Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
画像処理論セミナー7-1-3
Search
Kuno Ayana
July 02, 2020
Education
37
0
Share
画像処理論セミナー7-1-3
Kuno Ayana
July 02, 2020
More Decks by Kuno Ayana
See All by Kuno Ayana
アクセシビリティ、まだ完璧じゃないけど ── “今から”できること
kno3a87
2
1.2k
ぬるぬる動かせ! Riveでアニメーション実装🐾
kno3a87
1
1.9k
Dart 参戦!!静的型付き言語界の隠れた実力者
kno3a87
0
280
Flutterを言い訳にしない!アプリの使い心地改善テクニック5選🔥
kno3a87
3
830
iOS 18 がやってきた!
kno3a87
1
260
おうちハッカソン #2
kno3a87
0
160
ミクアカ成果報告会
kno3a87
0
68
SXSW2021
kno3a87
0
75
ミクアカ中間発表会
kno3a87
0
54
Other Decks in Education
See All in Education
この講義について / 00-setup
kaityo256
PRO
2
370
2026年度春学期 統計学 第5回 分布をまとめるー記述統計量(平均・分散など) (2026. 5. 7)
akiraasano
PRO
0
110
From Participation to Outcomes
territorium
PRO
0
450
BITCOIN : Les fondamentaux !
rlifchitz
0
140
fake vs real
latrrr
0
120
✅ レポート採点基準 / How Your Reports Are Assessed
yasslab
PRO
0
340
0415
cbtlibrary
0
180
勝手にCULTIBASE で広げよう、探究の輪! - CULTIVAL 2026
hiroc_sk
1
190
Course Review - Lecture 13 - Information Visualisation (4019538FNR)
signer
PRO
1
2.6k
Dashboards - Lecture 11 - Information Visualisation (4019538FNR)
signer
PRO
1
2.6k
Tangible, Embedded and Embodied Interaction - Lecture 7 - Next Generation User Interfaces (4018166FNR)
signer
PRO
0
2.2k
Virtual and Augmented Reality - Lecture 8 - Next Generation User Interfaces (4018166FNR)
signer
PRO
0
2.2k
Featured
See All Featured
The World Runs on Bad Software
bkeepers
PRO
72
12k
Lessons Learnt from Crawling 1000+ Websites
charlesmeaden
PRO
1
1.2k
The innovator’s Mindset - Leading Through an Era of Exponential Change - McGill University 2025
jdejongh
PRO
1
170
Bash Introduction
62gerente
615
210k
Scaling GitHub
holman
464
140k
Leveraging LLMs for student feedback in introductory data science courses - posit::conf(2025)
minecr
1
250
Gemini Prompt Engineering: Practical Techniques for Tangible AI Outcomes
mfonobong
2
390
jQuery: Nuts, Bolts and Bling
dougneiner
66
8.5k
How to audit for AI Accessibility on your Front & Back End
davetheseo
0
370
