5966CBusan-KoreaC2006.10D
  60. Takeshi Saitoh and Ryosuke Konishi. Lip Reading using Video and Thermal Images. SICE-ICASE International Joint Conference 2006 (SICE-ICCAS2006)Cpp.5011-5015CBusan-KoreaC2006.10D
  61. Naoya OoshimaCTakeshi Saitoh and Ryosuke Konishi. Real Time Mean Shift Tracking using Optical Flow Distribution. SICE-ICASE International Joint Conference 2006 (SICE-ICCAS2006)Cpp.4316-4320CBusan-KoreaC2006.10D
  62. Takeshi SaitohCTetsuya Kodani and Ryosuke Konishi. A Real-time Footstep Tracking for Monitoring System. IASTED International Conference on Signal and Image Processing (SIP2006)C534-058Cpp.403-408CHawaii-USAC2006.8D
  63. Takeshi Saitoh and Ryosuke Konishi. Lip Reading based on Sampled Active Contour Model. International Conference on Image Analysis and Recognition (ICIAR2005)CLecture Notes in Computer Science(LNCS) 3656Cpp.507-515CToronto-CanadaC2005.9D
  64. Takeshi SaitohCYasuhito Fukui and Ryosuke Konishi. Lip Detection and Visual Speech Recognition. SICE Annual Conference 2005CWA2-06-2Cpp.2951-2954COkayama-JapanC2005.8D
  65. Naoya OoshimaCTakeshi Saitoh and Ryosuke Konishi. Real-Time Invader Tracking System with Surveillance Camera. SICE Annual Conference 2005CWP1-06-3Cpp.3364-3367COkayama-JapanC2005.8D
  66. Takeshi SaitohCNaoya Ooshima and Ryosuke Konishi. Automatic Speaker Detection based on Voice and Image Processing. SICE Annual Conference 2005CWP1-06-4Cpp.3368-3371COkayama-JapanC2005.8D
  67. Takeshi SaitohCKimiya AokiCand Toyohisa Kaneko. Automatic Recognition of Blooming Flowers. 17th International Conference on Pattern Recognition (ICPR2004)CVol.1Cpp.27-30CCambridge-United KingdomC2004.8D
  68. Takeshi SaitohCMasatoshi IkedaCKimiya AokiCand Toyohisa Kaneko. Optimal Threshold for Hepatic Blood Vessels and Cancer Detection. 5th International Conference on Advances in Pattern Recognition (ICAPR2003)Cpp.319-322CKolkata-IndiaC2003.12D
  69. Takeshi SaitohCKimiya AokiCand Toyohisa Kaneko. Automatic Extraction of Object Region from Photographs. 13th Scandinavian Conference on Image Analysis (SCIA2003)CLecture Notes in Computer Science(LNCS) 2749Cpp.1130-1137CGoreborg-SwedenC2003.7D
  70. Takeshi SaitohCManabu SatoCToyohisa KanekoCand Shigeru Kuriyama. Transient Texture Synthesis Based on Multiple Templates. 2nd International Conference on Image and Signal Processing (ICISP2003)CVol.2Cpp.489-496CAgadir-MoroccoC2003.6D
  71. Takeshi SaitohCYuta TamuraCand Toyohisa Kaneko. Automatic Segmentation of Liver Region through Blood Vessels on Multi-Phase CT. 16th International Conference on Pattern Recognition (ICPR2002)CVol.1Cpp.735-738CQuebec-CanadaC2002.8D
  72. Takeshi SaitohCand Toyohisa Kaneko. Automatic Recognition of Wild Flowers. 15th International Conference on Pattern Recognition (ICPR2000)CVol.2Cpp.507-510CBarcelona-SpainC2000.9D

