⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩9,, ィ⟬࣓ࢹ࢕࢔ศ㔝 ࣓ࣥࣂ࣮ᩍᤵ㻌 㻌኱⏣㻌 ཭୍㻌෸ᩍᤵ㻌ட⏣㻌 ⬟ᡂ䠈໭ཎ㻌 ᱁㻌Ꮫ⏕኱Ꮫ㝔⏕ 20 ྡ㸪Ꮫ㢮⏕ 3 ྡ

 ᴫせ ᙜࢢ࣮ࣝࣉࡀᒓࡍࡿィ⟬᝟ሗᏛ◊✲㒊㛛ࡣ㸪ࠕ୰㛗ᮇⓗほⅬ࠿ࡽィ⟬⛉Ꮫࡢ◊✲ࢆᢤᮏⓗ࡟Ⓨᒎࡉࡏࡿ᩾᪂࡞᪉ἲࡢ㛤ᣅ◊✲ࢆ⾜࠺㒊㛛ࠖ࡜ࡋ࡚㸪2004 ᖺᗘ࡟Ⓨ㊊ࡋࡓ㒊㛛࡛࠶ࡾ㸪ே㛫♫఍࡜ࡑࡢ⎔ቃࢆ୺࡞ᑐ㇟࡜ࡍࡿ᪂ࡋ࠸ィ⟬⛉Ꮫࡢᯟ⤌ࡳࢆ๰ᡂࡋ㸪ࡑࡢᇶ┙ࢆ☜❧ࡍࡿࡇ࡜ࢆ┠ᶆ࡜ࡋ࡚◊✲άືࢆ᥎㐍ࡋ࡚࠸ࡿ㸬 ⣧⢋࡞ࢹ࣮ࢱฎ⌮ࡢຠ⋡ࡸ㏿ᗘࡀồࡵࡽࢀࡿ㏻ᖖࡢࢫ࣮ࣃ࣮ࢥࣥࣆ࣮ࣗࢱศ㔝࡜ࡣ㐪࠸㸪ே㛫࡟⧳ࢃࡿ᝟ሗࢆฎ⌮ᑐ㇟࡜ࡍࡿィ⟬⛉Ꮫ࡛ࡣ㸪᝟ሗฎ⌮ࡢ᫬㛫㍈ࢆே㛫࡟ྜࢃࡏࡿࡇ࡜ࡀᚲ㡲࡛࠶ࡿ㸬ࢢ࣮ࣟࣂࣝ࡟ᣑࡀࡿே㛫♫఍࡜ࡑࢀࢆྲྀࡾᕳࡃ⎔ቃ㸦⏕ά✵㛫ࡸ㒔ᕷ⎔ቃ࡞࡝㸧ࢆᑐ㇟࡜ࡋ࡚㸪ே㛫ࡢ᫬㛫㍈㸦ࡍ࡞ࢃࡕ㸪ࣜ࢔ࣝࢱ࢖࣒㸧࡟ἢࡗ࡚⭾኱࡞᝟ሗࢆฎ⌮ࡋ㸪ᐇほࢹ࣮ࢱ࡜ࢩ࣑࣮ࣗࣞࢩࣙࣥ⤖ᯝࡢ⼥ྜ᝟ሗࢆ㸪ே㛫࡟ศ࠿ࡾ᫆࠸ᙧ࡛ᥦ♧ࡋே㛫♫఍࡬ࣇ࢕࣮ࢻࣂࢵࢡࡍࡿࡓࡵ࡟ࡣ㸪ᐇୡ⏺ィ⟬᝟ሗᏛ࡜࿧ࡪ࡭ࡁ᪂ࡋ࠸ᯟ⤌ࡳࡀᚲせ࡜࡞ࡿ㸬 ලయⓗ࡟ࡣ㸪͆ᐇୡ⏺ࡢ᝟ሗࢆࢭࣥࢩࣥࢢࡍࡿᶵ⬟͇㸪͆⭾኱࡞᝟ሗࢆฎ⌮ࡍࡿ₶ἑ࡞ィ⟬ᶵ⬟͇㸪͆᝟ሗࢆ㑅ᢥ࣭⵳✚ࡍࡿ኱つᶍࢹ࣮ࢱ࣮࣋ࢫᶵ⬟͇ࢆ㸪ࢥࣥࣆ࣮ࣗࢱࢿࢵࢺ࣮࣡ࢡ
ୖ࡛⼥ྜࡍࡿࡇ࡜࡟ࡼࡾ኱つᶍ▱⬟᝟ሗ࣓ࢹ࢕࢔ࢆᵓ⠏ࡋ㸪ࡑࡢࣂࢵࢡ࣮࣎ࣥୖ࡛㸪ඛ➃ⓗせ⣲ᢏ⾡ࡢ◊✲㛤Ⓨ࡜㸪ࢽ࣮ࢬ࡟ᐦ╔ࡋࡓᛂ⏝ࢩࢫࢸ࣒ࡢ◊✲㛤Ⓨࢆ୪⾜ࡋ࡚㐍ࡵ࡚࠸ࡿ㸬 ◊✲ᡂᯝࡣ㸪◊✲ㄽᩥࡸᏛ఍Ⓨ⾲ࢆ୰ᚰ࡟බ㛤ࡋ࡚࠸ࡿࡀ㸪ࡑࢀࡔࡅ࡛࡞ࡃ㸪࢖ࣀ࣮࣋ࢩ࣭ࣙࣥࢪࣕࣃࣥ-኱Ꮫぢᮏᕷ➼ࡢᒎ♧ࡢᶵ఍ࡶ฼⏝ࡋ࡚ᗈሗάື࡟ࡶດࡵ࡚ࡁࡓ㸬ᮏᖺᗘࡢሗ࿌࡛ࡣ㸪ୗグࡢ㸲௳ࡢ◊✲࡟ࡘ࠸࡚ᴫせࢆㄝ᫂ࡍࡿ㸬࠙㸯ࠚᣑᙇ⌧ᐇឤ࡟ࡼࡿ㢦⾲᝟᧯సࢆ⏝࠸ࡓࣅࢹ࢜㏻ヰ᪉ᘧ㸸⏬ീฎ⌮ᢏ⾡ࢆ⏝࠸࡚ᣑᙇࡋࡓ㢦⾲᝟ࢆᣑᙇ⌧ᐇᥦ♧ࡍࡿࡇ࡜࡟ࡼࡾ㸪㇏࠿࡞⾲᝟⾲ฟࢆక࠺ᫎീࢥ࣑ࣗࢽࢣ࣮ࢩ࣓ࣙࣥࢹ࢕࢔ࢆᐇ⌧ࡍࡿ㸬㸦୺࡞◊✲㈝㸸⛉◊㈝ᣮᡓⓗⴌⱆ◊✲㸦໭ཎ㸧2010㹼2012 ᖺᗘ㸧࠙㸰ࠚ✵᧜⏬ീ࡜ࡢᑐᛂⅬ᝟ሗࢆ⏝࠸ࡓࣔࣂ࢖࣓ࣝ࢝ࣛࡢ఩⨨࣭ጼໃ᥎ᐃᡭἲ㸸⯟✵ᶵ࡞࡝࠿ࡽ᧜ᙳࡉࢀࡓ✵᧜⏬ീ࡜㸪ࢫ࣐࣮ࢺࣇ࢛ࣥ➼ࡢࣔࣂ࢖ࣝ➃ᮎ࡛᧜ᙳࡋࡓ⏬ീ㛫ࡢᑐᛂ
Ⅼ᝟ሗࢆ⏝࠸࡚㸪ࣔࣂ࢖ࣝ➃ᮎࡢ఩⨨࡜ጼໃࢆ᥎ᐃࡍࡿᡭἲࢆᐇ⌧ࡍࡿ㸬ᆅ⌮᝟ሗࢩࢫࢸ
࣒(GIS: Geographic Information System)ࡀ᭷ࡍࡿ✵᧜⏬ീࢹ࣮ࢱ࣮࣋ࢫ࡟㸪ᮏᡭἲࢆ㐺⏝ࡍࡿࡇ࡜࡟ࡼࡾ㸪ࣔࣂ࢖ࣝ➃ᮎࢆᣢࡗࡓ࣮ࣘࢨࡢ఩⨨࣭ጼໃࢆ᥎ᐃࡍࡿ㸬㸦୺࡞◊✲㈝㸸⛉◊㈝ⱝᡭ◊✲㸿㸦໭ཎ㸧2006㹼2008 ᖺᗘ㸧࠙㸱ࠚRGB-D ࣓࢝ࣛࢆ⏝࠸ࡓᩍ♧⪅ࡢసᴗࡢ AR ෌⾲♧㸸AR ᢏ⾡ࢆ⏝࠸࡚㸪ᐇ㝿ࡢసᴗ⎔ቃ࡟ྜࢃࡏ࡚ࢳ࣮ࣗࢺࣜ࢔ࣝࣅࢹ࢜୰ࡢᩍ♧⪅ࡢᵝᏊࢆ㸱ḟඖⓗ࡟෌⏕ࡍࡿࡇ࡜ࢆᥦ᱌
-166-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩ࡍࡿ㸬㸦୺࡞◊✲㈝㸸⛉◊㈝ᇶ┙◊✲(B)㸦ட⏣㸧2011㹼2013 ᖺᗘ㸧࠙㸲ࠚ᫬✵㛫ᣊ᮰ࢆ฼⏝ࡋࡓ㌴୧๓↷ⅉࡢ㊰ୖ཯ᑕ఩⨨᥎ᐃ㸸㐨㊰┘ど࣓࢝ࣛᫎീࢆ⏝࠸ࡓኪ㛫࡟࠾ࡅࡿ㌴୧఩⨨ࡢ 3 ḟඖ఩⨨᥎ᐃࢆᐇ⌧ࡍࡿ㸬 ◊✲ᡂᯝ࠙ࠚ ᣑᙇ⌧ᐇឤ࡟ࡼࡿ㢦⾲᝟᧯సࢆ⏝࠸ࡓࣅࢹ࢜㏻ヰ᪉ᘧ ே࡜ே࡜ࡢࢥ࣑ࣗࢽࢣ࣮ࢩ࡛ࣙࣥࡣ㸪ឤ᝟ࡸ㛵ᚰࢆ┦ᡭ࡟ṇ☜࡟ఏ࠼ࡿࡇ࡜ࡀ㔜せ࡛࠶ࡿࡀ㸪ゝㄒ᝟ሗࡔࡅ࡛ࡣ㸪ࡑࢀࡽࢆ༑ศఏ࠼ࡽࢀ࡞࠸ሙྜࡀ࠶ࡿ㸬ࡑࡢࡼ࠺࡞ሙྜ㸪ᡃࠎࡣ㸪⾲᝟㸪ࡋࡄࡉ㸪ኌࡢᢚᥭ࡜࠸ࡗࡓ㠀ゝㄒ᝟ሗࢆ⏝࠸ࡿࡇ࡜࡛㸪᝟ሗࢆ⿵ࡗ࡚࠸ࡿ㸬ࡑࡢࡓࡵ㸪఍ヰ┦ᡭࡢ⾲᝟⾲ฟࡀஈࡋ࠸ሙྜ࡟ࡣ㸪ࡑࡢឤ᝟ࡸ㛵ᚰࢆṇࡋࡃㄞࡳྲྀࡿࡇ࡜ࡀᅔ㞴࡜࡞ࡾ㸪⤖ᯝ࡜ࡋ࡚෇⁥࡞ࢥ࣑ࣗࢽࢣ࣮ࢩࣙࣥࡀጉࡆࡽࢀࡿࡇ࡜ࡀᠱᛕࡉࢀࡿ㸬ᡃࠎࡣ㸪⏬ീฎ⌮ᢏ⾡ࢆ⏝࠸࡚㢦ࡢ⾲᝟ࢆ᧯సࡋ㸪ࡑࢀࢆᣑᙇ⌧ᐇ㸦AR: Augmented Reality㸧ᥦ♧ࡍࡿࡇ࡜࡟ࡼࡾ㸪㇏࠿࡞⾲᝟⾲ฟࢆకࡗࡓᫎീࢥ࣑ࣗࢽࢣ࣮ࢩ࣓ࣙࣥࢹ࢕࢔ࡢᐇ⌧ࢆ┠ⓗ࡜ࡋࡓ◊✲࡟ྲྀࡾ⤌ࢇ࡛࠸ࡿ㸬 ᮏ◊✲࡛ᥦ᱌ࡍࡿࢩࢫࢸ࣒࡛ࡣ㸪Fig.1 ࡟♧ࡍࡼ࠺࡟㸪ᵝࠎ࡞⾲᝟⏬ീࢆ⏝࠸ࡓ஦๓Ꮫ⩦࡟ࡼࡾ㸪⾲᝟ኚ໬ࢆ⾲⌧ࡍࡿࣃ࣓ࣛࢺࣜࢵࢡ✵㛫ࢆᵓ⠏ࡋ㸪ධຊࡉࢀࡓ㢦⏬ീࢆࡑࡢ✵㛫࡟ᑕᙳࡋ࡚ᚓࡽࢀࡿࣃ࣓࣮ࣛࢱࢆ᧯సࡍࡿࡇ࡜࡛㸪⾲᝟᧯సࢆᐇ⌧ࡍࡿ㸬⾲᝟㆑ูฎ⌮ࢆ⾜࠺ࡇ࡜࡞ࡃ㸪⾲᝟ࡢ᧯సࡀྍ⬟࡛࠶ࡿࡓࡵ㸪ᚤ⣽࡞⾲᝟ኚ໬࡟ࡶᑐᛂྍ⬟࡜࠸࠺≉㛗ࢆ᭷ࡍࡿ㸬᧜ᙳ࠿ࡽᥦ♧ࡲ࡛ࡢฎ⌮ࢆࣜ࢔ࣝࢱ࢖࣒ᐇ⾜ࡍࡿࡇ࡜࡟ࡼࡾ㸪ࣅࢹ࢜㏻ヰࢩࢫࢸ࣒࡬ࡢᛂ⏝ࢆヨࡳࡿ㸬

