2 CAD : CAD 7

Size: px
Start display at page:

Download "2 CAD : CAD 7"

Transcription

1 1 CAD

2 2 CAD : CAD 7

3 3 CAD

4 :

5 :

6

7 F (x, y) = 0 t (x(t), y(t)) xy

8 t (x(t), y(t), z(t)) xyz

9 z = f(x, y) F (x, y, z) = 0 (x(u, v), y(u, v), z(u, v))

10 0 10 )

11 (, 0), (, 0) 5 (, 0, 0), (0,, 0), (0, 0, ) : 3 3

12

13 (a, b) a, b a, b (a, b) = 0 a, b a, b = 0 a u (u, a) a u 1.2 a, b a, b a, b 1.3 a, b a, b a, b a, b 1.2 (1.15) P n a n P n a 1.6(a) (1.16) R(θ) a θ R(θ)a

14 h n (1.27) 1 r 0 u (1.29) xy O θ t x y (1.40), (1.42), (1.43) O θ t (1.41), (1.44), (1.45) 1.5 F (x, y) = 0 t x = x(t), y = y(t) F (1.53) F (1.59) x = x(t), y = y(t) (1.57), (1.59) k k

15 (1.72) s (1.73) κ s x = x(s), y = y(s) (1.74) κ a, b,... (geometricalgebar) [65] a a R Ra (versor) R RaR. 20 (1.8) a, b a, b 2 2 a, b a, b

16 a, b, c a, b, c a, b, c c a, b a b c = a b a, b a, b a, b, c 2.1(a) a, b, c a, b, c 2.1(b) a, b, c a, b, c a, b (a, b) = 0 a, b a b = 0 a, b, c a, b, c = (2.16) P n a n P n a 2.2(a) (2.17) R(n, Ω) a n Ω R(n, Ω)a a n ω / ȧ = ω a ω = ωn

17 m O n (1.29) m, n m 2 + n 2 = 1 n m 2.3(b) 1 r 0 u (1.28) 2.3(a) h n 2.5(b) xyz O R t x y z O R t 2.6 t x = x(t), y = y(t), z = y(t) (2.58) x = x(t), y = y(t), z = z(t) (2.60), (2.61)

18 18 k k s (2.68) κ τ s x = x(s), y = y(s), z = z(s) (2.70) κ τ (2.13) a, b, c a, b, c a, b, c (Sir William Rowan Hamilton: ) (quaternion) q = q 0 + q 1 i + q 2 j + q 3 k z = x + iy i, j, k i 2 = j 2 = k 2 = 1 (Hermann Günter Grassmann: ) (Grassmann algebra) 2 p 1, p 2 l l = p 1 p 2 3 p 1, p 2, p 3 Π Π = p 1 p 2 p 3 (outer product)

19 19 (Josiah Willard Gibbs: ) (a, b) a b a, b, c (vector calculus) 3 (David Orlin Hestenes: 1933 ) [65] (join) (meet) (dual plane) (dual point) (dual line) 2 (duality theorem) 3 (2.20) (Benjamin Olinde Rodrigues: ) (Rodrigues formula) 3 (2.17) (2.22) binormal (moving frame)

20 xyz z = f(x, y) F (x, y, z) = 0 u, v x = x(u, v), y = y(u, v), z = z(u, v) 3.2 z = f(x, y) f(x, y) 3.3 xy (3.13) H K (K > 0) (K = 0) (K < 0) 3.5

21 F (x, y, z) = 0 F (3.35) F (3.36) κ H K 2 F F (3.40) (3.42) 3.6 x = x(u, v), y = y(u, v), z = z(u, v) x, y, z u, v e 1, e 2 (3.53) n (3.54) e 1, e 2, n {e 1, e 2, n} (3.60) κ H K {e 1, e 2, n} (3.78), (3.79) 3.7 {e 1, e 2, n} (u, v) e i / u j, n/ u j u = u 1, v = u 2 (3.91), (3.92) j k ij ff 2

22 22 (3.94) (3.95) 2 3 (differential geometry) (3.15) (total curvature) (Gaussian curvature) 3.3 (Jan Johan Koenderink: 1943 )[28] 3.6 (u, v) (tensor calculus) 3 CAD (Albert Einstein: ) (1936 ) (statistical geometry)

23

24 S ,

25 uv xyz 2 u, v 3 u, v 2 x, y, z 4 x, y, z x f(x) = 0, g(x) = 0 x 2 x, y f(x, y) = 0, g(x, y) = 0, h(x, y) = 0 x, y [27 32] (catastorophy) 1970 (René Frédéric Thom: )[52]

26 26 4.5

27 (5.4) L, l v L p p v l P (5.10) 1

28 (, 0) (0, ) 2 (0, 0) (1, 1) 1 (5.13) (5.15) Π, π v Π p p v π P (5.17) 2

29 (5.21), (5.22), (5.23) x 2 + y 2 = (a)

30 (b) (, 0, 0), (0,, 0) (0, 0, ) 2 (0, 0, 0) (1, 1, 1) 1 (5.44) (5.47) (5.49) 3

31 (5.55), (5.56), (5.57) x 2 + y 2 + z 2 = )

32 Π v p p v Π P 2 2 Π v π Π p p v π P p 2 π 2 π π π π CAD (homogeneous coordinate) CAD (projective space) (topology) 1 (point at infinity) 2 (linet at infinity) 2 3 (planet at infinity)

33 33 CAD 5.10 (x, y, z) (x, y, z ) x y = A z A (5.51) B x y z 1 x y z 1 = x x x y y BA z C,..., D y z = D BA z CAD [ x y z 1] [ x y z 1] = [ x y z 1 ]A 4 4 B [ x y z 1] = [ x y z 1 ]AB C,..., D [ x y z 1] = [ x y z 1 ]ABC D CAD x y z 1 ( x y z 1) ABC A B C...

34 34 6 : A : (x A, y A ), B : (x B, y B ), C : (x C, y C ) (x, y) (barycentric coordinates) (α, β, γ) (x, y) ( ) ( ) ( ) ( ) x x A x B x C = α + β + γ, α + β + γ = 1 (6.1) y y B y C y C x A x B x C α x y A y B y C β = y. (6.2) γ α = 1 x x B x C y y D B y C, β = 1 x A x x C y D A y y C, γ = 1 D x A x B x C D = y A y B y C x A x B x y A y B y 1 1 1, (6.3)

35 6 : 35 A A γ > 0 P β > 0 γ > 0 β < 0 P B α > 0 B α > 0 C C (a) (b) 6.1: P (a) P ABC (a) P ABC 3 A : (x A, y A ), B : (x B, y B ), C : (x C, y C ) (x, y) (α, β, γ) (6.3) (α, β, γ) (x, y) (6.1) (6.1) 6.1 ABC (1/3, 1/3, 1/3) 6.2 P (α, β, γ) A, P BC D B, P CA E C, P AC F 6.1 α : β : γ = P BC : P CA : P AB (6.4) 6.1 ABC ABC (α, β, γ) (area coordinages) (x, y) ABC α 0, β 0, γ 0 α > 0, β > 0, γ > (x, y) BC, CA, AB α = 0, β = 0, γ = 0 α < 0 (x, y) BC A β < 0 CA B γ < 0 AB C , 6.2 (6.3) (α, β, γ) 1.1