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    • Šw¶§—ãÜ ŽóÜ
  15. Ž™‹Ê ’m–çCꎓ¡ „ŽjDÄ‹AŒ^ƒjƒ…[ƒ‰ƒ‹ƒlƒbƒgƒ[ƒN‚ð—p‚¢‚½ƒ}ƒ‹ƒ`ƒ‚[ƒ_ƒ‹Žè˜b”FŽ¯D‘æ20‰ñ@‰æ‘œ‚Ì”FŽ¯E—‰ðƒVƒ“ƒ|ƒWƒEƒ€iMIRU2017jCPS3-53CL“‡‘Û‰ï‹cêiL“‡jC2017.8D
  16. —› åCCꎓ¡„ŽjDConvolutional Neural Network‚ð—p‚¢‚½“ª•”Žp¨•ª—ށD“®“I‰æ‘œˆ—ŽÀ—˜—p‰»ƒ[ƒNƒVƒ‡ƒbƒv2017iDIA2017jCIS2-3Cpp.235-239C‚­‚É‚Ñ‚«ƒƒbƒZi“‡ªŒ§¼]ŽsjC2017.3D
  17. ¬‹{ ꣎qCƒƒ‰ƒ|ƒ“ ƒ`ƒ“ƒTƒeƒBCꎓ¡„ŽjC“~–ì”ü°DƒXƒs[ƒ`Žw“±‚Ì‚½‚߂̉f‘œî•ñ‚ð—p‚¢‚½˜bŽÒ‚ÌŠç‰ðÍD“®“I‰æ‘œˆ—ŽÀ—˜—p‰»ƒ[ƒNƒVƒ‡ƒbƒv2017iDIA2017jCOS3-4Cpp.195-200C‚­‚É‚Ñ‚«ƒƒbƒZi“‡ªŒ§¼]ŽsjC2017.3D
  18. ‹{‰€ —ƒCꎓ¡ „ŽjDCNN‚ð—p‚¢‚½“Á’¥“_•t‰æ‘œ‚É‚æ‚é‹›ŽíŽ¯•ÊD‰Î‚̍‘î•ñƒVƒ“ƒ|ƒWƒEƒ€2017CA3-4CŽ­Ž™“‡‘åŠwiŽ­Ž™“‡Œ§Ž­Ž™“‡ŽsjC2017.3.1-2D
  19. ‹ß“¡ “⍹CWarapon ChinsatitCꎓ¡ „ŽjDCNN‚ð—p‚¢‚½–ډ摜‚©‚ç‚Ì“µEŒŸo‚ÉŠÖ‚·‚錤‹†D‰Î‚̍‘î•ñƒVƒ“ƒ|ƒWƒEƒ€2017CA3-2CŽ­Ž™“‡‘åŠwiŽ­Ž™“‡Œ§Ž­Ž™“‡ŽsjC2017.3.1-2D
  20. ŒEì ”ü’qŽqCꎓ¡ „ŽjD“ǐOŒü‚¯ŒöŠJƒf[ƒ^ƒx[ƒX‚̏ЉîD‘æ6‰ñƒoƒCƒIƒƒgƒŠƒNƒX‚Æ”FŽ¯E”FØƒVƒ“ƒ|ƒWƒEƒ€iSBRA2016jCS2-20Cpp.48-49CŽÅ‰YH‹Æ‘åŠwi“Œ‹ž“s]“Œ‹æjC2016.11.16-17D
  21. ꎓ¡ „ŽjDƒtƒŒ[ƒ€˜AŒ‹‰æ‘œ‚ð—p‚¢‚½CNN‚É‚æ‚é“ǐOD‘æ6‰ñƒoƒCƒIƒƒgƒŠƒNƒX‚Æ”FŽ¯E”FØƒVƒ“ƒ|ƒWƒEƒ€iSBRA2016jCS2-13Cpp.35-36CŽÅ‰YH‹Æ‘åŠwi“Œ‹ž“s]“Œ‹æjC2016.11.16-17D
  22. ‹´‘º ‰À—SCꎓ¡ „ŽjD‹——£‰æ‘œ‚̃tƒŒ[ƒ€˜AŒ‹‰æ‘œ‚ð—p‚¢‚½Convolutional Neural Network‚É‚æ‚éŽè˜b’PŒê”FŽ¯D“dŽqî•ñ’ʐMŠw‰ï@•ŸŽƒHŠwŒ¤‹†‰ïCvol.116Cno.248CWIT2016-36Cpp.17-22C“‚’̓Cƒ„ƒ‹ƒzƒeƒ‹i²‰êŒ§“‚’ÃŽsjC2016.10D
  23. ꎓ¡ „ŽjDƒtƒŒ[ƒ€˜AŒ‹‰æ‘œ‚ð—p‚¢‚½CNN‚É‚æ‚é“ǐOD‘æ3‰ñƒTƒCƒŒƒ“ƒg‰¹º”FŽ¯ƒ[ƒNƒVƒ‡ƒbƒvC12Dp.6D•Ÿ‰ª’©“úƒrƒ‹i•Ÿ‰ªŒ§•Ÿ‰ªŽsjD2016.10D
  24. ‹´‘º ‰À—SCꎓ¡ „ŽjD‹——£‰æ‘œ‚̃tƒŒ[ƒ€˜AŒ‹‰æ‘œ‚ð—p‚¢‚½CNN‚É‚æ‚éŽè˜b’PŒê”FŽ¯D‘æ3‰ñƒTƒCƒŒƒ“ƒg‰¹º”FŽ¯ƒ[ƒNƒVƒ‡ƒbƒvC11Dp.5D•Ÿ‰ª’©“úƒrƒ‹i•Ÿ‰ªŒ§•Ÿ‰ªŽsjD2016.10D
  25. ŒEì ”ü’qŽqCꎓ¡ „ŽjDŒûŒ`ƒpƒ^[ƒ“‚ƕꉹ•À‚ÑŒê‚ð—˜—p‚µ‚½“ú–{ŒêƒeƒLƒXƒg“ü—̓VƒXƒeƒ€D•½¬28”N“x“d‹CEî•ñŠÖŒWŠw‰ï‹ãBŽx•”˜A‡‘å‰ï i‘æ69‰ñ˜A‡‘å‰ïjC11-2P-06Cp.437C2016.9D
    • î•ñˆ—Šw‰ï‹ãBŽx•”§—ãÜ ŽóÜ
  26. ‹ß“¡ “⍹CWarapon ChinsatitCꎓ¡ „ŽjDƒEƒFƒAƒ‰ƒuƒ‹ƒJƒƒ‰‚ð—p‚¢‚½Ž‹ü“ü—̓VƒXƒeƒ€‚Ì—LŒø«‚ÌŒŸ“¢D•½¬28”N“x“d‹CEî•ñŠÖŒWŠw‰ï‹ãBŽx•”˜A‡‘å‰ï i‘æ69‰ñ˜A‡‘å‰ïjC11-2P-05Cp.436C2016.9D
  27. ¬‹{ ꣎qCWarapon ChinsatitCꎓ¡ „ŽjDƒAƒCƒRƒ“ƒ^ƒNƒg‰ðÍ‚Ì‚½‚ß‚ÌCNN‚ð—p‚¢‚½–ÚŒŸoD•½¬28”N“x“d‹CEî•ñŠÖŒWŠw‰ï‹ãBŽx•”˜A‡‘å‰ï i‘æ69‰ñ˜A‡‘å‰ïjC08-2P-08Cp.419C2016.9D
  28. Warapon Chinsatit, Takeshi SaitohDPupil Center Estimation by using Deep Convolutional Neural NetworkD‘æ19‰ñ@‰æ‘œ‚Ì”FŽ¯E—‰ðƒVƒ“ƒ|ƒWƒEƒ€iMIRU2016jCPS1-50C ƒAƒNƒgƒVƒeƒB•l¼i•l¼jC2016.8D
  29. ꎓ¡ „ŽjCZiheng ZhouCIryna AninaCGuoying ZhaoCMatti PietikainenDƒtƒŒ[ƒ€˜AŒ‹‰æ‘œ‚ð—p‚¢‚½CNN‚É‚æ‚éƒV[ƒ“”FŽ¯D‘æ19‰ñ@‰æ‘œ‚Ì”FŽ¯E—‰ðƒVƒ“ƒ|ƒWƒEƒ€iMIRU2016jCPS1-27C ƒAƒNƒgƒVƒeƒB•l¼i•l¼jC2016.8D
  30. ¬‹{ ꣎qCꎓ¡ „ŽjC“~–ì ”ü°CŽR‰º —FŽqC’†“‡ ËDDƒXƒs[ƒ`Žw“±‚Ì‚½‚ß‚ÌŠç“Á’¥“_‚ð—p‚¢‚½˜bŽÒ“ª•”‚Ì“®‚«‰ðÍD“dŽqî•ñ’ʐMŠw‰ï‘‡‘å‰ïCD-15-32Cp.211C‹ãB‘åŠwi•Ÿ‰ªjC2016.3D
  31. Chinsatit Warapon, Takeshi SaitohDEye Detection by using Gradient Value for Performance Improvement of Wearable Gaze Estimation SystemD“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯‚ƃƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.115Cno.456CPRMU2015-163CCNR2015-64Cpp.149-154C‹ãBH‹Æ‘åŠwi•Ÿ‰ªjC2016.2D
  32. —S@ ‚“¿Ca’J ¹®Cì“c Œ’ŽiCꎓ¡ „ŽjDŽè˜bƒV[ƒ“ŠÏŽ@Žž‚Ì’Ž‹î•ñ•ªÍD“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯‚ƃƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.115Cno.456CPRMU2015-158CCNR2015-59Cpp.129-130C‹ãBH‹Æ‘åŠwi•Ÿ‰ªjC2016.2D
  33. Œ´ Œ«‘¾Cꎓ¡ „ŽjDConstrained Local Model‚ð—p‚¢‚½Šç“Á’¥“_ŒŸo‚ÉŠÖ‚·‚錟“¢D“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯‚ƃƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.115Cno.456CPRMU2015-154CCNR2015-55Cpp.121-122C‹ãBH‹Æ‘åŠwi•Ÿ‰ªjC2016.2D
  34. ˜eâ Œ’‘¾˜YCꎓ¡ „ŽjDConvolutional Neural Network‚ð—p‚¢‚½‹›‰æ‘œ”FŽ¯D“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯‚ƃƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.115Cno.456CPRMU2015-132CCNR2015-33Cpp.1-5C‹ãBH‹Æ‘åŠwi•Ÿ‰ªjC2016.2D
  35. a’J ¹®Cì“c Œ’ŽiCꎓ¡ „ŽjD’Ž‹“_„’è‹Zp‚ð—˜—p‚µ‚½•¶Žš“ü—̓VƒXƒeƒ€‚ÌŠJ”­DŒv‘ªŽ©“®§ŒäŠw‰ï‘æ34‰ñ‹ãBŽx•”Šwpu‰‰‰ïC204B3Cpp.179-182D•Ÿ‰ªH‹Æ‘åŠwi•Ÿ‰ªjD2015.11D
  36. ”яÀ áÁ‹ICꎓ¡ „ŽjD•ê‰¹ŒûŒ`‚ð—˜—p‚µ‚½ƒeƒLƒXƒg“ü—̓VƒXƒeƒ€D‘æ2‰ñƒTƒCƒŒƒ“ƒg‰¹º”FŽ¯ƒ[ƒNƒVƒ‡ƒbƒvC4Dp.2D_ŒË‘åŠwi•ºŒÉŒ§_ŒËŽsjD2015.10D
  37. ¬‹{ ꣎qCꎓ¡ „ŽjD“ǐO‚Å—˜—p‰Â”\‚ÈŒöŠJƒf[ƒ^ƒx[ƒX‚̏ЉîD‘æ2‰ñƒTƒCƒŒƒ“ƒg‰¹º”FŽ¯ƒ[ƒNƒVƒ‡ƒbƒvC1Dp.1D_ŒË‘åŠwi•ºŒÉŒ§_ŒËŽsjD2015.10D
  38. ‹´‘º ‰À—SCꎓ¡ „ŽjDLight-HMM‚ð—p‚¢‚½Žè˜b”FŽ¯D•½¬27”N“x“d‹CEî•ñŠÖŒWŠw‰ï‹ãBŽx•”˜A‡‘å‰ï i‘æ68‰ñ˜A‡‘å‰ïjC11-2P-06Cp.511C2015.9D
  39. ‹{‰€ —ƒCꎓ¡ „ŽjD‹›‚̉摜”FŽ¯‚ÉŠÖ‚·‚錤‹†D•½¬27”N“x“d‹CEî•ñŠÖŒWŠw‰ï‹ãBŽx•”˜A‡‘å‰ï i‘æ68‰ñ˜A‡‘å‰ïjC11-2A-04Cp.352C2015.9D
  40. Œ´ Œ«‘¾Cꎓ¡ „ŽjDConstrained Local Model‚ð—p‚¢‚½“Á’¥“_ŒŸoD•½¬27”N“x“d‹CEî•ñŠÖŒWŠw‰ï‹ãBŽx•”˜A‡‘å‰ï i‘æ68‰ñ˜A‡‘å‰ïjC11-2A-03Cp.351C2015.9D
  41. ì“c Œ’ŽiCꎓ¡ „ŽjDInside-Out ƒJƒƒ‰‚ð—p‚¢‚½’PŠá‚É‚æ‚钍Ž‹“_„’èŽè–@‚̐«”\•]‰¿D‘æ14‰ñî•ñ‰ÈŠw‹ZpƒtƒH[ƒ‰ƒ€iFIT2015jCI-040Cpp.301-302Cˆ¤•Q‘åŠwiˆ¤•QjC2015.9D
  42. Œ´ Œ«‘¾CŠâè «–çCꎓ¡ „ŽjCZiheng ZhouCIryna AninaCGuoying ZhaoCMatti PietikainenD‘½Ž‹“_Audio-Visualƒf[ƒ^ƒx[ƒXOuluVS2D‘æ18‰ñ@‰æ‘œ‚Ì”FŽ¯E—‰ðƒVƒ“ƒ|ƒWƒEƒ€iMIRU2015jCSS4-46C ƒzƒeƒ‹ã‹}ƒGƒLƒXƒ|ƒp[ƒNi‘åãjC2015.7D
  43. ¬–ì ‹±—TC–{“c “§Cꎓ¡ „ŽjDŠá‹¾Œ^ƒJƒƒ‰‚ð—p‚¢‚½ƒRƒ~ƒ…ƒjƒP[ƒVƒ‡ƒ“Žx‰‡‹@Ší‚̏d“x“ª•”EÒ‘ŠOŠ³ŽÒ‚ւ̉ž—pD‘æ35‰ñ“ú–{ƒŠƒnƒrƒŠƒe[ƒVƒ‡ƒ“ˆãŠw‰ï ’†‘EŽl‘’n•û‰ïC‚’m‘åŠwi‚’mjC2015.6D
  44. ”яÀ áÁ‹ICꎓ¡ „ŽjDŒûŒ`ƒpƒ^[ƒ“‚ƕꉹ•À‚ÑŒê‚ð—˜—p‚µ‚½ƒeƒLƒXƒg“ü—̓VƒXƒeƒ€D“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯‚ƃƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.114Cno.408CPRMU2015-55CSP2015-24CWIT2015-24Cpp.143-148CVŠƒ‘åŠwiVŠƒjC2015.6D
  45. ¼–ì ”Ž‹MCꎓ¡ „ŽjD“Á’¥“_ƒx[ƒX‚É‚æ‚郂ƒfƒ‹ƒtƒŠ[‚Ì•¨‘Ì“®üŒŸoD“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯‚ƃƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.114Cno.408CPRMU2015-55CSP2015-24CWIT2015-24Cpp.137-142CVŠƒ‘åŠwiVŠƒjC2015.6D
  46. ŽÄ“c —˜Ž÷Cꎓ¡ „ŽjD‹›‰æ‘œ”FŽ¯‚É—LŒø‚È“Á’¥—Ê‚ÌŒŸ“¢Dî•ñˆ—Šw‰ï@ƒRƒ“ƒsƒ…[ƒ^ƒrƒWƒ‡ƒ“‚ƃCƒ[ƒWƒƒfƒBƒAŒ¤‹†‰ïCvol.2015-CVIM-197Cno.26C“ú–{‰ÈŠw–¢—ˆŠÙi“Œ‹žjC2015.5D
  47. ŽÄ“c —˜Ž÷CìŒû 仓ށCꎓ¡ „ŽjD‰æ‘œˆ—‹Zp‚ð—p‚¢‚½‹›ŽíŒŸõƒVƒXƒeƒ€‚ÌŠJ”­D“®“I‰æ‘œˆ—ŽÀ—˜—p‰»ƒ[ƒNƒVƒ‡ƒbƒv2015iDIA2015jCIS1-B4CL“‡H‹Æ‘åŠwiL“‡jC2015.3D
  48. ¬ŽR ’qŒÈCꎓ¡ „ŽjDKinect‚ð—p‚¢‚½Žè˜b’PŒê”FŽ¯‚É—LŒø‚È“Á’¥—Ê‚ÌŒŸ“¢D“dŽqî•ñ’ʐMŠw‰ï@ME‚ƃoƒCƒIƒTƒCƒoƒlƒeƒBƒbƒNƒXŒ¤‹†‰ïCvol.114Cno.408CMBE2014-115Cpp.117-120CŒF–{‘åŠwiŒF–{jC2015.1D