Cheating the UX When There Is Nothing More to Optimize - PixelPioneers
stephaniewalter
287
14k
Making Projects Easy
brettharned
120
6.6k
Understanding Cognitive Biases in Performance Measurement
bluesmoon
32
2.9k
Transcript
,VOP"ZBOB σΟδλϧը૾ॲཧ ٯϑΟϧλɾΟʔφϑΟϧλʹΑΔը૾෮ݩ
લճͷ෮़ɿ΅͚ɾͿΕͱ ࣍ݩσϧλؔ δ(x, y) ྼԽը૾ g(x, y) ݪը૾ f(x, y)
લճͷ෮़ɿ֦͕ΓؔͷϞσϧԽ ΅͚ͷ֦͕ΓؔˠΨεͱۙࣅ ͿΕͷ֦͕ΓؔˠͿΕͷํВʹͷΈ෯XʹҰ࣍ݩͰ͕͍ͬͯΔؔͱۙࣅ
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ g(x, y) = f(x, y) * h(x, y) ྼԽը૾
ݪը૾ ֦͕Γؔ ֦͕Γ͕ؔطͷ߹ ٯϑΟϧλΛྼԽը૾ʹదԠ͢Δ͜ͱͰݪը૾͕ٻ·Δ
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ g(x, y) = f(x, y) * h(x, y) ྼԽը૾
ݪը૾ G(u, v) = F(u, v)H(u, v) ϑʔϦΤม 'ྼԽը૾ 'ݪը૾ ϑΟϧλ ֦͕Γؔ ֦͕Γ͕ؔطͷ߹ ٯϑΟϧλΛྼԽը૾ʹదԠ͢Δ͜ͱͰݪը૾͕ٻ·Δ
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ G(u, v) = F(u, v)H(u, v) 'ྼԽը૾ 'ݪը૾ ϑΟϧλ
֦͕Γ͕ؔطͷ߹ ٯϑΟϧλΛྼԽը૾ʹదԠ͢Δ͜ͱͰݪը૾͕ٻ·Δ
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ G(u, v) = F(u, v)H(u, v) 'ྼԽը૾ 'ݪը૾ ϑΟϧλ
֦͕Γ͕ؔطͷ߹ ٯϑΟϧλΛྼԽը૾ʹదԠ͢Δ͜ͱͰݪը૾͕ٻ·Δ K(u, v) K(u, v)
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ G(u, v) = F(u, v)H(u, v) 'ྼԽը૾ 'ݪը૾ ϑΟϧλ
1 H(u, v) ٯϑΟϧλ ֦͕Γ͕ؔطͷ߹ ٯϑΟϧλΛྼԽը૾ʹదԠ͢Δ͜ͱͰݪը૾͕ٻ·Δ 1 H(u, v)
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ G(u, v) = F(u, v)H(u, v) 'ྼԽը૾ 'ݪը૾ ϑΟϧλ
1 H(u, v) ٯϑΟϧλ ֦͕Γ͕ؔطͷ߹ ٯϑΟϧλΛྼԽը૾ʹదԠ͢Δ͜ͱͰݪը૾͕ٻ·Δ 1 H(u, v)
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ G(u, v) = F(u, v) 'ྼԽը૾ 'ݪը૾ ֦͕Γ͕ؔطͷ߹ ٯϑΟϧλΛྼԽը૾ʹదԠ͢Δ͜ͱͰݪը૾͕ٻ·Δ
1 H(u, v)
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ G(u, v) = F(u, v) 'ྼԽը૾ 'ݪը૾ ֦͕Γ͕ؔطͷ߹ ٯϑΟϧλΛྼԽը૾ʹదԠ͢Δ͜ͱͰݪը૾͕ٻ·Δ
1 H(u, v)
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ G(u, v) = F(u, v) 'ྼԽը૾ 'ݪը૾ ϑʔϦΤٯม g(x,
y) = f(x, y) ྼԽը૾ ݪը૾ ֦͕Γ͕ؔطͷ߹ ٯϑΟϧλΛྼԽը૾ʹదԠ͢Δ͜ͱͰݪը૾͕ٻ·Δ 1 H(u, v)
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ G(u, v) = F(u, v)H(u, v) 'ྼԽը૾ 'ݪը૾ ϑΟϧλ
1 H(u, v) ٯϑΟϧλ ֦͕Γ͕ؔطͷ߹ ٯϑΟϧλΛྼԽը૾ʹదԠ͢Δ͜ͱͰݪը૾͕ٻ·Δ ͘͠ݶΓͳ͘ʹ͍ۙͩͬͨΒʁ
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ G(u, v) = F(u, v)H(u, v) 'ྼԽը૾ 'ݪը૾ ϑΟϧλ
1 H(u, v) ٯϑΟϧλ ֦͕Γ͕ؔطͷ߹ ٯϑΟϧλΛྼԽը૾ʹదԠ͢Δ͜ͱͰݪը૾͕ٻ·Δ ͘͠ݶΓͳ͘ʹ͍ۙͩͬͨΒʁ ൃࢄͯ͠͠·͏ʂ