36 6 : 36 β = 0 α > 0 β < 0 γ < 0 γ = 0 α = 0 α > 0 β > 0 γ < 0 B A α > 0 β > 0 γ > 0 α > 0 β < 0 γ > 0 α < 0 β > 0 γ < 0 α < 0 β > 0 γ > 0 C α < 0 β < 0 γ > 0 6.2: BC, CA, AB α = 0, β = 0, γ = n r r (subspace) r (affine space) r (flat) n n 1 (hyperplane) n N n N r 1,..., r N c 1 x c N x N (6.5) c c N = 1 (affine combination) r 1,..., r N r 1,..., r N (6.5) c c N = 1 c 1 0,..., c N 0 (convex conbination) r 1,..., r N (convex hull) r 1,..., r N (convex set) 2 (6.1) n (general position) n + 1 n 6.2 n + 1 n (simplex)

37 6 : 37 - A A - B (α, β, γ ) (α, β, γ ) C - C B 6.3: Ā B C ABC 6.4: 6.2 Ā B C ABC Ā B C (α, β, γ) ABC (α, β, γ) 6.3 ( x, ȳ) (x, y) x = a 11 x + a 12 ȳ + a 13, y = a 21 x + a 22 ȳ + a 23 (6.6) 2 (affine transformation) (5.14) (6.6) (5.18) x a 11 a 12 a 13 x y = a 21 a 22 a 23 ȳ (6.7) (6.6), (6.7) ( x, ȳ) (x, y)

38 6 : 38 ABC (x, y) 1. (x, y) ABC (α, β, γ) ( (6.3)) 2. α 0, β 0, γ 0 Ā B C (α, β, γ) ( x, ȳ) ( (6.1)) 3. ( x, ȳ) (x, y) Ā B C ABC (texture mapping) 6.3 Ā B C ( x, ȳ) ABC (x, y) (x, y) ( x, ȳ) R G B (x, y) (6.6), (6.7) ( x, ȳ) (x, y) 1 (x, y) 2 (x, y) ( x, ȳ) ( x, ȳ) 4 ( x, ȳ) (i, j) ξ = x i, η = ȳ j (i, j) I(i, j) ( x, ȳ) 6.4 I( x, ȳ) = (1 ξ)(1 η)i(i, j) + ξ(1 η)i(i + 1, j) + (1 ξ)ηi(i, j + 1) + ξηi(i + 1, j + 1) (6.8)

39 6 : 39 (i, j) η 1-η (i, j+1) ξ 1-ξ (i+1, j) η 1-η (i+1, j+1) 6.5: I(i, j) I(i, j + 1) η : 1 η I(i + 1, j) I(i + 1, j + 1) η : 1 η ξ : 1 ξ I(i, j) I(i, j + 1) η : 1 η I(i + 1, j) I(i + 1, j + 1) η : 1 η ξ : 1 ξ 6.5 ξ : 1 ξ η : 1 η (bilinear interpolation) (a) 3 6.6(b) 6.6(a) 6.6(b) 6.6(a) (rendering)

40 6 : 40 3 (smooth shading) 3 3 CAD 3 (computer vision) 3 3 (3D reconstruction) 6.3 (5.13) (projective transformation, homography) 1 1 x = h 11x + h 12 y + h 13 h 31 x + h 32 y + h 33, ȳ = h 21x + h 22 y + h 23 h 31 x + h 32 y + h 33 (6.9) 6.7 (x, y), ( x, ȳ) X : Y : Z, X : Ȳ : Z 5.6 x = X/Z, y = Y/Z, x = X/ Z, ȳ = Ȳ / Z (5.17) X h 11 h 12 h 13 X Ȳ = k h 21 h 22 h 23 Y (6.10) Z h 31 h 32 h 33 Z k (projection transformation matrix, homography matrix) (6.9), (6.10) (6.6), (6.7) h 31 = h 32 = 0 3 {a 1, a 2, a 3 } (reciprocal system) {ã 1, ã 2, ã 3 } (a) (b) 6.6: (a) 3 (b) 3

41 6 : 41 ã 1 = a 2 a 3 a 1, a 2, a 3, ã 2 = a 3 a 1 a 1, a 2, a 3, ã 3 = a 1 a 2 a 1, a 2, a 3 (6.11) 6.3 {a 1, a 2, a 3 } {ã 1, ã 2, ã 3 } (a i, ã j ) = δ ij (6.12) δ ij i = j 1 i 0 0 (3.58), (3.74) (6.12) a 2 a 3 a 2, a a 1, a 2, a 3 (a 1 a 2, a 3 ) (2.13) a 1, ) a 2, a 3 A = (a 1 a 2 a 3 ã 1, ã 2, ã 3 ) (ã à = 1 ã 2 ã 3 (6.12) A à = I ( ) ) 6.3 à = ã 1 ã 2 ã 3 A = (a 1 a 2 a à = (A ) 1 (= (A 1 ) ) (6.13) ABC Ā B C A, B, C X A : Y A : Z A, X B : Y B : Z B, X C : Y C : Z C Ā, B, C X : Ā Y : ZĀ, X Ā B : Y B : Z B, X C : Y C : Z C {(X A, Y A, Z A ), (X B, Y B, Z B ), (X C, Y C, Z C ) } {( X A, ỸA, Z A ), ( X B, ỸB, Z B ), ( X C, ỸC, Z C ) } 6.4 αβγ 0 α, β, γ X Ā X A H = α Y Ā Ỹ A Z A Z Ā X B + β Y B Z B X B Ỹ B Z B + γ X C Y C Z C X C Ỹ C Z C (6.14) 3 A, B, C 3 Ā, B, C 6.7:

42 6 : (6.10) H α, β, γ α + β + γ = 1 (6.14) (α, β, γ) ABC Ā B C 6.4 (6.14) α = β = γ 6.4 ABCD Ā B C D ABCD Ā B C D 3 3 A, B. C 3 Ā, B. C (6.14) α, β, γ D D 6.6 (6.14) H D D α, β, γ W A = X A X D + ỸAY D + Z A Z D, W B = X B X D + ỸBY D + Z B Z D, W C = X C X D + ỸCY D + Z C Z D (6.15) X W Ā A X BW B X CW C α Y W Ā A Y BW B Y CW C β = Z W Ā A Z BW B Z CW C γ 6.5 X D Y D Z D (6.16) (6.16) ABCD Ā B C D 4 A, B, C, D 4 Ā, B, C, D 6.3 (6.9) 6.11 Ā B C D, ABCD 4 Ā, B. C, D (xā, y Ā ), (x B, y B ), (x C, y C ), (x D, y D ) 4 A, B. C, D (x A, y A ), (x B, y B ), (x C, y C ), (x D, y D ) X A x A Y A = y A, 1 Z A X B Y B Z B = x B y B 1, X C Y C Z C = x C y C 1,