Fig. 1 ⾲᝟᧯సࡢ࢔ࣉ࣮ࣟࢳ-167-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩

Fig. 2 ᥦ᱌ᡭἲࡢฎ⌮ࡢὶࢀ ᮏ◊✲࡛ࡣ㸪Fig.2 ࡟♧ࡍࡼ࠺࡟㸪஧✀㢮ࡢ Point Distribution Models (PDM) [1]ࢆ⏝࠸࡚㸪㢦ࡢᙧ≧࡜ࢸࢡࢫࢳࣕࡢࣃ࣓ࣛࢺࣜࢵࢡ⾲᝟✵㛫ࢆ⏕ᡂࡋ㸪⾲᝟᧯సࢆᐇ⌧ࡍࡿ㸬⾲᝟ࢆࣃ࣓ࣛࢺࣜࢵࢡ✵㛫࡟ᑕᙳࡍࡿࡇ࡜࡟ࡼࡾ㸪࠶ࡿ⾲᝟ࢆࣃ࣓ࣛࢺࣜࢵࢡ✵㛫࡟࠾ࡅࡿ୍Ⅼ࡜ࡋ࡚⪃࠼ࡿࡇ࡜ࡀ࡛ࡁࡿ㸬࠶ࡿ⾲᝟Ⅼ࠿ࡽ࠶ࡿ⾲᝟Ⅼ࡬ࡢ࣋ࢡࢺࣝࡢ㛗ࡉࢆ᧯సࡍࡿࡇ࡜࡟ࡼࡾ㸪⾲᝟㆑ูฎ⌮ࢆ⾜࠺ࡇ࡜࡞ࡃ㸪㢦⾲᝟ኚ໬ࢆ᧯సࡍࡿࡇ࡜ࡀྍ⬟࡜࡞ࡿ㸬ࡲࡓ㸪ࡇࡢᡭἲࢆ⏝࠸ࢀࡤ㸪ᇶᮏⓗ࡞஬ࡘࡢ⾲᝟㸦㦫ࡁ㸪႐ࡧ㸪ᜍᛧ㸪ᝒࡋࡳ㸪᎘ᝏ㸧࡟ᒓࡉ࡞࠸᭕᫕࡞⾲᝟ࡢቑᙉࡶྍ⬟࡛࠶ࡿ㸬 㢦ᙧ≧ࡣ㸪Constrained Local Models (CLM) [2]ࢆ⏝࠸࡚㢦ᙧ≧ࢆ᥎ᐃࡋ㸪ࣃ࣓ࣛࢺࣜࢵࢡ✵㛫ࢆᵓ⠏ࡍࡿ㸬ධຊࡉࢀࡓࣅࢹ࢜఍ヰᫎീ࡟ CLM ࢆ㐺⏝ࡋ࡚ࣜ࢔ࣝࢱ࢖࣒࡛㢦ᙧ≧ࡢ࣋ࢡࢺࣝࢆ᥎ᐃࡋ㸪஦๓࡟ồࡵ࡚࠾࠸ࡓᇶ‽⾲᝟ࡢᙧ≧࣋ࢡࢺࣝࢆ᥎ᐃࡋ㸪ࡑࢀࢆ㄂ᙇࡍࡿࡼ࠺࡞᧯సࢆ᪋ࡍࡇ࡜࡛㢦ࡢᙧ≧ࡢቑᙉࢆ⾜࠺㸬⾲᝟⾲ฟ࡟క࠺ࢸࢡࢫࢳࣕࡢኚ໬ࡣ㸪
Active Appearance Models (AAM)[11]ࢆ⏝࠸࡚ࣃ࣓ࣛࢺࣜࢵࢡ✵㛫ࢆᵓ⠏ࡋ㸪⾲᝟᧯సࢆᐇ⌧ࡍࡿ㸬ධຊ⏬ീࢆ AAM ࡢࣃ࣓ࣛࢺࣜࢵࢡ✵㛫࡟ᑕᙳࡋ㸪஦๓࡟ồࡵ࡚࠾࠸ࡓᇶ‽⾲᝟ࡢᑕᙳⅬ࡜ࡢ㛫࡛୚࠼ࡽࢀࡿ࣋ࢡࢺࣝࢆ᧯సࡍࡿࡇ࡜࡛㸪㢦ࡢࢸࢡࢫࢳࣕኚ໬ࢆ᧯సࡍࡿ㸬 ᥦ᱌ᡭἲࢆࣅࢹ࢜㏻ヰࢩࢫࢸ࣒࡟㐺⏝ࡍࡿࡇ࡜࡟ࡼࡾ㸪Fig. 3 ࡟♧ࡍ㸪㇏࠿࡞⾲᝟ࢆᥦ♧ྍ⬟࡞㏻ヰࢩࢫࢸ࣒ࢆᐇ⿦ࡍࡿ㸬ᕥ࡟⾲᝟ቑᙉࢆ⏝࠸ࡓࣅࢹ࢜㏻ヰࡢ୍౛ࡀ♧ࡉࢀ࡚࠾ࡾ㸪ྑୗ࡟㏻ヰࡢᵝᏊࢆ᧜ᙳࡋࡓ⏬ീࡀ♧ࡉࢀ࡚࠸ࡿ㸬-168-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩Fig.3 ⾲᝟᧯సࢆ⏝࠸ࡓࣅࢹ࢜㏻ヰࢩࢫࢸ࣒[1]Cootes T. F., Taylor C. J. “Active shape models—‘smart snakes’.” BMVC. (1992):266-275.
[2] Cristinacce D., Cootes T. F. “Feature Detection and Tracking with Constrained LocalModels.” BMVC. (2006): 929-938.
࠙ࠚ ✵᧜⏬ീ࡜ࡢᑐᛂⅬ᝟ሗࢆ⏝࠸ࡓࣔࣂ࢖࣓ࣝ࢝ࣛࡢ఩⨨࣭ጼໃ᥎ᐃᡭἲ GPS㸦Global Positioning System㸧ࢆᦚ㍕ࡋࡓࢫ࣐࣮ࢺࣇ࢛ࣥࡸࢱࣈࣞࢵࢺ➃ᮎ㸪ࢥࣥࣃࢡࢺࢹࢪࢱ࣓ࣝ࢝ࣛ࡞࡝ࡢࣔࣂ࢖ࣝ➃ᮎࡢᬑཬࢆ⫼ᬒ࡜ࡋ࡚㸪࣮ࣘࢨࡢ఩⨨࣭᪉఩᝟ሗࢆ⏝࠸ࡓ⾤ゅ᱌ෆࢩࢫࢸ࣒ࡀᐇ⏝໬ࡉࢀ࡚࠸ࡿ㸬୍⯡ⓗ࡞ GPS ࡢ఩⨨᝟ሗ࡟ࡣ㸪ᩘ࣓࣮ࢺࣝ⛬ᗘࡢㄗᕪࡀྵࡲࢀࡿ㸬ࡉࡽ࡟㸪㒔ᕷ㒊࡞࡝ࡢᘓ≀ࡀᯘ❧ࡍࡿᆅⅬ࡛ࡣ㸪GPS ⾨ᫍࡢ㟁Ἴࡢ࣐ࣝࢳࣃࢫࡢᙳ㡪࡛ィㄗᕪࡀቑ኱ࡋ㸪ࡑࡢ⤖ᯝㄗࡗࡓ᝟ሗᥦ♧ࡀ⾜ࢃࢀࡿၥ㢟ࡀᏑᅾࡍࡿ㸬⮬ື㌴࡟ᦚ㍕ࡉࢀࡓ GPS ࡢሙྜ㸪᫬⣔ิࡢ⟬ฟࡸィ఩࡟ࡼࡾ㐍⾜᪉ྥࢆ᥎ᐃࡋ㸪᪉఩᝟ሗㄗᕪࡢ⿵ṇࢆ⾜ࡗ࡚࠸ࡿࡀ㸪ࣔࣂ࢖ࣝ➃ᮎࡢሙྜ㸪࣮ࣘࢨࡣ❧ࡕṆࡲࡿ࠿ప㏿࡛Ṍ⾜ࡋ࡚࠸ࡿࡇ࡜ࡀከ࠸ࡓࡵ㸪᫬⣔ิほࡢ฼⏝ࡀᅔ㞴࡛࠶ࡿ㸬㟁Ꮚࢥࣥࣃࢫࢆే⏝ࡍࡿࡇ࡜ࡀ୍ࡘࡢゎỴ⟇࡛࠶ࡿࡀ㸪㟁Ꮚࢥࣥࣃࢫࡢ᪉఩᝟ሗ࡟ࡣ 0㹼5 ᗘ࡯࡝ࡢㄗᕪࡀྵࡲࢀ㸪⎔ቃ࡟ࡼࡗ࡚ࡣ࿘ᅖࡢ㟁Ꮚᶵჾ࡞࡝࡛Ⓨ⏕ࡋࡓ☢ሙ࡟ࡼࡾㄗᕪࡀ⏕ࡌࡿࡇ࡜ࡀ▱ࡽࢀ࡚࠸ࡿ㸬 ㏆ᖺ㸪Google Earth ࢆ௦⾲࡜ࡋ࡚㸪ேᕤ⾨ᫍࡸ⯟✵ᶵ࠿ࡽᆅୖࢆ᧜ᙳࡋࡓ✵᧜⏬ീࡢධᡭࡀᐜ᫆࡟࡞ࡾࡘࡘ࠶ࡿ㸬ᆅ⌮᝟ሗࢩࢫࢸ࣒㸦GIS: Geographic Information System㸧ࡢ✵᧜⏬ീࢹ࣮ࢱࡣ㸪ྛ⏬⣲࡟఩⨨᝟ሗࢆྵࢇ࡛࠸ࡿࡓࡵ㸪✵᧜⏬ീ࡜ࣔࣂ࢖࣓ࣝ࢝ࣛ⏬ീ࡜ࡢᑐᛂ㛵ಀࢆ᥎ᐃ࡛ࡁࢀࡤ㸪ṇ☜࡞ࣔࣂ࢖࣓ࣝ࢝ࣛࡢ఩⨨࣭ጼໃࢆồࡵࡿࡇ࡜ࡀྍ⬟࡜࡞ࡿ㸬