  49. ŽÄ“c —˜Ž÷Cꎓ¡ „ŽjD‰æ‘œˆ—‹Zp‚ð—p‚¢‚½‹›ŽíŽ¯•Ê‚É—LŒø‚È“Á’¥—Ê‚ÌŒŸ“¢DŒv‘ªŽ©“®§ŒäŠw‰ï‘æ33‰ñ‹ãBŽx•”Šwpu‰‰‰ïC203A1Cpp.157-160D‹ãBH‹Æ‘åŠwi•Ÿ‰ªjD2014.12D
  50. ”’Î ~Cꎓ¡ „ŽjDƒIƒvƒeƒBƒJƒ‹ƒtƒ[‚ð—p‚¢‚½“ǐO‚É—LŒø‚È’–ڗ̈æ‚ÌŒŸ“¢DŒv‘ªŽ©“®§ŒäŠw‰ï‘æ33‰ñ‹ãBŽx•”Šwpu‰‰‰ïC203A2Cpp.161-164D‹ãBH‹Æ‘åŠwi•Ÿ‰ªjD2014.12D
  51. ”’Î ~Cꎓ¡ „ŽjDƒIƒvƒeƒBƒJƒ‹ƒtƒ[‚ð—p‚¢‚½“ǐO‚ÉŠÖ‚·‚錤‹†D•½¬26”N“x“d‹CEî•ñŠÖŒWŠw‰ï‹ãBŽx•”˜A‡‘å‰ïC10-2A-04Dp.375DŽ­Ž™“‡‘åŠwiŽ­Ž™“‡jD2014.9D
  52. ì“c Œ’ŽiCꎓ¡ „ŽjDƒOƒ‰ƒXŒ^Inside-Out ƒJƒƒ‰‚ð—p‚¢‚½’Ž‹“_„’èD•½¬26”N“x“d‹CEî•ñŠÖŒWŠw‰ï‹ãBŽx•”˜A‡‘å‰ïC06-2A-12Dp.333DŽ­Ž™“‡‘åŠwiŽ­Ž™“‡jD2014.9D
    • •½¬26”N“x˜A‡‘å‰ïu‰‰§—ãÜ ŽóÜ
  53. ”’Î ~Cꎓ¡ „ŽjDƒIƒvƒeƒBƒJƒ‹ƒtƒ[‚ð—p‚¢‚½“ǐO‚ÉŠÖ‚·‚錤‹†D‘æ1‰ñƒTƒCƒŒƒ“ƒg‰¹º”FŽ¯ƒ[ƒNƒVƒ‡ƒbƒvC1Dp.1D‹ãBH‹Æ‘åŠwi•Ÿ‰ªjD2014.8D
  54. ꎓ¡ „ŽjC”’Î ~Dƒ}ƒ‹ƒ`ƒXƒgƒŠ[ƒ€“ǐOD‘æ17‰ñ@‰æ‘œ‚Ì”FŽ¯E—‰ðƒVƒ“ƒ|ƒWƒEƒ€iMIRU2014jCSS2-54C‰ªŽRƒRƒ“ƒxƒ“ƒVƒ‡ƒ“ƒZƒ“ƒ^[i‰ªŽRjC2014.7D
  55. ꎓ¡ „ŽjCŠâ“c ‘‘åC˜eâ Œ’‘¾˜YD‚¨–ØŠyƒT[ƒ`:—t‰æ‘œ‚ð—p‚¢‚½Ž÷–ØŒŸõƒVƒXƒeƒ€D“®“I‰æ‘œˆ—ŽÀ—˜—p‰»ƒ[ƒNƒVƒ‡ƒbƒv2014iDIA2014jCIS2-3Cpp.71-74CŒF–{‘åŠwiŒF–{jC2014.3D
  56. ꎓ¡ „ŽjD‰æ‘œˆ—‚É‚æ‚銄‚蔢Œ´Œ`‚ÌŠOŠÏŒŸ¸ƒVƒXƒeƒ€‚ÌŠJ”­Dî•ñˆ—Šw‰ï@ƒRƒ“ƒsƒ…[ƒ^ƒrƒWƒ‡ƒ“‚ƃCƒ[ƒWƒƒfƒBƒAŒ¤‹†‰ïCvol.2014-CVIM-191Cno.15C“Œ‹ž‘åŠwi“Œ‹žjC2014.3D
  57. ꎓ¡ „ŽjCŠâ“c ‘‘åD‰æ‘œˆ—‹Zp‚ð—p‚¢‚½Ž÷–Ø“¯’è‚ÉŠÖ‚·‚錤‹†Dî•ñˆ—Šw‰ï@ƒRƒ“ƒsƒ…[ƒ^ƒrƒWƒ‡ƒ“‚ƃCƒ[ƒWƒƒfƒBƒAŒ¤‹†‰ïCvol.2014-CVIM-191Cno.14C“Œ‹ž‘åŠwi“Œ‹žjC2014.3D
  58. ˆäã ‰õC¬ŽR ’qŒÈCꎓ¡ „ŽjD‹——£‰æ‘œ‚ð—p‚¢‚½’PˆêŽw•¶Žš”FŽ¯D“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯EƒƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.113Cno.431CPRMU2013-167CCNR2013-75Cpp.195-199C•Ÿ‰ª‘åŠwi•Ÿ‰ªjC2014.2D
  59. Šâã ƒ¶Cꎓ¡ „ŽjDŽwæƒLƒƒƒŠƒuƒŒ[ƒVƒ‡ƒ“‚ð—˜—p‚µ‚½’Ž‹“_„’èD“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯EƒƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.113Cno.431CPRMU2013-166CCNR2013-74Cpp.189-194C•Ÿ‰ª‘åŠwi•Ÿ‰ªjC2014.2D

  60. ”’Î ~Cꎓ¡ „ŽjD‚‘¬“xƒJƒƒ‰‚ð—p‚¢‚½‰ŒûŒ`”FŽ¯DŒv‘ªŽ©“®§ŒäŠw‰ï‘æ32‰ñ‹ãBŽx•”Šwpu‰‰‰ïC203A1Cpp.189-192C2013.11D
  61. ¬ŽR ’qŒÈCꎓ¡ „ŽjCŽðŒü TŽiC–k‘º ³DKinect‚ð—p‚¢‚½Žè˜b”FŽ¯DŒv‘ªŽ©“®§ŒäŠw‰ï‘æ32‰ñ‹ãBŽx•”Šwpu‰‰‰ïC103A5Cpp.159-162C2013.11D
  62. ¼–ì ”Ž‹MCꎓ¡ „ŽjDu‹`‰f‘œ‚É‚¨‚¯‚éˆÚ“®l•¨‚ÌŽ©“®ŒŸoD•½¬25”N“x“d‹CŠÖŒWŠw‰ï‹ãBŽx•”˜A‡‘å‰ïi‘æ66‰ñ˜A‡‘å‰ïjC02-2A-13Cp.323C2013.9D
  63. Šâã ƒ¶Cꎓ¡ „ŽjDFirst Person Vision‚Ì‚½‚ß‚ÌInside-OutƒJƒƒ‰‚É‚¨‚¯‚钍Ž‹“_„’èD•½¬25”N“x“d‹CŠÖŒWŠw‰ï‹ãBŽx•”˜A‡‘å‰ïi‘æ66‰ñ˜A‡‘å‰ïjC02-2A-12Cp.322C2013.9D
  64. ꎓ¡ „ŽjDŽ÷”ç‰æ‘œ‚É‚æ‚éŽ÷–Ø“¯’è‚ÌŒŸ“¢D•½¬25”N“x“d‹CŠÖŒWŠw‰ï‹ãBŽx•”˜A‡‘å‰ïi‘æ66‰ñ˜A‡‘å‰ïjC02-2A-11Cp.321C2013.9D
  65. ¼–ì ”Ž‹MCꎓ¡ „ŽjDu‹`‰f‘œ‚É‚¨‚¯‚éˆÚ“®•¨‘Ì“®ü‚ÌŽ©“®ŒŸoD‘æ12‰ñî•ñ‰ÈŠw‹ZpƒtƒH[ƒ‰ƒ€iFIT2013jCH-052Cpp.207-208C’¹Žæ‘åŠwi’¹ŽæjC2013.9D
  66. Šâ“c ‘‘åCꎓ¡ „ŽjD—t‚Ì•\— ‚ð—˜—p‚µ‚½Ž÷–؂̉摜”FŽ¯D‘æ12‰ñî•ñ‰ÈŠw‹ZpƒtƒH[ƒ‰ƒ€iFIT2013jCH-031Cpp.161-162C’¹Žæ‘åŠwi’¹ŽæjC2013.9D
  67. ꎓ¡ „ŽjCŠâ“c ‘‘åCŽÄ“c —˜Ž÷C˜eâ Œ’‘¾˜YD—t‰æ‘œ‚ð—p‚¢‚½Ž÷–ØŒŸõƒVƒXƒeƒ€D‘æ16‰ñ@‰æ‘œ‚Ì”FŽ¯E—‰ðƒVƒ“ƒ|ƒWƒEƒ€iMIRU2013jCSS6-16C‘—§î•ñŠwŒ¤‹†Ši“Œ‹žjC2013.8D
  68. ¼–ì ”Ž‹MCꎓ¡ „ŽjDu‹`‰f‘œ‚É‚¨‚¯‚éˆÚ“®•¨‘Ì‚ÌŽ©“®ŒŸoD“dŽqî•ñ’ʐMŠw‰ï@‹³ˆçHŠwŒ¤‹†‰ïCvol.113Cno.166CET2013-27Cpp.47-52CŒF–{‘åŠwiŒF–{jC2013.7D
  69. ˆäã ‰õCꎓ¡ „ŽjDKinect‚ð—˜—p‚µ‚½Žw•¶Žš”FŽ¯‚ÉŠÖ‚·‚錟“¢D“dŽqî•ñ’ʐMŠw‰ï@ME‚ƃoƒCƒIƒTƒCƒoƒlƒeƒBƒbƒNƒXŒ¤‹†‰ïCvol.112Cno.417CMBE2012-81Cpp.45-50C‹ãBH‹Æ‘åŠwi•Ÿ‰ªjC2013.1D