43 6 : 43 X Ā Y Ā Z Ā = x Ā y Ā 1, X B Y B Z B = x B y B 1, X C Y C Z C = x C y C 1 (6.17) Ā B C D ABCD (6.17) (6.15) W A, W B, W C (6.16) α, β, γ (6.14) H ABCD (x, y) 1. (x, y) ABCD ( x, ȳ) x x ȳ = Z[H y ] (6.18) 1 1 Z[ ] 3 1 Z[(X, Y, Z) = (X/Z, Y/Z, 1) 2. Ā B C D ( x, ȳ) (x, y) (6.17), (6.18) Z 1 Z[ ] (6.10) k (x, y) ABCD (x, y) ABC BCD 6.1 (6.14) ABC Ā B C Ā B C D ABCD α, β, γ (6.16) D D 6.3 RGB ( x, ȳ) (x, y) ( x, ȳ) 1

44 6 : 44 (a) (b) 6.8: (a) (b) 3 6.8(b) 3 6.8(a) 6.8(b) 6.8(b) (b) 6.8(a) (projection mapping) (calibration) m

45 6 : 45 (a) (b) 6.9: (a) 2 4 (b) (image mosaicing) (mosaicing) (6.4) 6.2. (6.6) (6.6) 3 Ā : (xā, yā), B : (x B, y B), C : (x C, y C) 3 A : (x A, y A ), B : (x B, y B ), C : (x C, y C ) a 11, a 12,..., a (6.8) 6.5. (6.9) (6.10) 6.6. (A ) 1 = (A 1 )

46 (6.14) 3 A, B, C 3 Ā, B, C A, B, C, D Ā, B, C, D H (6.14) α, β, γ (6.16) (6.9) A : (x A, y A ), B : (x B, y B ), C : (x C, y C ), D : (x D, y D ) Ā : (xā, yā), B : (x B, y B), C : (x C, y C), D : (x D, y D) h 11, h 12,..., h ABC ABC ABC

47 (voxel) CT MRI (volume data) (solid texture) 3 (3D texture) 3

48 48 [49] 3 [67] 6.4 (video mapping) [69] [71] HMD (virtual reality) (feature point) (Harris operator)[57] SIFT (SIFT operator)[70] (descriptor) RANSAC [56] 4 [67] [67]

49 (transformation) (group of transformation) xy 7.2 (Euclidean transformation) ( ) x y = ( cos θ sin θ ) ( ) ( sin θ x + cos θ y t 1 t 2 ) (7.1)

50 : (rigid motion) (motion) (congruence transformation) (x, y) O θ (t 1, t 2 ) (7.1) x cos θ sin θ t 1 x y = sin θ cos θ t 2 y (7.2) (group of Euclidean transformations) (congruent) (Euclidean geometry) O x xy θ (t 1, t 2 ) x y 7.2 xy (x, y) x y (x, y ) (x, y) (x, y ) (7.1) θ (t 1, t 2 ) ( t 1, t 2 ) θ (1.40), (1.41) 1.10

51 7 51 y y O 1 2 x 1 O 1 2 x 7.2: 7.3 (similar transformation) ( ) ( x cos θ y = s sin θ ) ( ) ( sin θ x + cos θ y t 1 t 2 ), s 0 (7.3) (x, y) O θ s (t 1, t 2 ) (7.3) x s cos θ s sin θ t 1 x y = s sin θ s cos θ t 2 y, s 0 (7.4) (group of similar transformations) s = 1 (similar) (similar geometry) 7.3:

52 7 52 y y O 1 2 x 1 O 1 2 x 7.4: xy s θ (t 1, t 2 ) x y 7.4 xy (x, y) x y (x, y ) (x, y) (x, y ) (7.3) (Euclidean reconstruction) 7.4 (similar transformation) (5.14), (6.6) ( ) ( ) ( ) ( x a 11 a 12 x y = + a 21 a 22 y a 13 a 23 ), a 11 a 22 a 12 a 21 0 (7.5)

53 : (7.5) x a 11 a 12 a 13 x y = a 21 a 22 a 23 y, a 11 a 22 a 12 a 21 0 (7.6) (group of affine transformations) (7.5) s ( 0) s = 1 (affine geometry) xy x y 7.6 xy (x, y) x y (x, y ) (x, y) (x, y ) (7.5) (oblique coordinate system) (orthogonal coordinate system) (Cartesian coordinate sytem) 7.5 N (x 1, y 1 ),..., (x N, y N ) (x 1, y 1 ),..., (x N, y N ) N = N

54 7 54 y y O 1 2 x 1 O 1 2 x 7.6: 7.1 x = 1 N N x α, α=1 ȳ = 1 N N y α, α=1 x = 1 N N x α, α=1 ȳ = 1 N N y α (7.7) α=1 (7.5) x, y, x, y x α, y α, x α, y α (1/N) N α=1 ( ) ( ) ( ) ( ) 1 N x α = 1 N a 11 a 12 x α a 13 + N N a 21 a 22 y α a 23 α=1 y α α=1 ( ) ( ) ( ) ( x a 11 a 12 x ȳ = + a 21 a 22 ȳ a 13 a 23 ( x, ȳ) ( x, ȳ ) ) (7.8) (7.9) x α, y α, x α, y α (7.5) (7.9) ( x α x y α ȳ ) ( ) ( a 11 a 12 = a 21 a 22 x α x y α ȳ α = 1,..., N ( ) ( ) ( ) x 1 x x N x a 11 a 12 x 1 x x N x y 1 ȳ y N = ȳ a 21 a 22 y 1 ȳ y N ȳ ) (7.10) (7.11)

55 7 55 ( x 1 x x N x y 1 ȳ y N ȳ ) x 1 x y 1 ȳ ( ) ( a 11 a 12 x 1 x x N x = a 21 a 22 y 1 ȳ y N ȳ.. x N x y N ȳ ) x 1 x y 1 ȳ. x N x. y N ȳ (7.12) N ( N α=1 (x α x )(x α x)/n N α=1 (x α x )(y α ȳ)/n N α=1 (y α ȳ )(x α x)/n N α=1 (y α ȳ )(y α ȳ)/n = ( a 11 a 12 a 21 a 22 ) ( N α=1 (x α x) 2 /N N α=1 (y α ȳ)(x α x)/n ) ) N α=1 (x α x)(y α ȳ)/n N α=1 (y α ȳ) 2 /N (7.13) ( ) ( N a 11 a 12 α=1 = (x α x )(x α x)/n ) N α=1 (x α x )(y α ȳ)/n a 21 a N 22 α=1 (y α ȳ )(x α x)/n N α=1 (y α ȳ )(y α ȳ)/n ( N α=1 (x α x) 2 ) /N N 1 α=1 (x α x)(y α ȳ)/n (7.14) N α=1 (y α ȳ)(x α x)/n N α=1 (y α ȳ) 2 /N a 11, a 12, a 21, a 22 a 13, a 23 (7.9) ( ) ( ) ( x = ȳ a 13 a 23 a 11 a 12 a 21 a 22 ) ( ) x ȳ (7.15) (7.14) s s s 2 s (7.5) (7.6) σ (likelihood) (maximum likelihood estimation) (7.13) {(x α, y α )} (covariance matrix) {(x α, y α )}, {(x α, y α)} (correlation matrix) 7.3