ᮏⓎ⾲࡛ࡣ㸪఩⨨᝟ሗ௜ࡁ✵᧜⏬ീ࡜ࢫ࣐࣮ࢺࣇ࢛ࣥࡸࢱࣈࣞࢵࢺ➃ᮎ➼ࡢࣔࣂ࢖ࣝ➃ᮎ࡛᧜ᙳࡋࡓ⏬ീࢆᑐᛂⅬ᥈⣴ࡍࡿࡇ࡜࡟ࡼࡾ㸪Fig.4 ࡟♧ࡍࡼ࠺࡟㸪ࣔࣂ࢖ࣝ➃ᮎࡢ఩⨨࣭ጼໃࢆ⏬ീ᝟ሗ࡟ᇶ࡙ࡁ᥎ᐃࡍࡿᡭἲࢆᥦ᱌ࡍࡿ㸬-169-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩
Fig.4 ✵᧜⏬ീ࡜ࡢᑐᛂⅬ᝟ሗࢆ⏝࠸ࡓฎ⌮࣮ࣘࢨࡣ㸪ࣔࣂ࢖࣓ࣝ࢝ࣛࢆ⏝࠸࡚┠ࡢ๓ࡢ㢼ᬒࢆ᧜ᙳࡍࡿ㸬ࣔࣂ࢖࣓ࣝ࢝ࣛ⏬ീ࡜✵᧜⏬ീࡣ㸪ྠ୍✵㛫ࢆ᧜ᙳࡋ࡚࠸ࡿࡶࡢࡢ㸪᧜ᙳゅᗘࡀ኱ࡁࡃ␗࡞ࡿࡓࡵᑕᙳṍࡳࡀⓎ⏕ࡋ㸪⏬ീୖ࡛ࡢぢ࠼᪉ࢆࡑࡢࡲࡲ⏝࠸࡚ᑐᛂⅬ᥈⣴ࢆ⾜ࡗ࡚ࡶ㸪ṇ☜࡞ᑐᛂⅬࢆồࡵࡿࡇ࡜ࡀᅔ㞴࡛࠶ࡿ㸬ࡑࡇ࡛ᮏᡭἲ࡛ࡣ㸪ࣔࣂ࢖࣓ࣝ࢝ࣛ⏬ീ࠿ࡽ௬᝿ಠ▔⏬ീࢆ⏕ᡂࡋ㸪୧⪅ࡢどⅬ఩⨨ࢆ௬᝿ⓗ࡟୍⮴ࡉࡏࡿ㸬஦๓࡟ồࡵ࡚࠾࠸ࡓ࣓࢝ࣛࡢෆ㒊ࣃ࣓࣮ࣛࢱ࡜㸪᧜ᙳ᫬࡟ࣔࣂ࢖ࣝ➃ᮎ࡟ෆⶶࡉࢀࡓຍ㏿ᗘࢭࣥࢧ࠿ࡽᚓࡓ㔜ຊ᝟ሗ࡟ᇶ࡙࠸࡚㸪᧜ᙳࡋࡓ⏬ീࢆୖ✵࠿ࡽ┿ୗࢆぢୗࢁࡋࡓಠ▔どⅬ࠿ࡽࡢぢ࠼᪉࡟ᑕᙳኚ᥮ࡋ㸪௬᝿ಠ▔⏬ീࢆ⏕ᡂࡍࡿ㸬ࡇࡢ௬᝿ಠ▔⏬ീ࡜㸪⯟✵ᶵ࡞࡝࠿ࡽᐇ㝿࡟┿ୗࢆ᧜ᙳࡋࡓ✵᧜⏬ീ࡟࠾࠸࡚ SIFT[3]࡞࡝
ࡢ⏬ീ≉ᚩ㔞ࢆ⏝࠸ࡓᑐᛂⅬ᥈⣴ฎ⌮ࢆ⾜࠸㸪ࡑࡢ⤖ᯝ࠿ࡽ㸪୧⏬ീ㛫ࡢ࣍ࣔࢢࣛࣇ࢕⾜ิࢆ⟬ฟࡍࡿ㸬ồࡵࡽࢀࡓ࣍ࣔࢢࣛࣇ࢕⾜ิࢆ⏝࠸࡚㸪ࣔࣂ࢖࣓ࣝ࢝ࣛࡢ఩⨨࡜ጼໃࢆ᥎ᐃࡍࡿ㸬 Fig.5 ࡟♧ࡍ↓ே⯟✵ᶵ㸦UAV: Unmanned Aerial Vehicle㸧࡛᧜ᙳࡋࡓẚ㍑ⓗ㧗ゎീᗘ࡞✵᧜⏬ീࢆ⏝࠸࡚㸪ࣔࣂ࢖࣓ࣝ࢝ࣛࡢ఩⨨ጼໃ᥎ᐃᐇ㦂ࢆ⾜ࡗࡓ㸬✵᧜⏬ീࢆẁ㝵ⓗ࡟ࣜࢧ࢖ࢬࡋ㸪✵᧜⏬ീࡢ✵㛫ゎീᗘ࡜㸪ࣔࣂ࢖࣓ࣝ࢝ࣛࡢ఩⨨ጼໃ᥎ᐃࡢ⢭ᗘ࡜ࡢ┦㛵㛵ಀࢆ᳨ドࡋࡓ㸬-170-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩Fig.5 ᧜ᙳ࡟⏝࠸ࡓ 8$9 ࡑࡢ⤖ᯝ㸪✵᧜⏬ീࡢ✵㛫ゎീᗘࡀ 6 cm/⏬⣲ࡼࡾపࡃ࡞ࡿ࡜࣍ࣔࢢࣛࣇ࢕⾜ิࡢ⟬ฟ࡟ኻᩋࡋࡸࡍࡃ࡞ࡿࡇ࡜ࡀࢃ࠿ࡿ㸬ࡇࡢ⤖ᯝࡣ㸪఩⨨ጼໃ᥎ᐃࡢㄗᕪࡀ኱ࡁࡃ࡞ࡿ✵㛫ゎീᗘ࡜ࡶ࡯ࡰ୍⮴ࡋ࡚࠸ࡿ㸬 ᦠᖏ➃ᮎୖ࡟ᥦ᱌ࢩࢫࢸ࣒ࢆᐇ⿦ࡋࡓ㸬ᐇ⿦ࡋࡓࢩࢫࢸ࣒ࡢࣈࣟࢵࢡᅗࢆ Fig.6 ࡟♧ࡍ㸬࣓࢝ࣛ௜ࡁᦠᖏ➃ᮎ࡜ࡋ࡚ ASUS ♫ࡢ Android ࢱࣈࣞࢵࢺ PC ࡛࠶ࡿ ASUS Pad TF700T
㸦Android 4.2.1㸧ࢆ⏝࠸ࡓ㸬➃ᮎ࡟ࡣ GPS ࡜ຍ㏿ᗘࢭࣥࢧࡀᦚ㍕ࡉࢀ࡚࠸ࡿ㸬ᒇእ࡛ࡢ᧜ᙳ᫬࡟ࡣ㸪ࣔࣂ࢖ࣝ Wi-Fi ࣮ࣝࢱ࡞࡝ࢆ⏝࠸࡚࢖ࣥࢱ࣮ࢿࢵࢺ࡟᥋⥆ࡉࢀ࡚࠸ࡿᚲせࡀ࠶ࡿ㸬㛤Ⓨ࡟⏝࠸ࡓ OS ࡣ Windows 8 Pro㸪⤫ྜ㛤Ⓨ⎔ቃࡣ Eclipse 4.2㸪౑⏝ゝㄒࡣ Java㸪
㛤Ⓨࢶ࣮ࣝ࡜ࡋ࡚ OpenCV4Android SDK ver.2.4.3 ࢆ౑⏝ࡋࡓ㸬✵᧜⏬ീࡢྲྀᚓ࡟ࡣ
Google Static Maps API ver.2 ࢆ⏝࠸ࡓ㸬᧜ᙳ࡜ྠ᫬࡟ GPS ࡢ఩್ࢆྲྀᚓࡋ㸪ࡑࡢ GPS఩್ࢆྵࢇࡔ URL ࣃ࣓࣮ࣛࢱࢆ HTTP ࣜࢡ࢚ࢫࢺ࡛㏦ಙࡍࡿࡇ࡜࡛㸪ࡑࡢ㏉ಙ࡜ࡋ࡚᧜ᙳᆅⅬ࿘㎶ࡢ Google Map ࡢ✵᧜⏬ീࢆᚓࡿࡇ࡜ࡀ࡛ࡁࡿ㸬ຍ㏿ᗘࢭࣥࢧࡢ್࠿ࡽ௬᝿ಠ
▔⏬ീࢆ⏕ᡂࡋ㸪✵᧜⏬ീ࡜ࡢ㛫࡛≉ᚩⅬ࣐ࢵࢳࣥࢢࢆ⾜࠺㸬Fig.6 ᦠᖏ➃ᮎୖ࡟ᐇ⿦ࡋࡓࢩࢫࢸ࣒ࡢࣈࣟࢵࢡᅗ
-171-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩Fig.7 ࡟㸪࢔ࣉࣜࢣ࣮ࢩࣙࣥࡢᐇ⾜⏬㠃ࢆ♧ࡍ㸬Capture ࣎ࢱࣥࢆࢱࢵࢳࡋࡓ࡜ࡁ࡟ GPS఩್࠿ࡽ✵᧜⏬ീࢆྲྀᚓࡋ௬᝿ಠ▔⏬ീ࡜≉ᚩⅬ࣐ࢵࢳࣥࢢࢆ⾜࠺㸬ࢫࢸ࢕ࢵࢳࣥࢢฎ⌮ࡀᚲせ࡞ሙྜ࡟ࡣ㸪Capture ࣎ࢱࣥࢆࢱࢵࢳࡍࡿ๓࡟㸪どⅬࢆኚ࠼࡚ pre-Capture ࣎ࢱࣥఱᅇ࠿ࢱࢵࢳࡍࡿࡇ࡜࡛௬᝿ಠ▔⏬ീࢆ㏣ຍࡋ㸪ᗈᇦ௬᝿ಠ▔⏬ീࢆ⏕ᡂࡍࡿࡇ࡜ࡀ࡛ࡁࡿ㸬ࡲࡓ㸪Show Stitched VTV Image ࣎ࢱࣥࢆࢱࢵࢳࡋ࡚ᗈᇦ௬᝿ಠ▔⏬ീࢆ☜ㄆࡍࡿࡇ࡜ࡀ࡛ࡁࡿ㸬≉ᚩⅬ࣐ࢵࢳࣥࢢ࡟ᡂຌࡍࢀࡤ㸪ࣔࣂ࢖࣓ࣝ࢝ࣛ⏬ീ࡜✵᧜⏬ീ࡜ࡢ㛫ࡢ࣍ࣔࢢࣛࣇ࢕⾜ิࢆ⟬ฟࡍࡿࡇ࡜ࡀ࡛ࡁࡿ㸬ࡇࡢ࢔ࣉࣜࢣ࣮ࢩ࡛ࣙࣥࡣ㸪ࡑࡢ࣍ࣔࢢࣛࣇ࢕⾜ิࢆ⏝࠸࡚㸪Aerial ࣎ࢱࣥࢆࢱࢵࢳࡍࢀࡤ✵᧜⏬ീࢆ㸪RoadMap ࣎ࢱࣥࢆࢱࢵࢳࡍࢀࡤᆅᅗ⏬ീࢆࣔࣂ࢖࣓ࣝ࢝ࣛ⏬ീ࡟㔜␚ᥦ♧ࡉࡏࡿࡇ࡜ࡀྍ⬟࡛࠶ࡿ㸬