  70. Trung Hieu Bui, Eitaku Nobuyama and Takeshi SaitohDRoad Boundaries Extraction for General Road Images using Texture Orientation and Color InformationD‘æ31‰ñŒv‘ªŽ©“®§ŒäŠw‰ï‹ãBŽx•”Šwpu‰‰‰ïC103A6, pp.175-178C2012.12D
  71. ꎓ¡ „ŽjD‚‘¬“xƒJƒƒ‰‚ð—p‚¢‚½‰¹”FŽ¯DƒtƒH[ƒ‰ƒ€ŠçŠw2012CO2-4Cp.109C2012.10D
  72. Šâ“c ‘‘åCꎓ¡ „ŽjDŒ`ó‚ƐFî•ñ‚ÉŠî‚­—t‚̉摜”FŽ¯D•½¬24”N“x“d‹CŠÖŒWŠw‰ï‹ãBŽx•”˜A‡‘å‰ïi‘æ65‰ñ˜A‡‘å‰ïjC09-2P-18Cp.604C2012.9D
    • •½¬24”N“x˜A‡‘å‰ïu‰‰§—ãÜ ŽóÜ
  73. Šâ“c ‘‘åCꎓ¡ „ŽjD—t‰æ‘œ‚ð—p‚¢‚½Ž÷–Ø‚Ì”FŽ¯D“dŽqî•ñ’ʐMŠw‰ï@‰æ‘œHŠwŒ¤‹†‰ïCvol.112Cno.189CLOIS2012-28CIE2012-60CEMM2012-51Cpp.93-98CŽRŒû‘åŠwiŽRŒûjC2012.8D
  74. ˆäã ‰õCꎓ¡ „ŽjD‹——£‰æ‘œ‚ð—p‚¢‚½ƒŠƒAƒ‹ƒ^ƒCƒ€Žw•¶Žš”FŽ¯D“dŽqî•ñ’ʐMŠw‰ï@‰æ‘œHŠwŒ¤‹†‰ïCvol.112Cno.189CLOIS2012-27CIE2012-59CEMM2012-50Cpp.87-92CŽRŒû‘åŠwiŽRŒûjC2012.8D
  75. Šâã ƒ¶Cꎓ¡ „ŽjDSSRƒtƒBƒ‹ƒ^‚ð—p‚¢‚½–ÚŒŸo‹y‚яu‚«ŒŸoD“dŽqî•ñ’ʐMŠw‰ï@‰æ‘œHŠwŒ¤‹†‰ïCvol.112Cno.189CLOIS2012-26CIE2012-58CEMM2012-49Cpp.81-85CŽRŒû‘åŠwiŽRŒûjC2012.8D
  76. ꎓ¡ „ŽjCŠØ ƒŠƒƒƒ“D “ǐO‚É—LŒø‚Ȋ烂ƒfƒ‹‚ÌŠm—§ D‘æ15‰ñ@‰æ‘œ‚Ì”FŽ¯E—‰ðƒVƒ“ƒ|ƒWƒEƒ€iMIRU2012jCIS3-24C•Ÿ‰ª‘Û‰ï‹cêi•Ÿ‰ªjC2012.8D
  77. ꎓ¡ „ŽjCŠØ ƒŠƒƒƒ“D“ǐO‚É—LŒø‚Ȋ烂ƒfƒ‹‚ÌŒŸ“¢D“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯EƒƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.111Cno.499CPRMU2011-275CHIP2011-103Cpp.217-222C_ŒË‘åŠwi•ºŒÉjC2012.3D
  78. ‰Œû •¶‹MCꎓ¡ „ŽjC‹g—¯ Œ’Dƒjƒ…[ƒX‰f‘œ‚É‚¨‚¯‚éŠî–{ŒûŒ`‚Ì”FŽ¯D‘æ20‰ñŒv‘ªŽ©“®§ŒäŠw‰ï’†‘Žx•”Šwpu‰‰‰ïC421Cpp.194-195C‰ªŽR‘åŠwi‰ªŽRjC2011.11D
  79. “à“c Ž•FCꎓ¡ „ŽjC‹g—¯ Œ’D•¶Í”­˜bƒV[ƒ“‚É‚¨‚¯‚é’PŒêƒXƒ|ƒbƒeƒBƒ“ƒO”FŽ¯D‘æ20‰ñŒv‘ªŽ©“®§ŒäŠw‰ï’†‘Žx•”Šwpu‰‰‰ïC420Cpp.192-193C‰ªŽR‘åŠwi‰ªŽRjC2011.11D
  80. ‘O“c —SìC‹g“c _Ž¡C”öè ’mKCꎓ¡ „ŽjC‹g—¯ Œ’DƒŒ[ƒUƒŒƒ“ƒWƒtƒ@ƒCƒ“ƒ_‚ð—p‚¢‚½‰®“à’Ǐ]‹AŠÒŒ^ˆÚ“®ƒƒ{ƒbƒg‚ÌŠJ”­D‘æ20‰ñŒv‘ªŽ©“®§ŒäŠw‰ï’†‘Žx•”Šwpu‰‰‰ïC319Cpp.146-147C‰ªŽR‘åŠwi‰ªŽRjC2011.11D
  81. ‹g“c _Ž¡C‘O“c —SìC”öè ’mKCꎓ¡ „ŽjC‹g—¯ Œ’DƒŒ[ƒUƒŒƒ“ƒWƒtƒ@ƒCƒ“ƒ_‚ð—p‚¢‚½ SLAM ‚É‚æ‚鉹º‘€ìŒ^“d“®ŽÔ‚¢‚·‚Ì‘–s•â•D‘æ20‰ñŒv‘ªŽ©“®§ŒäŠw‰ï’†‘Žx•”Šwpu‰‰‰ïC307Cpp.122-123C‰ªŽR‘åŠwi‰ªŽRjC2011.11D
  82. –{“c “§Cꎓ¡ „Žj.ƒfƒWƒ^ƒ‹‰æ‘œˆ—‚É‚æ‚é“ǐO‹Zp‚ð—p‚¢‚½‰¹ºƒRƒ~ƒ…ƒjƒP[ƒVƒ‡ƒ“Žx‰‡ƒVƒXƒeƒ€D‘æ48‰ñ“ú–{ƒŠƒnƒrƒŠƒe[ƒVƒ‡ƒ“ˆãŠw‰ïŠwpW‰ïC1-6-19Cp.558C–‹’£ƒƒbƒZiç—tjC2011.11D
  83. ꎓ¡ „ŽjDƒŠƒAƒ‹ƒ^ƒCƒ€“ǐOƒVƒXƒeƒ€D•½¬23”N“x“d‹CŠÖŒWŠw‰ï‹ãBŽx•”˜A‡‘å‰ïi‘æ64‰ñ˜A‡‘å‰ïjC08-2P-01Cp.558C2011.9D
  84. ŠØ ƒŠƒƒƒ“Cꎓ¡ „ŽjDActive Appearance Model‚É‚¨‚¯‚é“ǐO‚É—LŒø‚Ȋ烂ƒfƒ‹‚ÌŒŸ“¢D•½¬23”N“x“d‹CŠÖŒWŠw‰ï‹ãBŽx•”˜A‡‘å‰ïi‘æ64‰ñ˜A‡‘å‰ïjC08-2P-07Cp.564C2011.9D
  85. ꎓ¡ „ŽjD”­˜báŠQŽÒ‚Ì‚½‚߂̓ǐO‹Zp‚ð—˜—p‚µ‚½ƒRƒ~ƒ…ƒjƒP[ƒVƒ‡ƒ“Žx‰‡ƒVƒXƒeƒ€Dƒqƒ…[ƒ}ƒ“ƒCƒ“ƒ^ƒtƒF[ƒXƒVƒ“ƒ|ƒWƒEƒ€2011C1421LCpp.323-328C2011.9D
  86. ꎓ¡ „ŽjD“ǐO‚Ì‚½‚߂̐üŒ`‰ñ‹A‚É‚æ‚鎋“_•ÏŠ·D‘æ14‰ñ@‰æ‘œ‚Ì”FŽ¯E—‰ðƒVƒ“ƒ|ƒWƒEƒ€iMIRU2011jCIS2-36Cpp.747-752C2011.7D
  87. ‹g“c _Ž¡Cꎓ¡ „ŽjC¬¼ —º‰îDƒŒ[ƒUƒŒƒ“ƒWƒtƒ@ƒCƒ“ƒ_‚ð—p‚¢‚½SLAM‚É‚æ‚é“d“®ŽÔ‚¢‚·‚Ì‘–s§ŒäD“dŽqî•ñ’ʐMŠw‰ï@•ŸŽƒî•ñHŠwŒ¤‹†‰ïCWIT2010-64Cpp.43-47C—§–½ŠÙ‘åŠwiŽ ‰êjC2011.1D