56 7 56 s s = a a a a (7.16) (7.14) s A R A (singular value decomposition) A = U ( ) σ1 0 V (7.17) 0 σ 2 U, V σ 1 σ 2 ( 0) (singular value) R = UV (7.18) σ 1, σ 2 A σ 1 1, σ R (7.16) (7.18) (7.17), (7.18) (projective transformation, homography) (6.9), (5.13) x = h 11x + h 12 y + h 13 h 31 x + h 32 y + h 33, y = h 21x + h 22 y + h 23 h 31 x + h 32 y + h 33 (7.19) :

57 7 57 (7.19) (5.17) 6.5 X h 11 h 12 h 13 X Ȳ = k h 21 h 22 h 23 Y (7.20) Z h 31 h 32 h 33 Z k 0 (group of projective transformations) (7.19) h 33 = h 32 = 0, h 33 0 (projective geometry) (cross ratio) 5.1 xy x y 7.8 xy (x, y) x y (x, y ) (x, y) (x, y ) (7.19) 5.4 x y (0,0) ( (1,1) ( (, 0), (0, ) ( (1,1) 4 x (, 0) x x x (, 0) (1, 1) x y (0, ) y y y oo y O 1 2 x O 1 2 x oo 7.8: (0, 0) (1, 1) (, 0), (0, ) (, 0), (0, )

58 7 58 (, 0), (0, ) x, y N (x 1, y 1 ),..., (x N, y N ) (x 1, y 1 ),..., (x N, y N ) N = N (7.19) N projective affine similar Eculidean identity 7.9:

59 7 59 (group of translations) (group of rotations) (invariant) (invariance) (Felix Christian Klein: ) (Erlangen program) , (residual) 7.9 A B 7.10

60 7 60 class A class B data 7.10: B A B A (degree of freedom) 9 8 6, 4, 3 (overfitting) 0 (geometric model selection) (model selection criterion) J = ( ) + ( ) (7.21) (penalty term) (penalty term) J AIC(geometric AIC) BIC(geometric BIC) MDL(geometric MDL) 7.1. (7.14), (7.15) 2 a 11,..., a 23 ( ) J = 2 N x ( ( ) ( ) ( α a 11 a 12 x α + N a 21 a 22 y α α=1 y α a 13 a 23 ) ) 2 2/N (7.22)

61 (7.11) (7.14) (7.6) a 11,..., a (7.19) N (x 1, y 1 ),..., (x N, y N ) N (x 1, y 1 ),..., (x N, y N ) O θ (t 1, t 2 ) (7.1) O θ s ( 0) (t 1, t 2 ) (7.3) 2

62 (7.5) (x α, y α ) (x α, y α), α = 1,..., N 3 (N = 3) N > (7.14), (7.15)

63 (7.19) N (> 4) (7.17), (7.18) [23] [61] [63]

64 64 [67] 7.9 [58, 59] AIC BIC MDL [60] [64] [62] Triono [72] [68] [66] 3 GPS

65 65 [56] M. A. Fischler and R. C. Bolles: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, 24-6(1981), pp [57] C. Harris and M. Stephens: A combined corner and edge detector, Proceedings of the 4th Alvey Vision Conference, August 1988, Manchester, U.K., pp [58] K. Kanatani: Comments on Symmetry as a Continuous Feature, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19-3 (1997), pp [59] K. Kanatani: Comments on Nonparametric Segmentation of Curves into Various Representations, IEEE Transactions on Pattern Analysis and Machine Intelligence, (1997), pp [60] K. Kanatani: Geometric information criterion for model selection, International Journal of Computer Vision, 26-2 (1998), pp [61] (2003). [62] K. Kanatani: Uncertainty modeling and model selection for geometric inference, IEEE Transactions on Pattern Analysis and Machine Intelligence, (2004), pp [63] (2005). [64] K. Kanatani: Geometric BIC, IEICE Transaction on Information & Systems, E93-D-1 (2010), pp [65] Geometric Algebra:, (2014). [66] K. Kanatani and C. Matsunaga: Computing internally constrained motion of 3-D sensor data for motion interpretation, Pattern Recognition, 46-6 (2013), pp [67] 3, (2016).

66 66 [68] AIC A Vol. J83-A-6 (2000), pp [69] Vol. J83-12 (2000), pp [70] D. Lowe: Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, 60-2 (2004), pp [71] M. Sakamoto, Y. Sugaya, and K. Kanatani: Homography optimization for consistent circular panorama generation, Proceedings of the 2000 IEEE Pacific-Rim Symposium on Image and Video Technology, December 2006, Hsinchu, Taiwan, pp [72] I. Triono, N. Ohta and K. Kanatani: Automatic recognition of regular figures by geometric AIC, IEICE Transactions on Information and Systems, E81-D-2 (1998), pp

67 (6.3) α α = 1 x x B x x C x D y y B y y C y = 1 D x B x x C x y B y y C y = 1 ( ) ( ) xb x xc x D, = 1 P B, P C y B y y C y D 1 2, 3 (3,3) (1.8) 1.1 P B, P C α P BC 2/D β P CA 2/D γ P AB 2/D 1.1 P ABC 6.1(b) 7.2. (6.3) (x, y) (α, β, γ) (x, y) 1 (6.1) (α, β, γ) (x, y) (α, β, γ) 1 ( x, ȳ) (α, β, γ) (x, y) (x, y) (6.6) ( x, ȳ) Ā, B, C A, B, C (6.6) x A = a 11 x Ā + a 12 y Ā + a 13, y A = a 21 x Ā + a 22 y Ā + a 23, x B = a 11 x B + a 12 y B + a 13, y B = a 21 x B + a 22 y B + a 23, x C = a 11 x C + a 12 y C + a 13, y C = a 21 x C + a 22 y C + a 23 xā y Ā 1 x B y B 1 a 11 a 12 = x A x B, x C y C 1 a 13 x C xā y Ā 1 x B y B 1 a 21 a 22 = y A y B x C y C 1 a 23 y C 1 a 11, a 12,..., a 23 Ā B C I(i, j) I(i, j + 1) η : 1 η I(i + 1, j) I(i + 1, j + 1) η : 1 η