Fig7. ᐇ⿦ࡋࡓ࢔ࣉࣜࢣ࣮ࢩࣙࣥࡢᐇ⾜⏬㠃
[3] D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints,"
International Journal of Computer Vision (IJCV), pp91-110, 2004.
࠙ࠚ RGB-D ࣓࢝ࣛࢆ⏝࠸ࡓᩍ♧⪅ࡢసᴗࡢ AR ෌⾲♧ᩍ♧⪅ࡀ࠸࡞࠸సᴗ⌧ሙ࡟࠾࠸࡚㸪సᴗࢆᏛ⩦ࡍࡿ᫬㸪Ꮫ⩦⪅ࡣࢳ࣮ࣗࢺࣜ࢔ࣝࣅࢹ࢜ࢆぢ࡚ࣅࢹ࢜ෆࡢᩍ♧⪅ࡢసᴗ࡜ᐇ㝿ࡢసᴗ⎔ቃࡢᑐᛂ௜ࡅࢆ┠ど࡛☜ㄆࡍࡿࡇ࡜ࡀ࠶ࡿ㸬ᮏ◊✲࡛ࡣ AR ᢏ⾡ࢆ⏝࠸࡚㸪ᐇ㝿ࡢసᴗ⎔ቃ࡟ྜࢃࡏ࡚ࢳ࣮ࣗࢺࣜ࢔ࣝࣅࢹ࢜୰ࡢᩍ♧⪅ࡢᵝᏊࢆ㸱ḟඖⓗ࡟෌⏕ࡍࡿࡇ࡜ࢆᥦ᱌ࡍࡿ㸬ᮏ᪉ἲ࡛ࡣ㸪ᩍ♧⪅ࡢసᴗグ㘓᫬࡟୍ྎࡢRGB-D ࣓࢝ࣛࢆᡭ࡛ᵓ࠼࡚᧜ᙳࡍࡿ㸬ᥦ᱌ᡭἲࡣ KinectFusion ࢆ࣮࣋ࢫ࡜ࡋ㸪ࡑࢀ࡟ࡼࡿグ㘓ࡋࡓసᴗ⎔ቃ࡜⌧ᅾࡢసᴗ⎔ቃࡢࣞࢪࢫࢺ࣮ࣞࢩࣙࣥ⤖ᯝࢆ฼⏝ࡍࡿࡇ࡜࡛㸪Ꮫ⩦⪅࡟ᑐࡋ࡚ྠࡌసᴗ⎔ቃࡢୖ࡟ᩍ♧⪅ࡢ 3 ḟඖⓗ࡞సᴗࢆ㔜ࡡྜࢃࡏ࡚ AR ෌⾲♧ࢆᐇ⌧ࡍࡿ㸬సᴗ࡟ྜࢃࡏ࡚ὀ┠どⅬࢆ᥮࠼࡞ࡀࡽసᴗᵝᏊࢆグ㘓ࡋ࡚㸪ὀ┠どⅬࡢ࿘ࡾ࡛ࡑࡢᵝᏊࢆ୕ḟඖⓗ࡟෌⾲♧࡛ࡁࢀࡤ㸪సᴗ☜ㄆ࡟ຠᯝⓗ࡛࠶ࢁ࠺࡜⪃࠼࡚࠸ࡿ㸬ࡑࡇ࡛㸪Fig.8 ࡢࡼ࠺࡞㸪RGB-D ࣓࢝ࣛ 1 ྎࡢࡳࢆ⏝࠸ࡓసᴗ⌧ሙ࡟࠾ࡅࡿᩍ♧⪅ࡢసᴗᵝᏊࡢ⋓ᚓ࡜ AR ࡟-172-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩ࡼࡿ෌⾲♧ࢩࢫࢸ࣒ࢆᥦ᱌ࡍࡿ㸬ࡇࡢࢩࢫࢸ࣒ࡣ୍ྎࡢ RGB-D ࣓࢝ࣛࢆ⏝࠸࡚஦๓࡟సᴗ
ࢩ࣮ࣥ࡟࠾ࡅࡿᩍ♧⪅ࡢసᴗᵝᏊࢆグ㘓ࡋ࡚࠾ࡃ㸬ᩍ♧⪅ࡀసᴗ⌧ሙ࡟࠸࡞ࡃ࡜ࡶ㸪Ꮫ⩦⪅ࡣྠࡌసᴗ⌧ሙࡢୖ࡟ᩍ♧⪅ࡢ 3 ḟඖⓗ࡞సᴗᵝᏊࢆ㔜ࡡྜࢃࡏ࡚ AR ෌⾲♧࡛ࡁࡿ㸬

Fig. 8: AR ෌⾲♧ࡢᴫᛕᥦ᱌ᡭἲࡣ KinectFusion ࢆ࣮࣋ࢫ࡜ࡋ㸪୍ྎࡢ RGB-D ࣓࢝ࣛࡢࡳ⏝࠸࡚㸪సᴗࢩ࣮ࣥࡢグ㘓࡜ AR ෌⏕ࢆ⾜࠺㸬Fig.9 ࡟ᥦ᱌ࢩࢫࢸ࣒ࡢᵓᡂࢆ♧ࡍ㸬ᥦ᱌ࡋࡓࢩࢫࢸ࣒ࡣ኱ࡁࡃసᴗࢩ࣮ࣥࡢグ㘓࡜ AR ⾲♧ࡢ 2 ࡘࡢࢫࢸࢵࣉ࡟ศ࠿ࢀࡿ㸬᭱ึࡢసᴗࢩ࣮ࣥࡢグ㘓ࡢࢫࢸࢵࣉ࡟࠾࠸࡚ࡣ㸪ࡲࡎ㸪RGB-D ࣓࢝ࣛࢆ⏝࠸࡚ධຊࢹ࣮ࢱࢆྲྀᚓࡍࡿ㸬ḟ࡟㟼ⓗ࡞సᴗ⎔ቃࢆ⾲ࡍసᴗ⎔ቃࣔࢹࣝ࡜ࡢᙧ≧ẚ㍑࡟ࡼࡾ㸪ᗄఱᙧ≧ࡢᩚྜᛶࡀྲྀࢀࡓ㟼ⓗ࡞せ⣲࡜㸪ᩚྜᛶࡀྲྀࢀ࡞࠿ࡗࡓືⓗ࡞せ⣲࡟ศ๭ࡍࡿ㸬㟼ⓗ࡞せ⣲ࢆసᴗ⎔ቃࣔࢹࣝ࡟⤫ྜࡋ㸪⵳✚ࡉࢀࡓ㟼ⓗ࡞సᴗ⎔ቃࣔࢹࣝࢆ᭦᪂ࡍࡿ㸬ࡑࢀ࡜ྠ᫬࡟ྛࣇ࣮࣒ࣞࡢືⓗ࡞せ⣲ࢆ㸪ᩍ♧⪅ࡢసᴗᵝᏊࢆ⾲ࡍⅬ⩌࡜ࡍࡿ㸬AR ෌⾲♧ࡢࢫࢸࢵࣉ࡟࠾࠸࡚ࡣ㸪ࡲࡎ㸪AR ⾲♧ࡣグ㘓᫬࡛⋓ᚓࡋࡓ㟼ⓗ࡞⎔ቃࡢࣔࢹࣝ࡜ᐇ㝿ࡢసᴗ⎔ቃࡢཧ↷࡟ࡼࡾ㸪࣓࢝ࣛࡢ఩⨨ጼໃࢆ⟬ฟࡍࡿ㸬 ࡑࡢୖ࡛㸪ࢹ࢕ࢫࣉࣞ࢖ࢩ࣮ࢫ࣮ࣝࣅࢹ࢜ୖ࡛⋓ᚓࡋࡓᩍ♧⪅ࡢసᴗᵝᏊࡢⅬ⩌ࢆᐇ㝿ࡢసᴗ⎔ቃ࡟ྜࢃࡏ࡚㔜␚⾲♧ࡍࡿ㸬AR ෌⾲♧ࡍࡿ㝿࡟ࡣ㸪Ꮫ⩦⪅ࡀどⅬࡢ᧯స࡟ࡼࡾ㸪ඖࡢ࣓࢝ࣛ఩⨨࠿ࡽࡼࡾⰋ࠸═ࡵ࠿ࡽࡑࡢసᴗᵝᏊཬࡧ┦஫స⏝ࢆほᐹ࡜⌮ゎ࡛ࡁࡿࡼ࠺࡟࡞ࡿ㸬