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ G(u, v) = F(u, v)H(u, v) + N(u, v)
ൃࢄ͢ΔͱϊΠζ͕૿෯ͯ͠͠·͏ ˠ) V W ͕ʹ͍ۙͱ͖ʹൃࢄ͠ͳ͍ϑΟϧλΛߟ͑Δඞཁ͕͋Δ 'ϊΠζ
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ Kw (u, v) = 1 H(u, v) ෮ݩը૾ɹɹɹͱݪը૾ɹɹɹͷޡࠩΛ࠷খʹ͢ΔΑ͏ͳϑΟϧλ Kw
(u, v) ̂ f(x, y) f(x, y)
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ Kw (u, v) = 1 H(u, v) |H(u, v)|2
|H(u, v)|2 + |N(u, v)|2 /|F(u, v)|2 ෮ݩը૾ɹɹɹͱݪը૾ɹɹɹͷޡࠩΛ࠷খʹ͢ΔΑ͏ͳϑΟϧλ Kw (u, v) ̂ f(x, y) f(x, y)
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ Kw (u, v) = 1 H(u, v) |H(u, v)|2
|H(u, v)|2 + |N(u, v)|2 /|F(u, v)|2 ෮ݩը૾ɹɹɹͱݪը૾ɹɹɹͷޡࠩΛ࠷খʹ͢ΔΑ͏ͳϑΟϧλ Kw (u, v) ̂ f(x, y) f(x, y) ΟʔφϑΟϧλ
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ Kw (u, v) = 1 H(u, v) |H(u, v)|2
|H(u, v)|2 + |N(u, v)|2 /|F(u, v)|2 ෮ݩը૾ɹɹɹͱݪը૾ɹɹɹͷޡࠩΛ࠷খʹ͢ΔΑ͏ͳϑΟϧλ Kw (u, v) ̂ f(x, y) f(x, y) ϊΠζ͕ͷ߹
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ Kw (u, v) = 1 H(u, v) |H(u, v)|2
|H(u, v)|2 + |N(u, v)|2 /|F(u, v)|2 ෮ݩը૾ɹɹɹͱݪը૾ɹɹɹͷޡࠩΛ࠷খʹ͢ΔΑ͏ͳϑΟϧλ Kw (u, v) ̂ f(x, y) f(x, y) ϊΠζ͕ͷ߹ ͕͜͜ʹͳΔͷͰ ٯϑΟϧλͱಉ༷ʹΔ
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ Kw (u, v) = 1 H(u, v) |H(u, v)|2
|H(u, v)|2 + |N(u, v)|2 /|F(u, v)|2 ෮ݩը૾ɹɹɹͱݪը૾ɹɹɹͷޡࠩΛ࠷খʹ͢ΔΑ͏ͳϑΟϧλ Kw (u, v) ̂ f(x, y) f(x, y) ͍͍ͩͨϊΠζݪը૾ະ దͳఆϵΛஔ͘͜ͱ͕ଟ͍
ը૾Λ෮ݩ͢ΔۭؒϑΟϧλΛߟ͑Δ Kw (u, v) = 1 H(u, v) |H(u, v)|2
|H(u, v)|2 + Γ ෮ݩը૾ɹɹɹͱݪը૾ɹɹɹͷޡࠩΛ࠷খʹ͢ΔΑ͏ͳϑΟϧλ Kw (u, v) ̂ f(x, y) f(x, y)
ϵΛมԽͤ͞Δ Kw (u, v) = 1 H(u, v) |H(u, v)|2
|H(u, v)|2 + Γ ਤ
ϵΛมԽͤ͞Δ Kw (u, v) = 1 H(u, v) |H(u, v)|2
|H(u, v)|2 + Γ ਤ ൃࢄͯ͠͠·͍ըૉ͕ൃࢄ͍ͯ͠Δ θϩΛؚΜͰ͍ΔͨΊ
ϵΛมԽͤ͞Δ Kw (u, v) = 1 H(u, v) |H(u, v)|2
|H(u, v)|2 + Γ ਤ ൃࢄͯ͠͠·͍ըૉ͕ൃࢄ͍ͯ͠Δ θϩΛؚΜͰ͍ΔͨΊ ൃࢄ͍ͯ͠ͳ͍
ϵΛมԽͤ͞Δ Kw (u, v) = 1 H(u, v) |H(u, v)|2
|H(u, v)|2 + Γ ਤ
ϵΛมԽͤ͞Δ Kw (u, v) = 1 H(u, v) |H(u, v)|2
|H(u, v)|2 + Γ ਤ Ӷʹ෮ݩ͞ΕΔ ϊΠζ૿෯͢Δ ϊΠζ૿෯͞Εͳ͍ ΅͚ɾͿΕͷ෮ݩ͕͍
ϵΛมԽͤ͞Δ Kw (u, v) = 1 H(u, v) |H(u, v)|2
|H(u, v)|2 + Γ ਤ Ӷʹ෮ݩ͞ΕΔ ϊΠζ૿෯͢Δ ϊΠζ૿෯͞Εͳ͍ ΅͚ɾͿΕͷ෮ݩ͕͍ ϵ͕େ͖͘ͳΔͱ͕େ͖͘ͳΔͷͰ
ϵΛมԽͤ͞Δ Kw (u, v) = 1 H(u, v) |H(u, v)|2
|H(u, v)|2 + Γ ਤ Ӷʹ෮ݩ͞ΕΔ ϊΠζ૿෯͢Δ ϊΠζ૿෯͞Εͳ͍ ΅͚ɾͿΕͷ෮ݩ͕͍ ϵ͕େ͖͘ͳΔͱ͕େ͖͘ͳΔͷͰ ͜͜ͷ͕খ͘͞ͳͬͯ͋·ΓϑΟϧλ͕ޮ͔ͳ͘ͳΔ