68 68 ξ : 1 ξ ( ) ( ) I( x, ȳ) = (1 ξ) (1 η)i(i, j) + ηi(i, j + 1) + ξ (1 η)i(i + 1, j) + ηi(i + 1, j + 1) = 1 ξ)(1 η)i(i, j) + ξ(1 η)i(i + 1, j) + (1 ξ)ηi(i, j + 1) + ξηi(i + 1, j + 1) ξ : 1 ξ η : 1 η 7.5. X : Ȳ : Z = x : ȳ : 1 = h 11X/Z + h 12 Y/Z + h 13 : h 21X/Z + h 22 Y/Z + h 23 : 1 h 31 X/Z + h 32 Y/Z + h 33 h 31 X/Z + h 32 Y/Z + h 33 X = h 11 Z + h Y 12 Z + h X 13 : h 21 Z + h Y 22 Z + h X 23 : h 31 Z + h Y 32 Z + h 33 = h 11 X + h 12 Y + h 13 Z : h 21 X + h 22 Y + h 23 Z : h 31 X + h 32 Y + h 33 Z 0 k X = k(h 11 X + h 12 Y + h 13 Z), Ȳ = k(h 21 X + h 22 Y + h 23 Z), Z = k(h31 X + h 13 Y + h 33 Z) (6.10) 7.6. AA 1 = I (A 1 ) A = I (A 1 ) A (A 1 ) = (A ) A H X A Y A Z A ( = α XĀ Y Ā Z Ā X Ā = α Y Ā Z Ā X A Ỹ A Z A 1 + β + β X B X B Y B Ỹ B Z B Z B X C 0 + γ Y C X B Y B Z B Z C + γ X C Y C Z C X C ) Ỹ C Z C X A Y A Z A 0 (7.23) Ā B, C B, C 7.8. (6.14) H αβγ 0 α, β, γ α + β + γ = 1 (X A, Y A, Z A ) = (x A, y A, 1), (X B, Y B, Z B ) = (x B, y B, 1), (X C, Y C, Z C ) = (x C, y C, 1) (X Ā, ȲĀ, Z Ā ) = (x Ā, y Ā, 1), (X B, Y B, Z ) = (x B, y B, 1), (X C, Y C, Z C) = (x C, y C, 1) H x A y A = α xā y Ā1, H x B y B = β x B y, H x C y C 1 1 B1 1 = γ x C y C1

69 69 ABC G : (x G, y G ) H Ḡ : (xḡ, y Ḡ ) k 0 x Ḡ x G (x A + x B + x C )/3 yḡ1 = kh y G = kh (y A + y B + y C )/3 = k ( x A x B 3 H y A + y B + y C = k ( α xā y 3 Ā1 + β x B y + γ x C y ) B1 C1 x C ) 3 α + β + γ = 1 k = 3 ( ) xḡ xḡ ( ) ( ) ( ) xā x = α + β B x + γ C yā y B y C (6.1) α, β, γ Ḡ Ā B C 7.9. H Ḡ (α, β, γ) (1, 1, 1) (α, β, γ) = (1, 1, 1) G Ḡ A, B, C, G Ā, B, C, Ḡ A, B, C A, B, C, G Ā, B, C, Ḡ A, B, C, G Ā, B, C, Ḡ H (α, β, γ) = (1, 1, 1) α + β + γ = 1 α, β, γ α = β = γ H D D X D X D Y D = kh Y D Z D k 0 H (6.14) α, β, γ k (6.14) X D Y D Z D Z Ā Z D ( = α XĀ X A Y Ā Ỹ A + β X B X B Y B Ỹ B + γ Z Ā Z A Z B Z B X Ā X B X C = α Y Ā W A + β Y B W B + γ Y C W C (6.15) (6.16) Z B (6.9) 1 Z C X C Y C Z C X C ) Ỹ C Z C X D Y D Z D h 11 x + h 12 y + h 13 = h 31 xx + h 32 xy + h 33 x

70 70 A, B, C, D Ā, B, C, D h 11 x A + h 12 y A + h 13 = h 31 x A x A + h 32 x A y A + h 33 x A h 11 x B + h 12 y B + h 13 = h 31 x B x B + h 32 x B y B + h 33 x B h 11 x C + h 12 y C + h 13 = h 31 x C x C + h 32 x C y C + h 33 x C h 11 x D + h 12 y D + h 13 = h 31 x D x D + h 32 x D y D + h 33 x D (6.9) 2 h 21 x + h 22 y + h 23 = h 31 ȳx + h 32 ȳy + h 33 ȳ A, B, C, D Ā, B, C, D h 21 x A + h 22 y A + h 23 = h 31 ȳ A x A + h 32 ȳ A y A + h 33 ȳ A h 21 x B + h 22 y B + h 23 = h 31 ȳ B x B + h 32 ȳ B y B + h 33 ȳ B h 21 x C + h 22 y C + h 23 = h 31 ȳ C x C + h 32 ȳ C y C + h 33 ȳ C h 21 x D + h 22 y D + h 23 = h 31 ȳ D x D + h 32 ȳ D y D + h 33 ȳ D 1 x A y A x A x A x A y A h 11 x A x B y B x B x B x B y B h 12 x B x C y C x C x C x C y C h 13 x C x D y D x D x D x D y D h x A y A 1 ȳ A x A ȳ A y A h 22 = h 33 x D ȳ A x B y B 1 ȳ B x B ȳ B y B h 23 ȳ B x C y C 1 ȳ C x C ȳ C y C h 31 ȳ C x D y D 1 ȳ D x D ȳ D y D h 32 ȳ D 0 h 11, h 12,..., h 33 h 33 = 1 0 h 33 = 0 0 h 11, h 12,..., h (7.22 J = 2 N N ( ) ( ) a11 a ( 12 xα + a 22 y α α=1 a 21 ( a13 7 a 23 ) ( x α y α a 13 J = 1 N ( ) ( ) a11 a ( 12 xα + a 13 N a 21 a 22 y α = 1 N α=1 ) ( ) ( ) ( ) ( ) a11 a, 12 xα a13 x + α a 21 a 22 y α a 23 y α ) ( a13 N (a 11 x α + a 12 y α + a 13 x α) α=1 a 23 ) ( x α y α ) ( ) 1, ) 0