Fig. 9: ฎ⌮ࡢᵓᡂ

-173-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩⵳✚ࡉࢀࡓ㟼ⓗ࡞సᴗ⎔ቃࡣ volume ࡜ࡋ࡚ಖᏑࡉࢀࡿ㸬ᩍ♧⪅ࡢసᴗᵝᏊࡣⅬ⩌ࢫࢺ࣮࣒ࣜ࡜ࡋ࡚ಖᏑࡉࢀࡿ㸬ྠࡌసᴗ⎔ቃ࡛ࡑࡢᩍ♧⪅ࡢసᴗᵝᏊࢆ෌⾲♧ࡍࡿ㝿࡟ࡣ㸪ᡃࠎࡣࡍ࡛࡟ಖᏑࡉࢀࡓ㟼ⓗ࡞సᴗ⎔ቃࢆㄞࡳ㎸ࡳ㸪Ꮫ⩦⪅ࡀ࣓࢝ࣛࢆᵓ࠼㸪సᴗ⎔ቃ࡟ᑐࡋ࡚ࣞࢪࢫࢺ࣮ࣞࢩࣙࣥࡋ㸪⌧ᅾࡢసᴗ⎔ቃࡢࣔࢹࣝ࡜ᩚิࡉࡏࡿࡓࡵࡢ࣐ࢵࢳࣥࢢࢆ⾜࠺㸬ࡑࡢୖ࡛㸪ᩍ♧⪅ࡢసᴗᵝᏊࢆ⌧ᅾࡢసᴗ⎔ቃࡢୖ࡟㔜␚⾲♧ࡍࡿ㸬Fig.10(ᕥᅗࡀᐇ㝿ࡢ⎔ቃ㸪ྑᅗࡀ AR ෌⾲♧⤖ᯝ)ࡣ AR ෌⾲♧ࡢ⤖ᯝࢆ♧ࡋ࡚࠸ࡿ㸬Fig.10 ࡛ࡣ㸪AR ෌⾲♧ࡍࡿ㝿㸪⋓ᚓࡋࡓᩍ♧⪅ࡢసᴗᵝᏊࡀ఩⨨ṇࡋࡃ෌⾲♧ࡉࢀ㸪⼥ྜࡋࡓᫎീࡀᚓࡽࢀ࡚࠸ࡿ㸬࣓࢝ࣛࡢ఩⨨ጼໃࡀᖖ࡟᥎ᐃࡉࢀ࡚࠸ࡿࡢ࡛㸪Ꮫ⩦⪅ࡣ࣓࢝ࣛࢆື࠿ࡍࡇ࡜࡛㸪Ꮫ⩦⪅ࡢほᐹどⅬ࡟ྜࢃࡏࡓࡼࡾⰋ࠸═ࡵࡢᫎീࢆぢࡽࢀࡿ㸬Fig. 10: ᐇ㦂⤖ᯝ (left: source, right: synthesized)࠙ࠚ ᫬✵㛫ᣊ᮰ࢆ฼⏝ࡋࡓ㌴୧๓↷ⅉࡢ㊰ୖ཯ᑕ఩⨨᥎ᐃᮏ◊✲࡛ࡣ㸪㐨㊰┘ど࣓࢝ࣛᫎീࢆ⏝࠸ࡓኪ㛫࡟࠾ࡅࡿ㌴୧఩⨨ࡢ 3 ḟඖ఩⨨᥎ᐃᡭἲࢆᥦ᱌ࡍࡿ㸬ኪ㛫ࡣ㌴୧ࡢ๓↷ⅉࡀⅬⅉࡋ࡚࠸ࡿࡓࡵ㸪㐨㊰┘ど࣓࢝ࣛᫎീ࡟࠾࠸࡚๓↷ⅉࡣᏳᐃࡋࡓほࡀྍ⬟࡛࠶ࡿ㸬ࡋ࠿ࡋ㸪⏬ീୖ࡛ࡢ๓↷ⅉ఩⨨ࡢࡳ࡛ࡣ㸪㌴୧ࡢ 3 ḟඖ఩⨨᥎ᐃࢆ⾜࠺ࡇ࡜ࡀ࡛ࡁ࡞࠸㸬ᡃࠎࡣ㸪ࡇࢀࡲ࡛࡟๓↷ⅉ࡜㊰㠃࡛ࡢ๓↷ⅉࡢ཯ᑕࡢ఩⨨㛵ಀ࠿ࡽ㌴୧఩⨨ࢆṇ☜࡟᥎ᐃ࡛ࡁࡿࡇ࡜࡟ὀ┠ࡍࡿ㸬ᮏᡭἲࡣ㸪㊰㠃཯ᑕ⋡ࡀ୍ᐃ࡛࠶
ࡿ࡜࠸࠺๓ᥦ᮲௳ࢆᚲせ࡜ࡍࡿ㸬ᐇ㝿ࡢ㊰㠃࡟ࡣ㐨㊰ᶆ♧➼ࡀᏑᅾࡋ㸪㊰㠃཯ᑕ⋡ࡀ୍ᐃ࡛࠶ࡿ࡜࠸࠺๓ᥦ᮲௳ࡀᡂࡾ❧ࡓࡎ࡟㸪཯ᑕ఩⨨ࢆㄗ᳨ฟࡍࡿၥ㢟ࡀ࠶ࡗࡓ㸬ࡑࡇ࡛㸪཯ᑕ఩⨨ࢆ᫬✵㛫ⓗ࡟㉮ᰝࡋ㸪ࡇࡢၥ㢟࡟ᑐฎࡍࡿᡭἲࢆᥦ᱌ࡍࡿ㸬

Fig. 11: ๓↷ⅉ࡜๓↷ⅉࡢ཯ᑕࡢ఩⨨㛵ಀࢆ฼⏝ࡋࡓ๓↷ⅉ఩⨨ࡢ᥎ᐃ
-174-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩ᮏ◊✲࡛ࡣ㸪༢⊂࡛タ⨨ࡉࢀ࡚࠸ࡿ㐨㊰┘ど࣓࢝ࣛࢆ฼⏝ࡍࡿ㸬஦๓࡟㐨㊰┘ど࣓࢝ࣛࡢ࢟ࣕࣜࣈ࣮ࣞࢩࣙࣥࢆ⾜࠸㸪㐨㊰┘ど࣓࢝ࣛ࡜㊰㠃ࡢ㛵ಀࡣ᪤▱࡜௬ᐃࡍࡿ㸬ࡇࡢ࡜ࡁ㸪Fig.11 ࡟♧ࡍࡼ࠺࡟㸪㐨㊰┘ど࣓࢝ࣛࡢ↔Ⅼ఩⨨ࢆ C ࡜ࡋ㸪ࡇࢀࡶ᪤▱࡜ࡍࡿ㸬↔Ⅼ఩⨨C ࢆ㊰㠃࡟ᆶ┤࡟ᑕᙳࡋࡓ఩⨨ࢆୡ⏺ᗙᶆ⣔ࡢཎⅬ O ࡜ᐃ⩏ࡍࡿ㸬๓↷ⅉࡀ㊰㠃࡛㙾㠃཯ᑕࡋ࡚㸪↔Ⅼ఩⨨ C ࡛ほࡉࢀࡿ࡜࠸࠺ࡇ࡜ࡣ㸪㙾㠃཯ᑕ఩⨨ R ࡀ๓↷ⅉ఩⨨ H ࡜↔Ⅼ఩⨨ C ࢆྵࡳ㸪㙾㠃཯ᑕ఩⨨ R ࠿ࡽࡢ㊰㠃ࡢἲ⥺࣋ࢡࢺࣝ n ࢆྵࡴᣊ᮰㠃ୖ࡟Ꮡᅾࡍࡿ࡜࠸࠺ࡇ࡜࡛࠶ࡿ㸬࠿ࡘ㸪ࡑࡢᣊ᮰㠃ࡣ↔Ⅼ఩⨨ C ࠿ࡽࡣᣊ᮰⥺࡜ࡋ࡚ほࡉࢀࡿ㸬ࡍ࡞ࢃࡕ㸪⏬ീୖ࡛ࡢ๓↷ⅉ఩⨨ H ࡢᢞᙳⅬ H͛࠿ࡽᣊ᮰⥺࡟ἢࡗ࡚࠸ࡅࡤ㸪㙾㠃཯ᑕ⏬ീ఩⨨ R͛ࢆⓎぢࡍࡿࡇ࡜ࡀ࡛ࡁࡿ㸬
Fig. 12 ࡢࡼ࠺࡞ᫎീࢆ᧜ᙳࡍࡿ㐨㊰┘ど࣓࢝ࣛ࡟ᑐࡋ࡚㸪ᐇ㦂ࢆ⾜ࡗࡓ㸬ᐇ㦂⤖ᯝࢆ Fig.
13 ࡟♧ࡍ㸬Frame number ࡣᫎീ␒ྕࢆ⾲ࡋ㸪vertical position ࡣᫎീ୰࡛ࡢ㐨㊰࡟ἢࡗࡓ఩⨨ࢆ♧ࡍ㸬᳨ฟࡉࢀࡓ㙾㠃཯ᑕ఩⨨ࢆ⥳Ⰽ࣐࣮࣮࡛࢝♧ࡋ࡚࠸ࡿ㸬ࡇࡢⅬࡀᏳᐃࡋ࡚ồࡲࡗ࡚࠸ࡿᵝᏊࡀࢃ࠿ࡿ㸬ࡇࡢ㙾㠃཯ᑕ఩⨨࡜㐨㊰┘ど࣓࢝ࣛࡢ࢟ࣕࣜࣈ࣮ࣞࢩࣙࣥ᝟ሗ࠿ࡽ㸪๓↷ⅉࡢ㊰ୖ࡛ࡢ㸱ḟඖ఩⨨ࢆồࡵࡿࡇ࡜ࡀྍ⬟࡟࡞ࡿ㸬