  88. ꎓ¡ „ŽjC•Ÿˆä K”üDŠ„‚蔢Œ´Œ`‚ÌŠOŠÏŒŸ¸ƒVƒXƒeƒ€‚ÌŠJ”­DƒrƒWƒ‡ƒ“‹Zp‚ÌŽÀ—˜—pƒ[ƒNƒVƒ‡ƒbƒviViEW2010jCI-37Cpp.296-302CƒpƒVƒtƒBƒR‰¡•lƒAƒlƒbƒNƒXƒz[ƒ‹i_“ސìjC2010.12D
  89. ꎓ¡ „ŽjCŽR‰º W•½C¬¼ —º‰îDŒûŒ`”FŽ¯‚É—LŒø‚ÈŽ‹“_‚ÌŒŸ“¢D‘æ15‰ñƒpƒ^[ƒ“Œv‘ªƒVƒ“ƒ|ƒWƒEƒ€i‘æ83‰ñƒpƒ^[ƒ“Œv‘ª•”‰ïŒ¤‹†‰ïjCpp.43-48Cƒfƒ…[ƒvƒŒƒbƒNƒXƒZƒ~ƒi[ƒzƒeƒ‹iˆï–؁jC2010.12D
  90. ¼‰º KŽiC‹g“c _Ž¡C‘O“c —SìCꎓ¡ „ŽjC”öè ’mKC¬¼ —º‰îD‰¹º‘€ìŒ^“d“®ŽÔ‚¢‚·‚̃GƒŒƒx[ƒ^æ~‚ÉŠÖ‚·‚錤‹†D‘æ19‰ñŒv‘ªŽ©“®§ŒäŠw‰ï’†‘Žx•”Šwpu‰‰‰ïC309Cpp.114-115C“‡ª‘åŠwi“‡ªjC2010.11D
  91. •Ä’Ž ‘å‹PCꎓ¡ „ŽjCŠÛŽR N•½C”öè ’mKC¬¼ —º‰îC—› Žd„D‹›ŠáƒJƒƒ‰‚ð—p‚¢‚½’Ǐ]‹AŠÒŒ^ˆÚ“®ƒƒ{ƒbƒg‚ÌŠJ”­D‘æ19‰ñŒv‘ªŽ©“®§ŒäŠw‰ï’†‘Žx•”Šwpu‰‰‰ïC310Cpp.116-117C“‡ª‘åŠwi“‡ªjC2010.11D
  92. ‘O“c —SìC‹g“c _Ž¡C¼‰º KŽiCꎓ¡ „ŽjC”öè ’mKC¬¼ —º‰îDƒŒ[ƒUƒŒƒ“ƒWƒtƒ@ƒCƒ“ƒ_‚ð—p‚¢‚½’Ǐ]‹AŠÒŒ^ˆÚ“®ƒƒ{ƒbƒg‚ÌŠJ”­D‘æ19‰ñŒv‘ªŽ©“®§ŒäŠw‰ï’†‘Žx•”Šwpu‰‰‰ïC312Cpp.120-121C“‡ª‘åŠwi“‡ªjC2010.11D
  93. ‹g“c _Ž¡C¼‰º KŽiC‘O“c —SìCꎓ¡ „ŽjC”öè ’mKC¬¼ —º‰îDƒŒ[ƒUƒŒƒ“ƒWƒtƒ@ƒCƒ“ƒ_‚ð—p‚¢‚½SLAM ‚É‚æ‚éŽÔ‚¢‚·‚̕ljˆ‚¢‘–sD‘æ19‰ñŒv‘ªŽ©“®§ŒäŠw‰ï’†‘Žx•”Šwpu‰‰‰ïC313Cpp.122-123C“‡ª‘åŠwi“‡ªjC2010.11D
  94. ‰Œû •¶‹MCꎓ¡ „ŽjC¬¼ —º‰îDƒjƒ…[ƒX‰f‘œ‚É‚¨‚¯‚éŠî–{ŒûŒ`‚Ì”FŽ¯D‘æ19‰ñŒv‘ªŽ©“®§ŒäŠw‰ï’†‘Žx•”Šwpu‰‰‰ïC504Cpp.184-185C“‡ª‘åŠwi“‡ªjC2010.11D
  95. ŠÛŽR N•½Cꎓ¡ „ŽjC¬¼ —º‰îDƒ‰ƒ“ƒ_ƒ€ƒTƒ“ƒvƒŠƒ“ƒO‚Æ2Ží—ނ̃gƒ‰ƒbƒJ‚É‚æ‚éMean Shift’ǐՁD“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ61‰ñ˜A‡‘å‰ïC23-18Cpp.56-57C‰ªŽRŒ§—§‘åŠwi‰ªŽRjC2010.10D
  96. ŽR‰º W•½Cꎓ¡ „ŽjC¬¼ —º‰îD‘½Ž‹“_ƒJƒƒ‰‚É‚æ‚é”­˜bƒV[ƒ“‚ð—p‚¢‚½ŒûŒ`”FŽ¯‚É—LŒø‚ÈŽ‹“_‚ÌŒŸ“¢D“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ61‰ñ˜A‡‘å‰ïC22-7Cpp.24-25C‰ªŽRŒ§—§‘åŠwi‰ªŽRjC2010.10D
  97. “à“c Ž•FCꎓ¡ „ŽjC¬¼ —º‰îD•¶Í”­˜bƒV[ƒ“‚©‚ç‚Ì’PŒêƒXƒ|ƒbƒeƒBƒ“ƒO”FŽ¯D“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ61‰ñ˜A‡‘å‰ïC22-8Cpp.26-27C‰ªŽRŒ§—§‘åŠwi‰ªŽRjC2010.10D
  98. ꎓ¡ „ŽjC“à“c Ž•FC¬¼ —º‰îD˜A‘±DPƒ}ƒbƒ`ƒ“ƒO‚ð—p‚¢‚½”­˜bƒV[ƒ“‚©‚ç‚Ì’PŒêƒXƒ|ƒbƒeƒBƒ“ƒO”FŽ¯D“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯EƒƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.110Cno.219CPRMU2010-98Cpp.53-58C–‹’£ƒƒbƒZiç—tjC2010.10D
  99. ꎓ¡ „ŽjC“à“c Ž•FC¬¼ —º‰îD”­˜bƒV[ƒ“‚©‚ç‚̃[ƒhƒXƒ|ƒbƒeƒBƒ“ƒO“ǐOD•½¬22”N“x“d‹CŠÖŒWŠw‰ï‹ãBŽx•”˜A‡‘å‰ïi‘æ63‰ñ˜A‡‘å‰ïjC09-2P-11Cpp.622-623C2010.9D
  100. ꎓ¡ „ŽjC¬¼ —º‰îDƒtƒŒ[ƒ€ƒŒ[ƒg•Ï‰»‚É‚æ‚é’PŒê“ǐO‚̉e‹¿‚ÉŠÖ‚·‚élŽ@D‘æ13‰ñ@‰æ‘œ‚Ì”FŽ¯E—‰ðƒVƒ“ƒ|ƒWƒEƒ€iMIRU2010jCIS1-53Cpp.400-407C‹ú˜HŽsŠÏŒõ‘ÛŒð—¬ƒZƒ“ƒ^[i–kŠC“¹jC2010.7D
  101. ŠÛŽR N•½Cꎓ¡ „ŽjC¬¼ —º‰îDŒ`ó‚Æ”wŒi•Ï‰»‚Ɋ挒‚ÈMean Shift’ǐՁD‘æ13‰ñ@‰æ‘œ‚Ì”FŽ¯E—‰ðƒVƒ“ƒ|ƒWƒEƒ€iMIRU2010jCOS13-2Cpp.1539-1546C‹ú˜HŽsŠÏŒõ‘ÛŒð—¬ƒZƒ“ƒ^[i–kŠC“¹jC2010.7D
  102. ꎓ¡ „ŽjCÎ‘q Š°”VCŽR‰º W•½C¬¼ —º‰îDƒgƒ‰ƒWƒFƒNƒgƒŠ“Á’¥—Ê‚ð—˜—p‚µ‚½’PŒê“ǐO‚ÉŠÖ‚·‚éŠî‘bŒŸ“¢D“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯EƒƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.109Cno.470CPRMU2009-277CHIP2009-162Cpp.259-264CŽ­Ž™“‡‘åŠwiŽ­Ž™“‡jC2010.3D
  103. ŠÛŽR N•½Cꎓ¡ „ŽjC¬¼ —º‰îD“Á’¥‘I‘ð‚Æ•¡”‚̃Tƒuƒgƒ‰ƒbƒJ‚É‚æ‚éMean Shift’ǐՁD“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯EƒƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.109Cno.373CCQ2009-96CPRMU2009-195CSP2009-136CMVE2009-118Cpp.267-272C‹ž“s‘åŠwi‹ž“sjC2010.1D

  104. ‘ºˆä ²Cꎓ¡ „ŽjC”öè ’mKC¬¼ —º‰îDƒŒ[ƒU[ƒŒƒ“ƒWƒtƒ@ƒCƒ“ƒ_‚ð—p‚¢‚½–¢’mŠÂ‹«‚É‚¨‚¯‚鎩ŒÈˆÊ’u„’è‚Æ’n}¶¬D‘æ18‰ñŒv‘ªŽ©“®§ŒäŠw‰ï’†‘Žx•”Šwpu‰‰‰ïC619Cpp.256-257C’¹Žæ‘åŠwi’¹ŽæjC2009.11D
  105. ’Óc ’¼Ž÷Cꎓ¡ „ŽjC”öè ’mKC¬¼ —º‰îC—› Žd„D’PŠáƒJƒƒ‰‚ð—p‚¢‚½Ž©—¥’Ǐ]ƒƒ{ƒbƒg‚ÌŠJ”­D‘æ18‰ñŒv‘ªŽ©“®§ŒäŠw‰ï’†‘Žx•”Šwpu‰‰‰ïC504Cpp.180-181C’¹Žæ‘åŠwi’¹ŽæjC2009.11D
  106. ŽR•½ _‘¾˜NCꎓ¡ „ŽjC¬¼ —º‰îDŒg‘Ñ“d˜b“‹ÚƒJƒƒ‰‚É‚æ‚é’PŒê“ǐOD‘æ18‰ñŒv‘ªŽ©“®§ŒäŠw‰ï’†‘Žx•”Šwpu‰‰‰ïC312Cpp.118-119C’¹Žæ‘åŠwi’¹ŽæjC2009.11D
  107. Î‘q Š°”VCꎓ¡ „ŽjC¬¼ —º‰îDŠç‰æ‘œ‚ð—p‚¢‚½’PŒê“ǐOD‘æ18‰ñŒv‘ªŽ©“®§ŒäŠw‰ï’†‘Žx•”Šwpu‰‰‰ïC311Cpp.116-117C’¹Žæ‘åŠwi’¹ŽæjC2009.11D
  108. ŠÛŽR N•½Cꎓ¡ „ŽjC¬¼ —º‰îD—̈敪Š„‚É‚æ‚é•¡”‚̃Tƒuƒgƒ‰ƒbƒJ‚Æ“Á’¥‘I‘ð‚É‚æ‚éMean Shift’ǐՁD‘æ18‰ñŒv‘ªŽ©“®§ŒäŠw‰ï’†‘Žx•”Šwpu‰‰‰ïC310Cpp.114-115C’¹Žæ‘åŠwi’¹ŽæjC2009.11D
  109. ꎓ¡ „ŽjC›Œ´ ˆêEC¬¼ —º‰îDŠ„‚蔢Œ´Œ`‚ÌŒŸ¸ƒVƒXƒeƒ€‚ÌŠJ”­D‘æ14‰ñƒpƒ^[ƒ“Œv‘ªƒVƒ“ƒ|ƒWƒEƒ€i‘æ80‰ñƒpƒ^[ƒ“Œv‘ª•”‰ïŒ¤‹†‰ïjCpp.7-12CŽOŒõ‘‘i‰ªŽRjC2009.11D
  110. X‰º ˜a•qCꎓ¡ „ŽjC¬¼ —º‰îD”­˜bƒV[ƒ“‚©‚ç‚̃L[ƒtƒŒ[ƒ€ŒŸo‚Æ’PŒê”FŽ¯D“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ60‰ñ˜A‡‘å‰ïC22-10Cpp.144-145CL“‡Žs—§‘åŠwiL“‡jC2009.10D
  111. –¾ŽR Š°ŽjCì‘º ®¶C›Œ´ ˆêECꎓ¡ „ŽjC¬¼ —º‰îD‘½‹@”\ƒRƒ“ƒZƒ“ƒg‚̃XƒPƒWƒ…[ƒŠƒ“ƒO‹@”\‚É‚æ‚é‘Ò‹@“d—͂̍팸D‘æ8‰ñî•ñ‰ÈŠw‹ZpƒtƒH[ƒ‰ƒ€iFIT2009jCRC-009Cpp.173-179C“Œ–kH‹Æ‘åŠwi‹{éjC2009.9D
    • ‘DˆäƒxƒXƒgƒy[ƒp[Ü ŽóÜ
  112. ŠÛŽR N•½Cꎓ¡ „ŽjC¬¼ —º‰îD’¹ŠQ‘΍ôƒVƒXƒeƒ€‚Ì‚½‚ß‚Ì’¹’ǐՂƉH‚΂½‚«“®ì‚Ì”FŽ¯D‘æ12‰ñ@‰æ‘œ‚Ì”FŽ¯E—‰ðƒVƒ“ƒ|ƒWƒEƒ€iMIRU2009jCIS2-65Cpp.1301-1308C‚­‚É‚Ñ‚«ƒƒbƒZi“‡ªjC2009.7D
  113. X‰º ˜a•qCꎓ¡ „ŽjC¬¼ —º‰îD”­˜bƒV[ƒ“‚©‚ç‚̃L[ƒtƒŒ[ƒ€ŒŸo‚ƃL[ƒtƒŒ[ƒ€‚ÉŠî‚­’PŒê“ǐOD‘æ12‰ñ@‰æ‘œ‚Ì”FŽ¯E—‰ðƒVƒ“ƒ|ƒWƒEƒ€iMIRU2009jCIS1-58Cpp.753-760C‚­‚É‚Ñ‚«ƒƒbƒZi“‡ªjC2009.7D
  114. …Œû ³Ž¡C‘ºˆä ²C¼X ‰ëlCꎓ¡ „ŽjC”öè ’mKC¬¼ —º‰îDƒGƒŒƒx[ƒ^‚É‚æ‚éƒtƒƒAŠÔˆÚ“®‚ª‰Â”\‚ȉ¹º‘€ìŒ^“d“®ŽÔ‚¢‚·D“dŽqî•ñ’ʐMŠw‰ï@•ŸŽƒî•ñHŠwŒ¤‹†‰ïCvol.108Cno.488CWIT2008-80Cpp.67-72C“‡ª‘åŠwi“‡ªjC2009.3D
  115. ‘½“c ’¼–çCꎓ¡ „ŽjC¬¼ —º‰îD’PŠáƒJƒƒ‰‚ð—p‚¢‚½‰®“àˆÚ“®ƒƒ{ƒbƒgD“dŽqî•ñ’ʐMŠw‰ï@2009‘‡‘å‰ïCAS-2-3Cˆ¤•Q‘åŠwiˆ¤•QjC2009.3D