71 71 a 23 J = 1 N (a 21 x α + a 22 y α + a 23 y a 23 N α) α=1 0 ( ) ( ) a11 a 12 x + a 22 ȳ a 21 ( a13 a 23 ) ( ) ( ) x 0 ȳ = 0 (7.9) (7.9) J J = 2 N N ( a11 a ( 12 α=1 a 21 a 22 ) ( xα x y α ȳ ) a 11 J = 1 N ( a11 a ( 12 a 11 N a 21 a 22 = 1 N α=1 ( x α x y α ȳ ) ( xα x y α ȳ ) ) ( ) ( ) a11 a, 12 xα x a 21 a 22 y α ȳ ( x α x y α ȳ ), ( ( x α x ) ( ) xα x ) y α ȳ N (a 11 (x α x) 2 + a 12 (x α x)(y α ȳ) (x α x )(x α x)) α=1 a 12, a 21, a 12 J a 12 = 1 N J a 21 = 1 N J a 22 = 1 N N (a 11 (x α x)(y α ȳ) + a 12 (y α ȳ) 2 (x α x )(y α ȳ)) α=1 N (a 21 (x α x) 2 + a 22 (x α x)(y α ȳ) (y α ȳ )(x α x)) α=1 N (a 21 (x α x)(y α ȳ) + a 22 (y α ȳ) 2 (y α ȳ )(y α ȳ)) α=1 0 ( ) ( N a11 a 12 α=1 (x α x) 2 ) N /N α=1 (x α x)(y α ȳ)/n a 21 a N 22 α=1 (x N α x)(y α ȳ)/n α=1 (x α x) 2 /N ( N α=1 (x α x )(x α x))/n ) N α=1 (x α x ( ) )(y α ȳ)/n 0 0 N α=1 (y α x )(x α x)/n = N α=1 (y α x )(y α ȳ)/n 0 0 (7.13) y α ȳ 7.2. (x α, y α ), (x α, y α), α = 1,..., N (7.6) x 1 x N a 11 a 12 a 13 x 1 x y 1 y N N = a 21 a 22 a 23 y 1 y N x 1 x x 1 y 1 1 N y 1 y N... = a 11 a 12 a 13 a 21 a 22 a 23 x 1 x N x 1 y 1 1 y 1 y N x N y N x N y N 1 N N α=1 x αx α /N N α=1 x αy α /N x N α=1 y αx α /N N α=1 y αy α /N ȳ = a N 11 a 12 a 13 α=1 a 21 a 22 a 23 x2 α/n N α=1 x αy α /N x N α=1 x ȳ x αy α /N N α=1 y2 αn ȳ x ȳ 1 ) )

72 72 N a 11 a 12 a 13 α=1 x αx α /N N α=1 x αy α /N x N a 21 a 22 a 23 = N α= y αx α /N α=1 N x2 α/n N α=1 x αy α /N x α=1 y αy α /N ȳ N α=1 x αy α /N N α=1 y2 αn ȳ x ȳ 1 x ȳ J = 2 N x α y α a 11 a 12 a 13 a 21 a 22 a 23 x α y α 2 N α= (x α, y α ), (x α, y α) (7.19) 1 h 11 x α +h 12 y α +h 13 = h 31 x αx α +h 32 x α y α +h 33 x α, h 21 x α +h 22 y α +h 23 = h 31 y αx α +h 32 y αy α +h 33 y α α = 1,... N 1 x 1 y x 1x 1 x 1y 1 x x N y N x N x N x N y N x N x 1 y 1 1 y 1x 1 y 1y 1 y x N y N 1 y N x N y N y N y N 2 N 1 N h 11 h 12 h 13 h 21 h 22 h 23 h 31 h 32 h = h 11 x 1 x N 0 0 h 12 y 1 y N 0 0 h h x 1 x N h y 1 y N h h 31 x 1x 1 x N h 32 x N y 1x 1 y N x N x 1y 1 x N y N y 1y 1 y N y N h 33 x 1 x N y 1 y N x 1 y x 1x 1 x 1y 1 x h 11 1 h h 13 x N y N x N x N x N y N x h 21 N x 1 y 1 1 y 1x 1 y 1y 1 y 1 h 22 h x N y N 1 y N x N y N y h 31 N y N h 32 h 33 2 ( h 11. h 33, M h 11. h 33 ) ( )

73 73 M N N α=1 x2 α/n α=1 x αy α /N x N α=1 x N αy α /N α=1 y2 α/n ȳ x ȳ M = N α=1 x αx 2 α/n N α=1 x αx α y α /N N α=1 x αx α /N N α=1 x αx α y α /N N α=1 x αyα/n 2 N α=1 x αy α /N N α=1 x αx α /N N α=1 x αy α /N x N N α=1 x2 α/n α=1 x αy α /N x N α=1 x αy α /N N α=1 y2 α/n ȳ x ȳ 1 N α=1 y αx 2 α/n N α=1 y αx α y α /N N N α=1 y αx α y α /N N α=1 y αy 2 α/n N N α=1 y αx α /N N α=1 y αy α /N ȳ α=1 y αx α /N α=1 y αy α /N N α=1 x αx 2 α/n N α=1 x αx α y α /N N α=1 x αx α /N N α=1 x αx α y α /N N α=1 x αy 2 α/n N α=1 x αy α /N N α=1 x αx α /N N α=1 x αy α /N x N α=1 y αx 2 α/n N α=1 y αx α y α /N N α=1 y αx α /N N α=1 y αx α y α /N N α=1 y αyα/n 2 N α=1 y αy α /N N α=1 y αx α /N N α=1 y αy α /N ȳ N α=1 (x α 2 + y α 2 )x 2 α/n N α=1 (x α 2 + y α 2 )x α y α /N N α=1 (x α 2 + y α 2 )x α /N N α=1 (x α 2 + y α 2 )x α y α /N N α=1 (x α 2 + y α 2 )yα/n 2 N α=1 (x α 2 + y α 2 )y α /N N α=1 (x 2 α + y α 2 )x α /N N α=1 (x α 2 + y α 2 )y α /N N α=1 (x α 2 + y α 2 )/N (7.19) {h ij } h h 2 33 = 1 2 ( ) {h ij } M

74 74 Albert Einstein, 22 affine geometry, 53 affine space, 36 affine combination, 36 affine transformation, 37, 52 group of affine transformations, 53, 22 topology, 32 general position, 36 moving frame, 19 motion, 50 Erlangen program, 59 overfitting, 60 outer product, 18 group of rotations, 59 Gaussian curvature, 22 virtual reality, 48 image mosaicing, 45 catastorophy, 25 AIC geometric AIC, 60 MDL geometric MDL, 60 geometric algebra, 15, 19 BIC geometric BIC, 60 geometric model selection, 60 descriptor, 48 Josiah Willard Gibbs, 19 covariance matrix, 55 Felix Christian Klein: , 59 Hermann Günter Grassmann, 18 Grassmann algebra, 18 Cramer s formula, 34 join, 19 Jan Johan Koenderink, 22, 25 meet, 19 calibration, 44 rigid motion, 50 congruent, 50 congruence transformation, 50 computer vision, 40 maximum likelihood estimation, 55 residual, D texture, D reconstruction, 40 quaternion, 18 SIFT SIFT operator, 48 projective geometry, 57 projective space, 32 projective transformation, homography, 40, 56 projection transformation matrix, homography matrix, 40 group of projective transformations, 57