Fig. 12: 㐨㊰┘ど࣓࢝ࣛ㸦᫨࣭ኪ㸧Fig. 13: ᐇ㦂⤖ᯝ ᩍ⫱ Ꮫ⏕ࡢᣦᑟ≧ἣ㸦Ꮫ⏕Ặྡ㸪Ꮫ఩ࡢ✀㢮㸪ㄽᩥྡ㸧-175-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩బ⸨⚽᫛ ༤ኈ㸦ᕤᏛ㸧ࠕ㙾ീࢆ⏝࠸ࡓ௬᝿ୡ⏺࡜⌧ᐇୡ⏺ࡢ㔜␚ᥦ♧ࠖᯘᑗஅ ༤ኈ㸦ᕤᏛ㸧ࠕᒇእ⎔ቃ࡟࠾ࡅࡿᣦ♧ඹ᭷ࡢࡓࡵࡢ」ྜ⌧ᐇᆺࢪ࣐࢜ࣛ࢖ࣥࢱࣇ࢙࣮ࢫࠖ኱ᕝཎ཭ᶞ ಟኈ㸦ᕤᏛ㸧ࠕ⮬⏤どⅬᫎീ᧯స࡟࠾ࡅࡿ௬᝿࣓࢝ࣛ఩⨨ጼໃ⿵ṇᡭἲࠖྩᓥ┤ᇛ ಟኈ㸦ᕤᏛ㸧ࠕศ๭ࣃ࣮ࢶࣔࢹࣝ࡟ࡼࡿపゎീᗘᫎീ࠿ࡽࡢṌ⾜⪅ࡢ 3 ḟඖ⾲⌧ࠖᑠᯘ┤ᶞ ಟኈ㸦ᕤᏛ㸧ࠕኪ㛫ࡢ㐨㊰┘ど࣓࢝ࣛᫎീ࡟࠾ࡅࡿ๓↷ⅉ࡟ὀ┠ࡋࡓ㌴୧ࡢ 3ḟඖ఩⨨᥎ᐃࠖబ⸨⩧ᝅ ಟኈ㸦ᕤᏛ㸧ࠕࣃ࣓ࣛࢺࣜࢵࢡ✵㛫ࢆ⏝࠸ࡓ㢦⾲᝟ࡢࣜ࢔ࣝࢱ࢖࣒ቑᙉฎ⌮ࠖ㕥ᮌ཭つ ಟኈ㸦ᕤᏛ㸧ࠕ௬᝿໬⌧ᐇ࡜」ྜ⌧ᐇᆺᥦ♧ࢆ⏝࠸ࡓྍᦙⓗ࡞༟ୖసᴗ✵㛫ࡢᐇ⌧ࠖᙇ᫂ ಟኈ㸦ᕤᏛ㸧ࠕᘓ≀ᶍᆺ࡬ࡢᢞᙳᆺ」ྜ⌧ᐇឤࢆ⏝࠸ࡓෆእほᥦ♧᪉ᘧࠖ㫽ᒇ๛Ẏಟኈ㸦ᕤᏛ㸧ࠕ✵᧜⏬ീࢆ⏝࠸ࡓࣔࣂ࢖࣓ࣝ࢝ࣛࡢ఩⨨ጼໃ᥎ᐃᡭἲࠖཬᕝ⣧⪨ Ꮫኈ㸦ᕤᏛ㸧ࠕ⎔ቃᇙࡵ㎸ࡳᆺ࣓࢝ࣛᫎീࡢ㜀ぴࡢࡓࡵࡢ㏱᫂ࢫࢡ࣮ࣜࣥࢆ⏝࠸ࡓ AR ᥦ♧ࠖୖᒣᔞ Ꮫኈ㸦ᕤᏛ㸧ࠕ㑅ᡭືస࡟ᇶ࡙ࡃࣂࢻ࣑ࣥࢺࣥᫎീศ๭ࠖ➲஭⩧ኴ Ꮫኈ㸦ᕤᏛ㸧ࠕࢲࢵࢩ࣮ࣗ࣎ࢻࡢ㏱᫂໬࡟ࡼࡿ㐠㌿㌴୧ࡢ㌴㍯㌶㐨ࡢྍど໬ࠖ㞟୰ㅮ⩏࡞࡝࡞ࡋ ཷ㈹㸪እ㒊㈨㔠㸪▱ⓗ㈈⏘ᶒ➼
ཷ㈹㸦㈹ࡢྡ⛠㸪ཷ㈹⪅ྡ㸪ࢱ࢖ࢺࣝ㸪ᖺ᭶᪥㸧
1. Junya Kashiwakuma, Itaru Kitahara, Yoshinari Kameda, and Yuichi Ohta
Best Paper Honorable Mention on "a Virtual Camera Controlling Method Using
Multi-Touch Gestures for Capturing Free-Viewpoint Video"
11th Europian Interactive TV Conference, 2013.
2013/06/24-26, Como, Italy.
2. Junya Kashiwakuma, Itaru Kitahara, Yoshinari Kameda, and Yuichi Ohta
Best Paper Honorable Mention on "a Virtual Camera Controlling Method Using
Multi-Touch Gestures for Capturing Free-Viewpoint Video"
11th Europian Interactive TV Conference, 2013.
2013/06/24-26, Como, Italy.

-176-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩እ㒊㈨㔠㸦ྡ⛠㸪Ặྡ㸪௦⾲࣭ศᢸࡢู㸪᥇ᢥᖺᗘ㸪㔠㢠㸪ㄢ㢟ྡ㸧
1. ⛉Ꮫ◊✲㈝⿵ຓ㔠 ᇶ┙◊✲(B) ㄢ㢟␒ྕ 23300064
ட⏣⬟ᡂ㸦◊✲௦⾲⪅㸧㸪኱⏣཭୍㸪໭ཎ᱁㸪㸰㸮㸯㸯ᖺᗘ㸪㸰㸪㸰㸯㸮㸪㸮㸮㸮෇㸦㸰㸮㸯㸱ᖺᗘศ㸧㸪ࠕ⎔ቃ࣓࢝ࣛ⩌ᫎീࡢᏳᚰ࠿ࡘຠ⋡ⓗぢ࠼ࡿ໬ࡢⅭࡢ᫬✵㛫ゎᯒ࡜」ྜ⌧ᐇឤⓗྍど໬ࠖ
2. ⛉Ꮫ◊✲㈝⿵ຓ㔠 ᇶ┙◊✲(B) ㄢ㢟␒ྕ 25280056኱⏣཭୍㸦◊✲௦⾲⪅㸧㸪ட⏣⬟ᡂ㸪໭ཎ᱁㸪㸰㸮㸯㸱ᖺᗘ㸪㸵㸪㸲㸯㸮㸪㸮㸮㸮෇㸦㸰㸮㸯㸱ᖺᗘศ㸧㸪ࠕ኱つᶍ✵㛫ࢆᑐ㇟࡜ࡍࡿே≀ࣅ࣮ࣝ࣎ࢻࢆ⏝࠸ࡓ௵ពどⅬᫎീ⏕ᡂᥦ♧᪉ᘧࡢ㧗⏬㉁໬ࠖ
3. ඹྠ◊✲㸦᪥ᮏ㟁Ẽᰴᘧ఍♫㸧㸪໭ཎ᱁㸦௦⾲⪅㸧࣭኱⏣཭୍࣭ட⏣⬟ᡂ㸪㸰㸮㸯㸱ᖺᗘ㸪㸵㸱㸳,㸮㸮㸮෇㸪⮬⏤どⅬᫎീ⏕ᡂࡢ◊✲
4. ඹྠ◊✲㸦ࣖࣇ࣮ࢪࣕࣃࣥᰴᘧ఍♫㸧㸪໭ཎ᱁㸦௦⾲⪅㸧㸪㸰㸮㸯㸱ᖺᗘ㸪㸳㸳㸮,㸮㸮㸮෇㸪㉸㧗ゎീᗘከどⅬ⏬ീ࡟࠾ࡅࡿどⅬ⛣ື࡟㛵ࡍࡿ◊✲▱ⓗ㈈⏘ᶒ㸦✀ู㸪Ặྡ㸪ㄢ㢟ྡ㸪ᖺ᭶᪥㸧࡞ࡋ ◊✲ᴗ⦼(1) ◊✲ㄽᩥA) ᰝㄞ௜ࡁㄽᩥ1. బ⸨⚽᫛, ໭ཎ᱁, ኱⏣཭୍, “㙾ീࢆ⏝࠸ࡓ⌧ᐇୡ⏺࡜௬᝿ୡ⏺ࡢ㔜␚ᥦ♧࡟࠾ࡅࡿ㐠ືどᕪ࡜๓ᚋ㛵ಀᢕᥱ⢭ᗘࡢ㛵ಀ,” ᪥ᮏࣂ࣮ࢳࣕࣝࣜ࢔ࣜࢸ࢕Ꮫ఍
ㄽᩥㄅ, 18, 3, pp.421-430, 2013.
2. Youhei Kawamura, Ashraf M Dewan, Bert Veenendaal, Masahiro Hayashi,Takeshi Shibuya, Itaru Kitahara, Hajime Nobuhara, Kento Ishii, “Using
GIS to Develop a Mobile Communications Network for Disaster-damagedAreas,” International Journal of Digital Earth (DOI:10.1080/17538947.2013.808277), 2013.
B) ᰝㄞ↓ࡋㄽᩥ(2) ᅜ㝿఍㆟Ⓨ⾲-177-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩A) ᣍᚅㅮ₇
1.Itaru Kitahara, Hisatoshi Toriya, Yuichi Ohta, “A Mobile Camera
Localization Method for Updating Topography Map,” The 3rdInternational Workshop on Soft Computing and Disaster Control (SocDic2013), 2013.

B) ୍⯡ㅮ₇
1.

Yun Li, Yoshinari Kameda, Yuichi Ohta, “Acquisition of Enhanced 3D
Video Stream by Analyzing Static Property of the Scene Taken by RGB-DCamera,” Proceedings of The 6th Korea-Japan Workshop on MixedReality (KJMR2013), 13 pages, 2013.

2.

Junya Kashiwakuma, Itaru Kitahara, Yoshinari Kameda, Yuichi Ohta,“A Virtual Camera Controlling Method Using Multi-Touch Gestures for
Capturing Free-viewpoint Video,” 11th European Interactive TVConference, pp.67-74, 2013.

3.

Masayuki Hayashi, Itaru Kitahara, Yoshinari Kameda, Yuichi Ohta, Koji
Makita, Takeshi Kurata, “Projective indices for AR/MR benchmarking in
TrakMark,” ISMAR2013 Joint Workshop on Tracking Methods &Applications and TrakMark, 3 pages, 2013.

4.

Hidehiko Shishido, Itaru Kitahara, Yoshinari Kameda, and Yuichi Ohta,“A Visual Object Tracking and Trajectory Estimation Method forBadminton Shuttlecock Utilizing Motion Blur”, 6th Pacific-RimSymposium on Image and Video Technology, 12 pages, 2013.