  116. ‰Á“¡ —FÆCꎓ¡ „ŽjC¬¼ —º‰îDƒgƒ‰ƒWƒFƒNƒgƒŠ“Á’¥—Ê‚ÉŠî‚­’PŒê”FŽ¯‚̃ŠƒAƒ‹ƒ^ƒCƒ€ˆ—D“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯EƒƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.108Cno.327CPRMU2008-122CMVE2008-71Cpp.75-80C‘åã‘åŠwi‘åãjC2008.11D
  117. ꎓ¡ „ŽjCX‰º ˜a•qC¬¼ —º‰îD”­˜bŽž‚É‚¨‚¯‚éŒûOŒ`ó‚̕ω»‚É‚æ‚é’PŒê”FŽ¯D‘æ13‰ñƒpƒ^[ƒ“Œv‘ªƒVƒ“ƒ|ƒWƒEƒ€i‘æ77‰ñƒpƒ^[ƒ“Œv‘ª•”‰ïŒ¤‹†‰ïjCpp.21-26C‚©‚ñ‚ۂ̏h C‘PŽ›iÃ‰ªjC2008.11D
  118. j–{ CŽŸC’Óc ’¼Ž÷C”öè ’mKCꎓ¡ „ŽjC¬¼ —º‰îDŽ©—¥“I‹AŠÒ‹@”\‚ðŽ‚ÂƒJƒƒ‰‰æ‘œ‚ð—˜—p‚µ‚½‰®“àˆÚ“®ƒƒ{ƒbƒgƒVƒXƒeƒ€‚ÌŠJ”­D‘æ17‰ñŒv‘ªŽ©“®§ŒäŠw‰ï’†‘Žx•”Šwpu‰‰‰ïC101Cpp.18-19CL“‡‘åŠwiL“‡jC2008.11D
  119. ‘½“c ’¼–çCꎓ¡ „ŽjC¬¼ —º‰îDŠëŒ¯‰ñ”ð‹@”\‚ð‚à‚˜L‰º’†‰›‘–sŒ^ˆÚ“®ƒƒ{ƒbƒg‚ÌŠJ”­D“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ59‰ñ˜A‡‘å‰ïC1404-2Cpp.475-476C’¹Žæ‘åŠwi’¹ŽæjC2008.10D
  120. ’Óc ’¼Ž÷Cꎓ¡ „ŽjC¬¼ —º‰îD’Ǐ]‘Ώێ҂̑«Œ³‚É’…–Ú‚µ‚½Ž©—¥’Ǐ]Œ^ƒVƒXƒeƒ€‚ÌŠJ”­D“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ59‰ñ˜A‡‘å‰ïC1404-1Cpp.473-474C’¹Žæ‘åŠwi’¹ŽæjC2008.10D
  121. X‰º ˜a•qCꎓ¡ „ŽjC¬¼ —º‰îD“ǐO‚É‚¨‚¯‚é”­˜bƒV[ƒ“‚©‚ç‚̉¹ß‚ÌŽ©“®’ŠoD“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ59‰ñ˜A‡‘å‰ïC1303-4Cpp.429-430C’¹Žæ‘åŠwi’¹ŽæjC2008.10D
  122. ‰Á“¡ —FÆCꎓ¡ „ŽjC¬¼ —º‰îDƒgƒ‰ƒWƒFƒNƒgƒŠ“Á’¥—Ê‚ð—p‚¢‚½ƒŠƒAƒ‹ƒ^ƒCƒ€’PŒê”FŽ¯D“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ59‰ñ˜A‡‘å‰ïC1303-3Cpp.427-428C’¹Žæ‘åŠwi’¹ŽæjC2008.10D
  123. ŽR•½ _‘¾˜NCꎓ¡ „ŽjC¬¼ —º‰îDŠç–Ê”M‰æ‘œ‚ð—p‚¢‚½ŒÂl”FØ‚ÉŠÖ‚·‚錤‹†D“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ59‰ñ˜A‡‘å‰ïC1301-5Cp.411C’¹Žæ‘åŠwi’¹ŽæjC2008.10D
  124. –¾ŽR Š°ŽjCì‘º ®¶C›Œ´ ˆêECꎓ¡ „ŽjC¬¼ —º‰îDƒRƒ“ƒZƒ“ƒg‚Ì‘½‹@”\‰»‚É‚æ‚é‘Ò‹@“d—͂̍팸D“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ59‰ñ˜A‡‘å‰ïC0604-5Cp.224C’¹Žæ‘åŠwi’¹ŽæjC2008.10D
  125. ¼X ‰ëlC…Œû ³Ž¡C‘ºˆä ²C”öè ’mKCꎓ¡ „ŽjC¬¼ —º‰îD‰¹º‘€ìŒ^“d“®ŽÔˆÖŽq‚̃tƒ@ƒWƒB§Œä‚ð—p‚¢‚½‘–s•â•‚ÉŠÖ‚·‚錤‹†D“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ59‰ñ˜A‡‘å‰ïC0104-6Cp.31C’¹Žæ‘åŠwi’¹ŽæjC2008.10D
  126. …Œû ³Ž¡C¼X ‰ëlC‘ºˆä ²Cꎓ¡ „ŽjC”öè ’mKC¬¼ —º‰îDÕ“ˉñ”ð‹@”\‚É‚æ‚鉹º‘€ìŒ^“d“®ŽÔˆÖŽq‚Ì‘€ì«Œüã‚ÉŠÖ‚·‚錤‹†D“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ59‰ñ˜A‡‘å‰ïC0104-5Cp.30C’¹Žæ‘åŠwi’¹ŽæjC2008.10D
  127. ŽR•½ _‘¾˜NCꎓ¡ „ŽjC¬¼ —º‰îDŠç–Ê”M‰æ‘œ‚ð—p‚¢‚½Šç”FŽ¯DŒv‘ªŽ©“®§ŒäŠw‰ï‘æ25‰ñƒZƒ“ƒVƒ“ƒOƒtƒH[ƒ‰ƒ€Cpp.100-104C²‰ê‘åŠwi²‰êjC2008.9D
  128. ¬¼ —º‰îCꎓ¡ „ŽjCÂ“c —SŽ÷C”öè ’mKC›Œ´ ˆêED“d—¬ƒZƒ“ƒT‚ÉŠî‚­‰Æ“d‹@Ší‚Ì”FŽ¯DŒv‘ªŽ©“®§ŒäŠw‰ï‘æ25‰ñƒZƒ“ƒVƒ“ƒOƒtƒH[ƒ‰ƒ€Cpp.88-93C²‰ê‘åŠwi²‰êjC2008.9D
  129. ꎓ¡ „ŽjC¬¼ —º‰îD‰¡Šç‰æ‘œ‚Ì—ÖŠsŒ`ó‚ÉŠî‚­“ǐOD“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯EƒƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.108Cno.198CPRMU2008-77CHIP2008-77Cpp.187-192CŒcœä‹`m‘åŠwi_“ސìjC2008.9D
  130. ꎓ¡ „ŽjCX‰º ˜a•qC¬¼ —º‰îD•¡”ŒûO—̈æ‚ð—˜—p‚µ‚½‘½Œ¾Œê‚É—LŒø‚È’PŒê“ǐOD“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯EƒƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.108Cno.198CPRMU2008-76CHIP2008-76Cpp.181-186CŒcœä‹`m‘åŠwi_“ސìjC2008.9D
  131. –¾ŽR Š°ŽjCì‘º ®¶C›Œ´ ˆêECꎓ¡ „ŽjC¬¼ —º‰îDƒlƒbƒgƒ[ƒN‚©‚琧Œä‰Â”\‚È‘½‹@”\ƒRƒ“ƒZƒ“ƒgD‘æ7‰ñî•ñ‰ÈŠw‹ZpƒtƒH[ƒ‰ƒ€iFIT2008jCC-025Cpp.259-262CŒcœä‹`m‘åŠwi_“ސìjC2008.9D
    • ƒ„ƒ“ƒOƒŠƒT[ƒ`ƒƒ[Ü ŽóÜi–¾ŽRŒNj
  132. ꎓ¡ „ŽjC‹v–Ø vCX‰º ˜a•qC¬¼ —º‰îD•¡”‚ÌŒûO—̈æ‚ð—p‚¢‚½’PŒê”FŽ¯D‘æ11‰ñ@‰æ‘œ‚Ì”FŽ¯E—‰ðƒVƒ“ƒ|ƒWƒEƒ€iMIRU2008jCIS1-17Cpp.434-439CŒyˆä‘òƒvƒŠƒ“ƒXƒzƒeƒ‹i’·–ìjC2008.7D
  133. …Œû ³Ž¡C¼X ‰ëlC‘ºˆä ²Cꎓ¡ „ŽjC”öè ’mKC¬¼ —º‰îD‰¹º–½—ß‚É‚æ‚é“d“®ŽÔˆÖŽq‚Ì‘€ìD“dŽqî•ñ’ʐMŠw‰ï@•ŸŽƒî•ñHŠwŒ¤‹†‰ïCvol.108Cno.66CWIT2008-9Cpp.49-54C_ŒË‘åŠwi•ºŒÉjC2008.5D
  134. ‰Á“¡ —FÆCꎓ¡ „ŽjC¬¼ —º‰îDƒŠƒAƒ‹ƒ^ƒCƒ€Œû•”Œ`ó”FŽ¯‚ð—˜—p‚µ‚½ˆÓŽv“`’BƒVƒXƒeƒ€D“dŽqî•ñ’ʐMŠw‰ï@•ŸŽƒî•ñHŠwŒ¤‹†‰ïCvol.107Cno.433CWIT2007-89Cpp.99-104C“‡ª‘åŠwi“‡ªjC2008.1D
  135. ꎓ¡ „ŽjC‰Á“¡ —FÆC¬¼ —º‰îDŒû•”ƒpƒ^[ƒ“Œ`ó‚ð—˜—p‚µ‚½•¶Žš“ü—̓VƒXƒeƒ€D“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯EƒƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.107Cno.427CPRMU2007-161Cpp.23-28C—´’J‘åŠwiŽ ‰êjC2008.1D