75 75 oblique coordinate system, 53 barycentric coordinates, 34 degree of freedom, 60 binormal, 19 smooth shading, 40 total curvature, 22 1 bilinear interpolation, 39 correlation matrix, 55 similar, 51 similar geometry, 51 similar transformation, 51 group of similar transformations, 51 dual line, 19 duality theorem, 19 dual point, 19 dual plane, 19 reciprocal system, 40 solid texture, 47 simplex, 36 hyperplane, 36 orthogonal coordinate system, 53 Cartesian coordinate sytem, 53 texture mapping, 38 tensor calculus, 22 statistical geometry, 22 homogeneous coordinate, 32 moving frame, 19 singular value, 56 singular value decomposition, 56 feature point, 48 convex conbination, 36 convex set, 36 convex hull, 36 René Frédéric Thom, 25 binormal, 19 penalty term, 60 Sir William Rowan Hamilton, 18 Harris operator, 48 video mapping, 48 differential geometry, 22 cross ratio, 57 subspace, 36 invariance, 59 invariant, 59 flat, 36 projection mapping, 44 group of translations, 59 vector calculus, 19 versor, 15 David Orlin Hestenes, 19 penalty term, 60 transformation, 49 group of transformation, 49 voxel, 47 volume data, 47 linet at infinity, 32 point at infinity, 32 planet at infinity, 32 area coordinages, 35

76 76 mosaicing, 45 model selection criterion, 60 Euclidean geometry, 50 Euclidean reconstruction, 52 Euclidean transformation, 49 group of Euclidean transformations, 50 likelihood, 55 RANSAC, 48 rendering, 39 Benjamin Olinde Rodrigues, 19 Rodrigues formula, 19 Rodrigues formula, 19

( ) ( )

( ) ( ) 20 21 2 8 1 2 2 3 21 3 22 3 23 4 24 5 25 5 26 6 27 8 28 ( ) 9 3 10 31 10 32 ( ) 12 4 13 41 0 13 42 14 43 0 15 44 17 5 18 6 18 1 1 2 2 1 2 1 0 2 0 3 0 4 0 2 2 21 t (x(t) y(t)) 2 x(t) y(t) γ(t) (x(t) y(t))

More information

(a) (b) (c) Canny (d) 1 ( x α, y α ) 3 (x α, y α ) (a) A 2 + B 2 + C 2 + D 2 + E 2 + F 2 = 1 (3) u ξ α u (A, B, C, D, E, F ) (4) ξ α (x 2 α, 2x α y α,

(a) (b) (c) Canny (d) 1 ( x α, y α ) 3 (x α, y α ) (a) A 2 + B 2 + C 2 + D 2 + E 2 + F 2 = 1 (3) u ξ α u (A, B, C, D, E, F ) (4) ξ α (x 2 α, 2x α y α, [II] Optimization Computation for 3-D Understanding of Images [II]: Ellipse Fitting 1. (1) 2. (2) (edge detection) (edge) (zero-crossing) Canny (Canny operator) (3) 1(a) [I] [II] [III] [IV ] E-mail [email protected]

More information

K E N Z OU

K E N Z OU K E N Z OU 11 1 1 1.1..................................... 1.1.1............................ 1.1..................................................................................... 4 1.........................................

More information

211 [email protected] 1 R *1 n n R n *2 R n = {(x 1,..., x n ) x 1,..., x n R}. R R 2 R 3 R n R n R n D D R n *3 ) (x 1,..., x n ) f(x 1,..., x n ) f D *4 n 2 n = 1 ( ) 1 f D R n f : D R 1.1. (x,

More information

x V x x V x, x V x = x + = x +(x+x )=(x +x)+x = +x = x x = x x = x =x =(+)x =x +x = x +x x = x ( )x = x =x =(+( ))x =x +( )x = x +( )x ( )x = x x x R

x V x x V x, x V x = x + = x +(x+x )=(x +x)+x = +x = x x = x x = x =x =(+)x =x +x = x +x x = x ( )x = x =x =(+( ))x =x +( )x = x +( )x ( )x = x x x R V (I) () (4) (II) () (4) V K vector space V vector K scalor K C K R (I) x, y V x + y V () (x + y)+z = x +(y + z) (2) x + y = y + x (3) V x V x + = x (4) x V x + x = x V x x (II) x V, α K αx V () (α + β)x

More information

1 nakayama/print/ Def (Definition ) Thm (Theorem ) Prop (Proposition ) Lem (Lemma ) Cor (Corollary ) 1. (1) A, B (2) ABC

1   nakayama/print/ Def (Definition ) Thm (Theorem ) Prop (Proposition ) Lem (Lemma ) Cor (Corollary ) 1. (1) A, B (2) ABC 1 http://www.gem.aoyama.ac.jp/ nakayama/print/ Def (Definition ) Thm (Theorem ) Prop (Proposition ) Lem (Lemma ) Cor (Corollary ) 1. (1) A, B (2) ABC r 1 A B B C C A (1),(2),, (8) A, B, C A,B,C 2 1 ABC

More information

2 1 κ c(t) = (x(t), y(t)) ( ) det(c (t), c x (t)) = det (t) x (t) y (t) y = x (t)y (t) x (t)y (t), (t) c (t) = (x (t)) 2 + (y (t)) 2. c (t) =

2 1 κ c(t) = (x(t), y(t)) ( ) det(c (t), c x (t)) = det (t) x (t) y (t) y = x (t)y (t) x (t)y (t), (t) c (t) = (x (t)) 2 + (y (t)) 2. c (t) = 1 1 1.1 I R 1.1.1 c : I R 2 (i) c C (ii) t I c (t) (0, 0) c (t) c(i) c c(t) 1.1.2 (1) (2) (3) (1) r > 0 c : R R 2 : t (r cos t, r sin t) (2) C f : I R c : I R 2 : t (t, f(t)) (3) y = x c : R R 2 : t (t,

More information

/ 2 n n n n x 1,..., x n 1 n 2 n R n n ndimensional Euclidean space R n vector point R n set space R n R n x = x 1 x n y = y 1 y n distance dx,

/ 2 n n n n x 1,..., x n 1 n 2 n R n n ndimensional Euclidean space R n vector point R n set space R n R n x = x 1 x n y = y 1 y n distance dx, 1 1.1 R n 1.1.1 3 xyz xyz 3 x, y, z R 3 := x y : x, y, z R z 1 3. n n x 1,..., x n x 1. x n x 1 x n 1 / 2 n n n n x 1,..., x n 1 n 2 n R n n ndimensional Euclidean space R n vector point 1.1.2 R n set

More information

1 Kinect for Windows M = [X Y Z] T M = [X Y Z ] T f (u,v) w 3.2 [11] [7] u = f X +u Z 0 δ u (X,Y,Z ) (5) v = f Y Z +v 0 δ v (X,Y,Z ) (6) w = Z +

1 Kinect for Windows M = [X Y Z] T M = [X Y Z ] T f (u,v) w 3.2 [11] [7] u = f X +u Z 0 δ u (X,Y,Z ) (5) v = f Y Z +v 0 δ v (X,Y,Z ) (6) w = Z + 3 3D 1,a) 1 1 Kinect (X, Y) 3D 3D 1. 2010 Microsoft Kinect for Windows SDK( (Kinect) SDK ) 3D [1], [2] [3] [4] [5] [10] 30fps [10] 3 Kinect 3 Kinect Kinect for Windows SDK 3 Microsoft 3 Kinect for Windows