5.

Yoshinari Kameda and Takeshi Kurata, “Introduction of Trakmark andits Standardization Activity at ISO/IEC JTC 1/SC 24/WG 9,” IEEEISMAR 2013 Joint Workshop on Tracking Methods & Applications andTrakMark, 3 pages, 2013.

6.

Yoshitaka Hara, Shigeru Bando, Takashi Tsubouchi, Akira Oshima, Itaru
Kitahara, and Yoshinari Kameda, “6DOF Iterative Closest Point-178-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩

Matching Considering a Priori with Maximum a Posteriori Estimation,”
IEEE/RSJ International Conference on Intelligent Robots and Systems,
pp.4172 - 4179, 2013.
7.

Hisatoshi Toriya, Itaru Kitahara, Yuichi Ohta, “A Mobile Camera
Localization Method Using Aerial-View Images,” Asian Conference on
Pattern Recognition 2013

8.

(ACPR2013), 5 pages, 2013.

Shogo Sato, Itaru Kitahara, Yuichi Ohta, ͆Augmented-Reality FacialExpression Enhancement for Video Chatting Using Point Distribution
Models ࠙Posterࠚ,͇ 5th Joint Virtual Reality Conference (JVRC2013),
pp.51-56, 2013.

9.

Yun Li, Yoshinari Kameda, Yuichi Ohta, ͆AR Replay in a SmallWorkspace,͇ Proceedings of 23rd International Conference on Artificial
Reality and Telexistence㸦ICAT2013), pp.97-101, 2013.

10.

Yasushi Yamagiri, Itaru Kitahara, Yoshinari Kameda, and Yuichi Ohta,“Body Motion Design for Maneuvering a Virtual Camera in 3D Soccer
Game,” Proceedings of 23th International Conference on Artificial
Reality and Telexistence (ICAT2013), 2 pages, 2013.

11.

Masashi Ueda, Itaru Kitahara, and Yuichi Ohta, “MR Simulation for
Re-Wallpapering a Room in a Free-Hand Movie,” The 20th Anniversary
International Conference on MultiMedia Modeling (MMM2014), LNCS
8325, pp.436-448, Springer, 2014.

(3) ᅜෆᏛ఍࣭◊✲఍Ⓨ⾲A) ᣍᚅㅮ₇B) ࡑࡢ௚ࡢⓎ⾲
1. ᯘᑗஅ, ໭ཎ᱁, ட⏣⬟ᡂ, ኱⏣཭୍, “ᒇእ࣓࢝ࣛタ⨨࡟࠾ࡅࡿ࣏࣮ࢬỴᐃࢱࢫࢡ࡟ࡼࡿ」ྜ⌧ᐇᆺࢪ࣐࢜ࣛ࢖ࣥࢱࣇ࢙࣮ࢫࡢホ౯,” 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ᢏ⾡◊✲ሗ࿌ MVE, 113, 51, pp.45-50, 2013.