  136. ‘½“c ’¼–çCꎓ¡ „ŽjC¬¼ —º‰îD1‘ä‚̃Jƒƒ‰‚𓋍ڂ·‚éˆÚ“®ƒƒ{ƒbƒg‚Ì‚½‚ß‚Ì‘–s—̈æ‚ÌŽ©“®ŒŸoDIEEEL“‡Žx•”‘æ9‰ñŠw¶ƒVƒ“ƒ|ƒWƒEƒ€CA-39C’¹Žæ‘åŠwi’¹ŽæjC2007.11D
  137. ‰Á“¡ —FÆCꎓ¡ „ŽjC¬¼ —º‰îDŒû•”Œ`ó‚̃ŠƒAƒ‹ƒ^ƒCƒ€”FŽ¯DIEEEL“‡Žx•”‘æ9‰ñŠw¶ƒVƒ“ƒ|ƒWƒEƒ€CA-36C’¹Žæ‘åŠwi’¹ŽæjC2007.11D
  138. ‘º“c Œ\‰îC‘½“c ’¼–çCꎓ¡ „ŽjC”öè ’mKC¬¼ —º‰îDƒJƒƒ‰‰æ‘œ‚ð—˜—p‚µ‚½‰®“àˆÚ“®ƒƒ{ƒbƒg‚ÌŠJ”­DŒv‘ªŽ©“®§ŒäŠw‰ï‘æ16‰ñ’†‘Žx•”Šwpu‰‰‰ïC410Cpp.154-155CŽRŒû‘åŠwiŽRŒûjC2007.11D
  139. ‘½“c ’¼–çCꎓ¡ „ŽjC¬¼ —º‰îD‰®“àˆÚ“®ƒƒ{ƒbƒg‚Ì‚½‚߂̉摜î•ñ‚ÉŠî‚­‹È‚ª‚èŠp‚ÌŽ©“®ŒŸoD“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ58‰ñ˜A‡‘å‰ïC22-12Cpp.214-215CL“‡‘åŠwiL“‡jC2007.10D
  140. ‰Á“¡ —FÆCꎓ¡ „ŽjC¬¼ —º‰îDƒŠƒAƒ‹ƒ^ƒCƒ€“ǐO‚Ì‚½‚ß‚ÌŒû•”Œ`ó”FŽ¯D“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ58‰ñ˜A‡‘å‰ïC22-11Cpp.212-213CL“‡‘åŠwiL“‡jC2007.10D
    • •½¬19”N“x@“dŽqî•ñ’ʐMŠw‰ï’†‘Žx•”§—ãÜ ŽóÜ
    • •½¬19”N“x@î•ñˆ—Šw‰ï’†‘Žx•”—DG˜_•¶”­•\Ü ŽóÜ
  141. ‘哇 ’¼–çCꎓ¡ „ŽjC¬¼ —º‰îD‚‘¬‚©‚ƒƒoƒXƒg‚ÈMean Shift’ǐՁD“dŽqî•ñ’ʐMŠw‰ï@2007ƒ\ƒTƒCƒGƒeƒB‘å‰ïCA-4-56Cp.117C’¹Žæ‘åŠwi’¹ŽæjC2007.9D
  142. ¼X ‰ëlCꎓ¡ „ŽjC¬¼ —º‰îD‰¹º‘€ìŒ^“d“®ŽÔˆÖŽq‚ÌŠJ”­D“dŽqî•ñ’ʐMŠw‰ï@2007ƒ\ƒTƒCƒGƒeƒB‘å‰ïCA-4-40Cp.101C’¹Žæ‘åŠwi’¹ŽæjC2007.9D
  143. …Œû ³Ž¡Cꎓ¡ „ŽjC¬¼ —º‰îDŠç–Ê”M‰æ‘œ‚ð—˜—p‚µ‚½ŒÂl”FØD“dŽqî•ñ’ʐMŠw‰ï@2007ƒ\ƒTƒCƒGƒeƒB‘å‰ïCA-4-20Cp.81C’¹Žæ‘åŠwi’¹ŽæjC2007.9D
  144. ‘½“c ’¼–çCꎓ¡ „ŽjC¬¼ —º‰îD‰®“àˆÚ“®ƒƒ{ƒbƒg‚Ì‚½‚ß‚Ì‘–s—̈æ‚ÌŒŸoD“dŽqî•ñ’ʐMŠw‰ï@2007ƒ\ƒTƒCƒGƒeƒB‘å‰ïCA-4-13Cp.74C’¹Žæ‘åŠwi’¹ŽæjC2007.9D
  145. ‰Á“¡ —FÆCꎓ¡ „ŽjC¬¼ —º‰îDƒŠƒAƒ‹ƒ^ƒCƒ€Œû•”ƒpƒ^[ƒ“”FŽ¯D“dŽqî•ñ’ʐMŠw‰ï@2007ƒ\ƒTƒCƒGƒeƒB‘å‰ïCA-4-7Cp.68C’¹Žæ‘åŠwi’¹ŽæjC2007.9D
  146. ‹v–Ø vCꎓ¡ „ŽjC¬¼ —º‰îD“ǐO‚É‚¨‚¯‚é”­˜b’PŒê‚©‚ç‚̉¹ß‚ÌŽ©“®’Šo‚ƕꉹ•ª—ށD“dŽqî•ñ’ʐMŠw‰ï@2007ƒ\ƒTƒCƒGƒeƒB‘å‰ïCA-4-6Cp.67C’¹Žæ‘åŠwi’¹ŽæjC2007.9D
  147. ꎓ¡ „ŽjC¬¼ —º‰îDO‚¨‚æ‚ÑŒû“à—̈æŒ`ó‚ÉŠî‚­ƒgƒ‰ƒWƒFƒNƒgƒŠ“Á’¥—Ê‚É‚æ‚é“ǐOD‘æ6‰ñî•ñ‰ÈŠw‹ZpƒtƒH[ƒ‰ƒ€iFIT2007jCH-016Cpp.39-40C’†‹ž‘åŠwiˆ¤’mjC2007.9D
    • ƒ„ƒ“ƒOƒŠƒT[ƒ`ƒƒ[Ü ŽóÜ
  148. ꎓ¡ „ŽjC‹v–Ø vC¬¼ —º‰îDŒû“à—̈æŒ`ó‚ÉŠî‚­“ú–{Œê’P‰¹‚Ì•ª—ށD“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯EƒƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.106Cno.606CPRMU2006-282C‰ªŽR‘åŠwi‰ªŽRjC2007.3D

  149. ‹v–Ø vCꎓ¡ „ŽjC¬¼ —º‰îD‰æ‘œˆ—‚É‚æ‚é•ê‰¹”FŽ¯‚Ì‚½‚ß‚Ì—LŒø‚È“Á’¥—Ê‚ÌŒŸ“¢DŒv‘ªŽ©“®§ŒäŠw‰ï‘æ15‰ñ’†‘Žx•”Šwpu‰‰‰ïCpp.252-253C‰ªŽR—‰È‘åi‰ªŽRjC2006.11D
  150. ‚‹´ ”͍sCꎓ¡ „ŽjC¬¼ —º‰îDŒõŒ¹ŠÂ‹«‚ɃƒoƒXƒg‚ÈŒûo—̈æ‚ÌŽ©“®’ŠoD“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ57‰ñ˜A‡‘å‰ïCpp.125-126C‰ªŽR—‰È‘åi‰ªŽRjC2006.10D
    • •½¬18”N“x@“dŽqî•ñ’ʐMŠw‰ï’†‘Žx•”§—ãÜ ŽóÜ
    • •½¬18”N“x@î•ñˆ—Šw‰ï’†‘Žx•”—DG˜_•¶”­•\Ü ŽóÜ
  151. ¬—Ñ ”¹lCꎓ¡ „ŽjC¬¼ —º‰îD“®‚«—Ê‚ÉŠî‚­SSRƒtƒBƒ‹ƒ^[‚É‚æ‚éŠçˆÊ’uŒŸoD“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ57‰ñ˜A‡‘å‰ïCpp.127-128C‰ªŽR—‰È‘åi‰ªŽRjC2006.10D
  152. ’·”ö ‘ˆê˜YCꎓ¡ „ŽjC¬¼ —º‰îDŽ©—¥Œ^’Ǐ]ƒVƒXƒeƒ€‚É‚¨‚¯‚éæsŽÒ—̈æ‚Ì’ŠoD“d‹CEî•ñŠÖ˜AŠw‰ï’†‘Žx•”‘æ57‰ñ˜A‡‘å‰ïCpp.129-130C‰ªŽR—‰È‘åi‰ªŽRjC2006.10D
  153. ꎓ¡ „ŽjC¬¼ —º‰îDƒrƒfƒI‰æ‘œ‚Æ”M‰æ‘œ‚ð—p‚¢‚½’PŒê”FŽ¯D‘æ5‰ñî•ñ‰ÈŠw‹ZpƒtƒH[ƒ‰ƒ€iFIT2006jCpp.94-100C•Ÿ‰ª‘åŠwi•Ÿ‰ªjC2006.9D
  154. ꎓ¡ „ŽjC¬—Ñ ”¹lC¬¼ —º‰îDƒeƒŒƒr‰ï‹c‚Ì‚½‚ß‚Ì”­Œ¾ŽÒŽ©“®ŒŸoD“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯EƒƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.106Cno.72CPRMU2006-5Cpp.25-30Cˆ¤’mŒ§—§‘åŠwiˆ¤’mjC2006.5D
  155. ‘哇 ’¼–çCꎓ¡ „ŽjC¬¼ —º‰îDƒIƒvƒeƒBƒJƒ‹ƒtƒ[•ª•z‚ð—˜—p‚µ‚½Mean Shift–@‚É‚æ‚é’ǐՁD“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯EƒƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.105Cno.534CPRMU2005-168Cpp.111-116C‘åã‘åŠwi‘åãjC2006.1D
  156. ꎓ¡ „ŽjC¬¼ —º‰îDƒrƒfƒI‰æ‘œ‚Æ”M‰æ‘œ‚É‚æ‚é“ǐOD“dŽqî•ñ’ʐMŠw‰ï@ƒpƒ^[ƒ“”FŽ¯EƒƒfƒBƒA—‰ðŒ¤‹†‰ïCvol.105Cno.534CPRMU2005-175Cpp.153-158C‘åã‘åŠwi‘åãjC2006.1D