-179-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩2. ᶡぢభு, ட⏣⬟ᡂ , ኱⏣཭୍, “Ṍ⾜⤒㊰グ㘓ᫎീ࡟࠾ࡅࡿ⏬ീ᳨⣴ᛶ⬟ホ౯,” ➨ 19 ᅇ⏬ീࢭࣥࢩࣥࢢࢩ࣏ࣥࢪ࣒࢘ㄽᩥ㞟 (SSII2013), 5 pages, 2013.
3. ᏷ᡞⱥᙪ, ໭ཎ᱁, ட⏣⬟ᡂ, ኱⏣཭୍, “࣮ࣔࢩࣙࣥࣈ࣮ࣛࢆά⏝ࡋࡓࣂࢻ࣑
ࣥࢺࣥࢩࣕࢺࣝࡢ㌶㊧᥎ᐃἲ,” ⏬ീࡢㄆ㆑࣭⌮ゎࢩ࣏ࣥࢪ࣒࢘ 2013 ㄽᩥ㞟(MIRU2013), pp.2, 2013.
4. ᒣ᱒㟹ྐ, ໭ཎ᱁, ட⏣⬟ᡂ, ኱⏣཭୍, “⮬⏤どⅬᫎീ㜀ぴ࡟㐺ࡋࡓ඲㌟㌟య
ືసࡢ᳨ウ,” ➨ 12 ᅇ᝟ሗ⛉Ꮫᢏ⾡ࣇ࢛࣮࣒ࣛ(FIT2013), vol.3, pp.529-530,2013.
5. 㕥ᮌ཭つ, ໭ཎ᱁, ኱⏣཭୍, “༟ୖ✵㛫ࡢ㸱ḟඖࣔࢹࣝࡢ⋓ᚓ࡜」ྜ⌧ᐇᆺᥦ♧ࢆ⏝࠸ࡓ෌⌧,” ➨ 18 ᅇ᪥ᮏࣂ࣮ࢳࣕࣝࣜ࢔ࣜࢸ࢕Ꮫ఍኱఍ㄽᩥ㞟,pp.55-58, 2013.
6. ᙇ᫂, ໭ཎ᱁, ட⏣⬟ᡂ, ኱⏣཭୍, “ᢞᙳᆺ」ྜ⌧ᐇឤ࡜ᘓ≀ᶍᆺࢆ⏝࠸ࡓ࢖ࣥࢱࣛࢡࢸ࢕ࣈ࡞ෆእほᥦ♧᪉ᘧ,” ➨ 18 ᅇ᪥ᮏࣂ࣮ࢳࣕࣝࣜ࢔ࣜࢸ࢕Ꮫ఍኱఍ㄽᩥ㞟, pp.224-227, 2013.
7. ᳃⏣⯟ᖹ, ட⏣⬟ᡂ, ໭ཎ᱁, ኱⏣཭୍, “࢘࢕ࣥࢻࢩ࣮ࣝࢻࢹ࢕ࢫࣉࣞ࢖ࢆ⏝࠸ࡓ஺ᕪⅬ࡟࠾ࡅࡿ㐍ධ㌴୧ᥦ♧ἲ,” ➨ 18 ᅇ᪥ᮏࣂ࣮ࢳࣕࣝࣜ࢔ࣜࢸ࢕Ꮫ఍኱఍ㄽᩥ㞟, pp.295-298, 2013.
8. బ⸨⩧ᝅ, ໭ཎ᱁, ኱⏣཭୍, “ࣅࢹ࢜㏻ヰࡢࡓࡵࡢ Point Distribution
Models ࢆ⏝࠸ࡓ㢦⾲᝟ቑᙉᡭἲ,” 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ ᢏ⾡◊✲ሗ࿌ MVE,113, 227, pp.71-76, 2013.
9. ᒣ᱒㟹ྐ, ໭ཎ᱁, ட⏣⬟ᡂ, ኱⏣཭୍, “⮬⏤どⅬᫎീ㜀ぴ࡟㐺ࡋࡓ඲㌟㌟య
ືసࡢ᳨ウ,” ➨ 12 ᅇ᝟ሗ⛉Ꮫᢏ⾡ࣇ࢛࣮࣒ࣛ(FIT2013), 2 pages, 2013.
10. ཌぢᙲ࿃, ໭ཎ᱁, ኱⏣཭୍, “ከどⅬ⏬ീࢆ⏝࠸ࡓᒾ▼ࡢࣔࢹࣜࣥࢢ࡜㓄⨨ᅗࡢ⏕ᡂ,” ᆅ┙ᕤᏛ఍㛵ᮾᨭ㒊Ⓨ⾲఍ 2013, 4 pages, 2013.
11. ᏷ᡞⱥᙪ, ໭ཎ᱁, ட⏣⬟ᡂ, ኱⏣཭୍, “࣮ࣔࢩࣙࣥࣈ࣮ࣛࢆά⏝ࡋࡓࣂࢻ࣑
ࣥࢺࣥࡢࢩࣕࢺࣝ㌶㊧᥎ᐃἲ,” SAT ࢸࢡࣀࣟࢪ࣮࣭ࢩ࣮ࣙࢣ࣮ࢫ 2014, p.84,2014.
-180-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩12. ∾⏣ ᏕႹ, ᯘ ᑗஅ, ⶶ⏣ Ṋᚿ, ட⏣ ⬟ᡂ, బ⸨ ᬛ࿴, “TrakMark: AR/MRࢺࣛࢵ࢟ࣥࢢࡢ࣋ࣥࢳ࣐࣮ࢡ,” 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ ᢏ⾡◊✲ሗ࿌ MVE,vol.113, no.402, pp.245-250, 2014.
13. ᑠᯘ┤ᶞ, ໭ཎ᱁, ட⏣⬟ᡂ, ኱⏣཭୍, “๓↷ⅉࡢ㊰ୖ཯ᑕ఩⨨ࢆ฼⏝ࡋࡓ㌴
୧ࡢ 3 ḟඖ఩⨨᥎ᐃ,” 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ ᢏ⾡◊✲ሗ࿌ ITS, vol.113, no.433,pp.111-116, 2014.
14. ᶡぢభு, ட⏣⬟ᡂ, ኱⏣཭୍, “⤒㊰ୖ࡛ࡢ஦๓ࡢ⛣ື᧜ᙳᫎീࢆ⏝࠸ࡓࢫࢼࢵࣉࢩࣙࢵࢺ᧜ᙳ఩⨨ࡢ᥎ᐃ,” 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ ᢏ⾡◊✲ሗ࿌ MVE,vol.113, no.470, pp.1-5, 2014.
15. ᑠᯘ┤ᶞ, ໭ཎ᱁, ட⏣⬟ᡂ, ኱⏣཭୍, “᫬✵㛫ᣊ᮰ࢆ฼⏝ࡋࡓ㌴୧๓↷ⅉࡢ㊰ୖ཯ᑕ఩⨨᥎ᐃ,” 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ ᢏ⾡◊✲ሗ࿌ MVE, vol.113, no.470,pp.25-30, 2014.
16. 㞸Ὀ⿱, ໭ཎ᱁, ኱⏣཭୍, “」ྜ⌧ᐇឤࢆ⏝࠸ࡓⓎヰෆᐜࡢྍど໬࡜ 3 ḟඖ࢖ࣥࢱࣛࢡࢩࣙࣥ,” 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ ᢏ⾡◊✲ሗ࿌ MVE, vol.113, no.470,pp.205-210, 2014.
17. 㕥ᮌ཭つ, ໭ཎ᱁, ኱⏣཭୍, “௬᝿໬⌧ᐇ࡜」ྜ⌧ᐇᆺᥦ♧ࢆ⏝࠸ࡓྍᦙⓗ࡞༟ୖసᴗ✵㛫,” 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ ᢏ⾡◊✲ሗ࿌ MVE, vol.113, no.470,pp.211-216, 2014.
18. ᮤப, ட⏣⬟ᡂ, ኱⏣཭୍, “RGB-D ࣓࢝ࣛࢆ⏝࠸ࡓᩍ♧⪅ࡢసᴗࡢ AR ෌⾲♧,” 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ ᢏ⾡◊✲ሗ࿌ MVE, vol.113, no.470, pp.223-228,2014.
19. ኱ᕝཎ཭ᶞ, ໭ཎ᱁, ட⏣⬟ᡂ, ኱⏣཭୍, “࣮ࣘࢨࡢ௬᝿࣓࢝ࣛ᧯సࢆ⪃៖ࡋࡓ఩⨨ጼໃ⿵ṇ࡟ࡼࡿ⮬⏤どⅬᫎീࡢ⏕ᡂ,” 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ ᢏ⾡◊✲ሗ࿌ MVE, vol.113, no.470, pp.261-266, 2014.
20. ᏷ᡞⱥᙪ, ໭ཎ᱁, ட⏣⬟ᡂ, ኱⏣཭୍, “㏫㡰᫬⣔ิ᥈⣴࡟ࡼࡿ㧗㏿࡟⛣ືࡍࡿࣂࢻ࣑ࣥࢺࣥࢩࣕࢺࣝࡢ㏣㊧,” 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ 2014 ᖺ⥲ྜ኱఍ㅮ₇ㄽᩥ㞟 (᝟ሗ࣭ࢩࢫࢸ࣒ㅮ₇ㄽᩥ㞟 2), pp.141, 2014.
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⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩21. ᫂ᬛ㑣ኸ, ໭ཎ᱁, ኱⏣཭୍, ͆࢜ࢡ࣮ࣝࢪࣙࣥࢆ⪃៖ࡋࡓ⏬ീ⿵㛫ᡭἲ, 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ 2014 ᖺ⥲ྜ኱఍ㅮ₇ㄽᩥ㞟 (᝟ሗ࣭ࢩࢫࢸ࣒ㅮ₇ㄽᩥ㞟 2),pp.137, 2014.
22. Ụኟᐶᮁ, ໭ཎ᱁, ட⏣⬟ᡂ, ኱⏣཭୍, “ᒇእ✵㛫࡟࠾ࡅࡿṌ⾜⪅࡜ࡑࡢὀど㡿ᇦࡢ᧜ᙳἲ,” 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ 2014 ᖺ⥲ྜ኱఍ㅮ₇ㄽᩥ㞟 (᝟ሗ࣭ࢩࢫࢸ࣒ㅮ₇ㄽᩥ㞟 2), pp.133, 2014.
23. 㔠ᕝ♸㈗, ໭ཎ᱁, ட⏣⬟ᡂ, ኱⏣཭୍, “㐨㊰┘ど࣓࢝ࣛᫎീ࡟࠾ࡅࡿ㌴୧㡿ᇦࡢṇつ໬ࢆ⏝࠸ࡓ㌶㊧᥎ᐃ,” 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ 2014 ᖺ⥲ྜ኱఍ㅮ₇ㄽᩥ㞟 (᝟ሗ࣭ࢩࢫࢸ࣒ㅮ₇ㄽᩥ㞟 2), pp.132, 2014.
24. Ἑෆ㥴, ໭ཎ᱁, ட⏣⬟ᡂ, ኱⏣཭୍, “ࢱࣈࣞࢵࢺ➃ᮎ࡜࣮ࣘࢨどⅬ࡜ࡢ఩⨨㛵ಀࡀࢩ࣮ࢫ࣮ࣝࣅࢪࣙࣥࡢ▱ぬ࡟୚࠼ࡿᙳ㡪࡟ࡘ࠸࡚ࡢᇶ♏ⓗ᳨ウ,” 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ 2014 ᖺ⥲ྜ኱఍ㅮ₇ㄽᩥ㞟 (᝟ሗ࣭ࢩࢫࢸ࣒ㅮ₇ㄽᩥ㞟 2),pp.134, 2014.
25. ᮾ஭㞙ᩯ, ໭ཎ᱁, ட⏣⬟ᡂ, ኱⏣཭୍, “࢘࢕ࣥࢻࢩ࣮ࣝࢻࢹ࢕ࢫࣉࣞ࢖ࢆ⏝࠸ࡓ㏿ᗘᢚไࢆಁࡍᣑᙇ⌧ᐇᆺᥦ♧,” 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ 2014 ᖺ⥲ྜ኱఍ㅮ₇ㄽᩥ㞟 (᝟ሗ࣭ࢩࢫࢸ࣒ㅮ₇ㄽᩥ㞟 2), pp.135, 2014.
26. ᏷ᡞ ⱥᙪ, ட⏣ ⬟ᡂ, ኱⏣ ཭୍, ͆ ㏫㡰᫬⣔ิ᥈⣴࡟ࡼࡿ㧗㏿࡟⛣ືࡍࡿࣂࢻ࣑ࣥࢺࣥࢩࣕࢺࣝࡢ㏣㊧,͇ 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ 2013 ᖺ⥲ྜ኱఍ㅮ₇ㄽᩥ㞟(᝟ሗ࣭ࢩࢫࢸ࣒ㅮ₇ㄽᩥ㞟 2), pp.141, 2014.
(4) ⴭ᭩㸪ゎㄝグ஦➼
1. ட⏣ ⬟ᡂ, “USB ࣈ࣮ࢱࣈࣝ࡞ AR/MR ࣉࣟࢢ࣒ࣛ㛤Ⓨ᳨࣭ド⎔ቃ(CasperCartridge Project),” 㟁Ꮚ᝟ሗ㏻ಙᏛ఍఍ㄅ, vol.96, no.7, pp.512-515, 2013.
2. Yoshinari Kameda, “Producing Free Viewpoint 3D Video from a Real Soccer
Game and its User Interface for the Virtual Camera Control,” A Talk at Max
Plank Institute for Intelligent Systems, 2014. (At Perceiving Systems, Max
Planck Institute for Intelligent Systems, Tubingen, Germany.)-182-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩3. ட⏣ ⬟ᡂ, “どぬ᝟ሗ࣓ࢹ࢕࢔㸫ࢥࣥࣆ࣮ࣗࢱࣅࢪࣙࣥ࡜ᣑᙇ⌧ᐇ࡬ࡢᣮᡓ㸫,”ி㒔኱ᏛᏛ⾡᝟ሗ࣓ࢹ࢕࢔ࢭࣥࢱ࣮᭶౛ࢭ࣑ࢼ࣮, 2014. (2014 ᖺ 3 ᭶ 24 ᪥, ி㒔኱Ꮫ) ␗ศ㔝㛫㐃ᦠ࣭ᅜ㝿㐃ᦠ࣭ᅜ㝿άື➼⟃Ἴ኱Ꮫ◊✲ᡓ␎࢖ࢽࢩ࢔ࢸ࢕ࣈ᥎㐍ᶵᵓ㸦URA㸧ᖹᡂ㸰㸳ᖺᗘᾏእⱝᡭ◊✲⪅୰▷ᮇᣍ࡬࠸ࣉࣟࢢ࣒ࣛࢆ฼⏝ࡋ࡚㸪Curtin University ࡢ Dr. Yohei Kawamura ࢆᖹᡂ 26
ᖺ 2 ᭶ 10 ᪥㹼24 ᪥ࡢ㛫㸪⟃Ἴ኱Ꮫ࡟ᣍ⪸ࡋ㸪ࣔࣂ࢖࣓ࣝ࢝ࣛࢆ⏝࠸ࡓ GIS ᆅᙧ࣐ࢵࣉࡢ᭦᪂᪉ᘧ࡟㛵ࡍࡿඹྠ◊✲࡟ྲྀࡾ⤌ࢇࡔ㸬 ࢩ࣏ࣥࢪ࣒࢘㸪◊✲఍㸪ࢫࢡ࣮ࣝ➼ࡢ㛤ദᐇ⦼࡞ࡋ ⟶⌮࣭㐠Ⴀ኱⏣཭୍๪Ꮫ㛗࣭⌮஦㸦௻⏬ホ౯࣭᝟ሗ㸧᝟ሗ⎔ቃᶵᵓ㛗ட⏣⬟ᡂ⟃Ἴ኱Ꮫ e ࣮ࣛࢽࣥࢢጤဨ఍ጤဨ໭ཎ᱁
⟃Ἴ኱Ꮫ඲Ꮫィ⟬ᶵጤဨ఍ጤဨ♫఍㈉⊩࣭ᅜ㝿㈉⊩኱⏣཭୍㟁Ꮚ᝟ሗ㏻ಙᏛ఍ ᩍ⛉᭩ጤဨ఍ጤဨ
➨ 13 ᅇ᝟ሗᏛࣇ࢛࣮࣒ࣛ㸦FIT2014㸧⌧ᆅᐇ⾜ጤဨ఍ ጤဨ㛗ட⏣⬟ᡂ
TMA/TrakMark 2013: IEEE ISMAR 2013 Joint Workshop on Tracking Methods& Applications and TrakMark; One of the organizers [2013/03 - 2013/10/1]
IEEE and ACM International Symposium on Mixed and Augmented Reality; a PC-183-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩member [2013/03 -]㟁Ꮚ᝟ሗ㏻ಙᏛ఍ ᝟ሗࢩࢫࢸ࣒ࢯࢧ࢖࢚ࢸ࢕ ᗢົᖿ஦
㟁Ꮚ᝟ሗ㏻ಙᏛ఍ ࣃࢱ࣮ࣥㄆ㆑࣭࣓ࢹ࢕࢔⌮ゎ ◊✲ᑓ㛛ጤဨ఍ ጤဨ㟁Ꮚ᝟ሗ㏻ಙᏛ఍ ࣐ࣝࢳ࣓ࢹ࢕࢔࣭௬᝿⎔ቃᇶ♏ ◊✲ᑓ㛛ጤဨ఍ ጤဨ㟁Ꮚ᝟ሗ㏻ಙᏛ఍ ࢧ࢖ࣂ࣮࣮࣡ࣝࢻ ᫬㝈◊✲ᑓ㛛ጤဨ఍ ጤဨ➨ 13 ᅇ᝟ሗᏛࣇ࢛࣮࣒ࣛ㸦FIT2014㸧⌧ᆅᐇ⾜ጤဨ఍ ᖿ஦໭ཎ᱁
㟁Ꮚ᝟ሗ㏻ಙᏛ఍ ࿴ᩥㄽᩥㄅ D ⦅㞟ጤဨ
㟁Ꮚ᝟ሗ㏻ಙᏛ఍ ࣃࢱ࣮ࣥㄆ㆑࣭࣓ࢹ࢕࢔⌮ゎ ◊✲ᑓ㛛ጤဨ఍ ጤဨ᪥ᮏࣂ࣮ࢳࣕࣝࣜ࢔ࣜࢸ࢕Ꮫ఍

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➨ 13 ᅇ᝟ሗᏛࣇ࢛࣮࣒ࣛ㸦FIT2014㸧⌧ᆅᐇ⾜ጤဨ఍ ᖿ஦ࡑࡢ௚࡞ࡋ

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