  157. à_–ì WˆêCꎓ¡ „ŽjC¬¼ —º‰îDŽü”g”EˆÊ‘Š„’èŽè–@‚É‚æ‚é•¡”‰¹º‚Ì•ûŒü„’è‚̉ü‘PDŒv‘ªŽ©“®§ŒäŠw‰ï‘æ14‰ñ’†‘Žx•”Šwpu‰‰‰ïCpp.44-45C“‡ª‘åŠwi“‡ªjC2005.11D
  158. ’·”ö ‘ˆê˜YCŽR’n —C‰îC”öè ’mKCꎓ¡ „ŽjC¬¼ —º‰îDŽ©—¥Œ^’Ǐ]ƒVƒXƒeƒ€‚É‚¨‚¯‚éƒtƒŒ[ƒ€ŠÔ·•ª–@‚ð—p‚¢‚½’Ǐ]‘Ώێ҂̌ŸoDŒv‘ªŽ©“®§ŒäŠw‰ï‘æ14‰ñ’†‘Žx•”Šwpu‰‰‰ïCpp.96-97C“‡ª‘åŠwi“‡ªjC2005.11D
  159. ‚‹´ ”͍sCꎓ¡ „ŽjC¬¼ —º‰îDFî•ñ‚ð—p‚¢‚½Sampled Active Contour Model‚É‚æ‚éO—̈æ‚Ì’ŠoDŒv‘ªŽ©“®§ŒäŠw‰ï‘æ14‰ñ’†‘Žx•”Šwpu‰‰‰ïCpp.98-99C“‡ª‘åŠwi“‡ªjC2005.11D
  160. ‘哇 ’¼–çCꎓ¡ „ŽjC¬¼ —º‰îDÆŽËŒ`ÔŠOüƒJƒƒ‰‚É‚æ‚él•¨’Ç”öDŒv‘ªŽ©“®§ŒäŠw‰ï‘æ22‰ñƒZƒ“ƒVƒ“ƒOƒtƒH[ƒ‰ƒ€Cpp.9-13C‘åã‘åŠwi‘åãjC2005.9D
  161. ¬—Ñ ”¹lC‘哇 ’¼–çC”öè ’mKCꎓ¡ „ŽjC¬¼ —º‰îD“®‰æ‘œˆ—‚ð—˜—p‚µ‚½ŠÈˆÕ“I‚ȃlƒbƒgƒ[ƒNƒeƒŒƒr‰ï‹cƒVƒXƒeƒ€DŒv‘ªŽ©“®§ŒäŠw‰ï‘æ22‰ñƒZƒ“ƒVƒ“ƒOƒtƒH[ƒ‰ƒ€Cpp.85-90C‘åã‘åŠwi‘åãjC2005.9D

  162. •Ÿˆä NmCꎓ¡ „ŽjC”öè ’mKC¬¼ —º‰îD‰¹º‚ƐO‰æ‘œ‚ð—p‚¢‚½ƒoƒCƒ‚[ƒ_ƒ‹‰¹º”FŽ¯‚Ì‚½‚߂̓ǐOŽè–@‚ÉŠÖ‚·‚錤‹†DŒv‘ªŽ©“®§ŒäŠw‰ï‘æ13‰ñ’†‘Žx•”Šwpu‰‰‰ïCpp.84-85C’¹Žæ‘åŠwi’¹ŽæjC2004.11D
    • •½¬16”N“x@Œv‘ªŽ©“®§ŒäŠw‰ï’†‘Žx•”§—ãÜ ŽóÜ
  163. ‘哇 ’¼–çCà_–ì WˆêC”öè ’mKCꎓ¡ „ŽjC¬¼ —º‰îDŒÂl‚Å‚Ì—˜—p‚ð‘z’肵‚½N“üŽÒ’Ç”öƒVƒXƒeƒ€‚̍\’zDŒv‘ªŽ©“®§ŒäŠw‰ï‘æ13‰ñ’†‘Žx•”Šwpu‰‰‰ïCpp.100-101C’¹Žæ‘åŠwi’¹ŽæjC2004.11D
  164. à_–ì WˆêC‘哇 ’¼–çC”öè ’mKCꎓ¡ „ŽjC¬¼ —º‰îD‰¹Œ¹•ûŒü„’è‚ð—p‚¢‚½‰ï‹cƒVƒXƒeƒ€‚̍\’zDŒv‘ªŽ©“®§ŒäŠw‰ï‘æ13‰ñ’†‘Žx•”Šwpu‰‰‰ïCpp.110-111C’¹Žæ‘åŠwi’¹ŽæjC2004.11D
  165. ¬ù —²KCꎓ¡ „ŽjC”öè ’mKC¬¼ —º‰îDŽ©—¥Œ^’Ǐ]ƒVƒXƒeƒ€‚̉~ŠŠ‹ì“®‚ÉŠÖ‚·‚錤‹†DŒv‘ªŽ©“®§ŒäŠw‰ï‘æ13‰ñ’†‘Žx•”Šwpu‰‰‰ïCpp.216-217C’¹Žæ‘åŠwi’¹ŽæjC2004.11D
  166. ¬ù —²KC”öè ’mKCꎓ¡ „ŽjC¬¼ —º‰îDŽ©—¥Œ^’Ǐ]ƒVƒXƒeƒ€‚ÌŽÀ—p‰»‚ÉŠÖ‚·‚錤‹†DŒv‘ªŽ©“®§ŒäŠw‰ï‘æ21‰ñƒZƒ“ƒVƒ“ƒOƒtƒH[ƒ‰ƒ€Cpp.195-199C“Œ—m‘åŠwi“Œ‹žjC2004.9D
  167. ꎓ¡ „ŽjC”öè ’mKC¬¼ —º‰îDSampled Active Contour Model‚É‚æ‚é“®‰æ‘œ‚©‚ç‚̐O—̈æ‚ÌŽ©“®’ŠoDŒv‘ªŽ©“®§ŒäŠw‰ï‘æ21‰ñƒZƒ“ƒVƒ“ƒOƒtƒH[ƒ‰ƒ€Cpp.201-205C“Œ—m‘åŠwi“Œ‹žjC2004.9D
  168. ‘哇 ’¼–çCà_–ì WˆêC”öè ’mKCꎓ¡ „ŽjC¬¼ —º‰îDƒCƒ“ƒ^[ƒlƒbƒgEŒg‘Ñ“d˜b‚ð—˜—p‚µ‚½–h”ƃVƒXƒeƒ€‚ÌŒ¤‹†DŒv‘ªŽ©“®§ŒäŠw‰ï‘æ21‰ñƒZƒ“ƒVƒ“ƒOƒtƒH[ƒ‰ƒ€Cpp.419-423C“Œ—m‘åŠwi“Œ‹žjC2004.9D

  169. ꎓ¡ „ŽjCÂ–Ø Œö–çC‹àŽq –L‹vDMore Intelligent ScissorsD‰æ‘œ“dŽqŠw‰ïƒrƒWƒ…ƒAƒ‹ƒRƒ“ƒsƒ…[ƒeƒBƒ“ƒOƒ[ƒNƒVƒ‡ƒbƒv2003CˆÀŒ|ƒOƒ‰ƒ“ƒhƒzƒeƒ‹iL“‡jC2003.11D

  170. ¼‰Y ^ŽiCꎓ¡ „ŽjC‹àŽq –L‹vCŒIŽR ”ɁD•¨—ƒ‚ƒfƒ‹‚É‚æ‚é‰Ô‚̈ނê‚̃Vƒ~ƒ…ƒŒ[ƒVƒ‡ƒ“DîˆŠwƒOƒ‰ƒtƒBƒNƒX‚ÆCADŒ¤‹†•ñCVol.2002CNo.77Cpp.92-102Ciˆ¤’mjC2002.8D
  171. ꎓ¡ „ŽjCÂ–Ø Œö–çC‹àŽq –L‹vDŽ÷”çƒeƒNƒXƒ`ƒƒ‚É—LŒø‚ȃeƒNƒXƒ`ƒƒ“Á’¥—Ê‚ÌŒŸ“¢D‰æ‘œ‚Ì”FŽ¯E—‰ðƒVƒ“ƒ|ƒWƒEƒ€(MIRU2002)CVol.2002CNo.11Cpp.II-93 - II-98C–¼H‘åiˆ¤’mjC2002.7D

  172. ’r“c ‰ë•qCꎓ¡ „ŽjCÂ–Ø Œö–çC‹àŽq –L‹vDŽ÷”çƒeƒNƒXƒ`ƒƒ‚Æ—tŒ`ó‚ð—p‚¢‚½Ž÷–؉摜‚Ì”FŽ¯D“ŒŠC˜A‘åCp.384C–L‹´‹Z‰È‘åiˆ¤’mjC2001.11D
  173. ꎓ¡ „ŽjCÂ–Ø Œö–çC‹àŽq –L‹vD—̈敪Š„–@‚ÉŠî‚­‰Ô—̈æ‚ÌŽ©“®’ŠoDMŠw‘‘åCî•ñEƒVƒXƒeƒ€2Cp.228C—§–½ŠÙ‘åiŽ ‰êjC2001.3D

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  1. Yuki Takasaki, Kenta Hara, Tomoki Koyama and Takeshi Saitoh. Eigenlips based lip reading using Kinect sensor. Proc. of 2nd BMIRC International Symposium, p.52C2014.1D
  2. Tomoki Koyama and Takeshi Saitoh. Japanese sign language recognition using Kinect sensor. Proc. of 2nd BMIRC International Symposium, p.51C2014.1D
  3. Takeshi Saitoh. Lip reading for communication support. Proc. of 2nd BMIRC International Symposium, p.19C2014.1D
  4. Junki Iwagami and Takeshi Saitoh. Gaze point detection using inside-out camera. Proc. of 1st BMIRC International Symposium on Frontiers in Computational Systems Biology and Bioengineering, p.48C2013.2D
  5. Kai Inoue and Takeshi Saitoh. A study of real-time finger character recognition using Kinect. Proc. of 1st BMIRC International Symposium on Frontiers in Computational Systems Biology and Bioengineering, p.47C2013.2D

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  1. ‹àŽq –L‹vCÂ–Ø Œö–çCꎓ¡ „ŽjD’ʐMƒlƒbƒgƒ[ƒN‚ð’Ê‚¶‚½Ž©‘R‰æ‘œ”FŽ¯ƒVƒXƒeƒ€‚̍\’zD“d‹C’ʐM•‹yà’cŒ¤‹†’²¸•ñ‘‘æ‚P‚V†Cpp.602-607C2002D
  2. Takeshi SaitohCMasatoshi IkedaCand Toyohisa Kaneko. Computer Aided Diagnosis (CAD) System for Hepatic Cancer. Proc. of the First Symposium on Intelligent Human Sensing (IHSS2003)Cpp.174-179C2003.3D

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