More information

II 1 3 2 5 3 7 4 8 5 11 6 13 7 16 8 18 2 1 1. x 2 + xy x y (1 lim (x,y (1,1 x 1 x 3 + y 3 (2 lim (x,y (, x 2 + y 2 x 2 (3 lim (x,y (, x 2 + y 2 xy (4 lim (x,y (, x 2 + y 2 x y (5 lim (x,y (, x + y x 3y

More information

I, II 1, A = A 4 : 6 = max{ A, } A A 10 10%

I, II 1, A = A 4 : 6 = max{ A, } A A 10 10% 1 2006.4.17. A 3-312 tel: 092-726-4774, e-mail: [email protected], http://www.math.kyushu-u.ac.jp/ hara/lectures/lectures-j.html Office hours: B A I ɛ-δ ɛ-δ 1. 2. A 1. 1. 2. 3. 4. 5. 2. ɛ-δ 1. ɛ-n

More information

B 38 1 (x, y), (x, y, z) (x 1, x 2 ) (x 1, x 2, x 3 ) 2 : x 2 + y 2 = 1. (parameter) x = cos t, y = sin t. y = f(x) r(t) = (x(t), y(t), z(t)), a t b.

B 38 1 (x, y), (x, y, z) (x 1, x 2 ) (x 1, x 2, x 3 ) 2 : x 2 + y 2 = 1. (parameter) x = cos t, y = sin t. y = f(x) r(t) = (x(t), y(t), z(t)), a t b. 2009 7 9 1 2 2 2 3 6 4 9 5 14 6 18 7 23 8 25 9 26 10 29 11 32 12 35 A 37 1 B 38 1 (x, y), (x, y, z) (x 1, x 2 ) (x 1, x 2, x 3 ) 2 : x 2 + y 2 = 1. (parameter) x = cos t, y = sin t. y = f(x) r(t) = (x(t),

More information

21 2 26 i 1 1 1.1............................ 1 1.2............................ 3 2 9 2.1................... 9 2.2.......... 9 2.3................... 11 2.4....................... 12 3 15 3.1..........

More information

No δs δs = r + δr r = δr (3) δs δs = r r = δr + u(r + δr, t) u(r, t) (4) δr = (δx, δy, δz) u i (r + δr, t) u i (r, t) = u i x j δx j (5) δs 2

No δs δs = r + δr r = δr (3) δs δs = r r = δr + u(r + δr, t) u(r, t) (4) δr = (δx, δy, δz) u i (r + δr, t) u i (r, t) = u i x j δx j (5) δs 2 No.2 1 2 2 δs δs = r + δr r = δr (3) δs δs = r r = δr + u(r + δr, t) u(r, t) (4) δr = (δx, δy, δz) u i (r + δr, t) u i (r, t) = u i δx j (5) δs 2 = δx i δx i + 2 u i δx i δx j = δs 2 + 2s ij δx i δx j

More information

.3. (x, x = (, u = = 4 (, x x = 4 x, x 0 x = 0 x = 4 x.4. ( z + z = 8 z, z 0 (z, z = (0, 8, (,, (8, 0 3 (0, 8, (,, (8, 0 z = z 4 z (g f(x = g(

.3. (x, x = (, u = = 4 (, x x = 4 x, x 0 x = 0 x = 4 x.4. ( z + z = 8 z, z 0 (z, z = (0, 8, (,, (8, 0 3 (0, 8, (,, (8, 0 z = z 4 z (g f(x = g( 06 5.. ( y = x x y 5 y 5 = (x y = x + ( y = x + y = x y.. ( Y = C + I = 50 + 0.5Y + 50 r r = 00 0.5Y ( L = M Y r = 00 r = 0.5Y 50 (3 00 0.5Y = 0.5Y 50 Y = 50, r = 5 .3. (x, x = (, u = = 4 (, x x = 4 x,

More information

x () g(x) = f(t) dt f(x), F (x) 3x () g(x) g (x) f(x), F (x) (3) h(x) = x 3x tf(t) dt.9 = {(x, y) ; x, y, x + y } f(x, y) = xy( x y). h (x) f(x), F (x

x () g(x) = f(t) dt f(x), F (x) 3x () g(x) g (x) f(x), F (x) (3) h(x) = x 3x tf(t) dt.9 = {(x, y) ; x, y, x + y } f(x, y) = xy( x y). h (x) f(x), F (x [ ] IC. f(x) = e x () f(x) f (x) () lim f(x) lim f(x) x + x (3) lim f(x) lim f(x) x + x (4) y = f(x) ( ) ( s46). < a < () a () lim a log xdx a log xdx ( ) n (3) lim log k log n n n k=.3 z = log(x + y ),

More information

IA 2013 : :10722 : 2 : :2 :761 :1 (23-27) : : ( / ) (1 /, ) / e.g. (Taylar ) e x = 1 + x + x xn n! +... sin x = x x3 6 + x5 x2n+1 + (

IA 2013 : :10722 : 2 : :2 :761 :1 (23-27) : : ( / ) (1 /, ) / e.g. (Taylar ) e x = 1 + x + x xn n! +... sin x = x x3 6 + x5 x2n+1 + ( IA 2013 : :10722 : 2 : :2 :761 :1 23-27) : : 1 1.1 / ) 1 /, ) / e.g. Taylar ) e x = 1 + x + x2 2 +... + xn n! +... sin x = x x3 6 + x5 x2n+1 + 1)n 5! 2n + 1)! 2 2.1 = 1 e.g. 0 = 0.00..., π = 3.14..., 1

More information

28 Horizontal angle correction using straight line detection in an equirectangular image

28 Horizontal angle correction using straight line detection in an equirectangular image 28 Horizontal angle correction using straight line detection in an equirectangular image 1170283 2017 3 1 2 i Abstract Horizontal angle correction using straight line detection in an equirectangular image

More information

(, Goo Ishikawa, Go-o Ishikawa) ( ) 1

(, Goo Ishikawa, Go-o Ishikawa) ( ) 1 (, Goo Ishikawa, Go-o Ishikawa) ( ) 1 ( ) ( ) ( ) G7( ) ( ) ( ) () ( ) BD = 1 DC CE EA AF FB 0 0 BD DC CE EA AF FB =1 ( ) 2 (geometry) ( ) ( ) 3 (?) (Topology) ( ) DNA ( ) 4 ( ) ( ) 5 ( ) H. 1 : 1+ 5 2

More information

数学の基礎訓練I

数学の基礎訓練I I 9 6 13 1 1 1.1............... 1 1................ 1 1.3.................... 1.4............... 1.4.1.............. 1.4................. 3 1.4.3........... 3 1.4.4.. 3 1.5.......... 3 1.5.1..............

More information

1 Abstract 2 3 n a ax 2 + bx + c = 0 (a 0) (1) ( x + b ) 2 = b2 4ac 2a 4a 2 D = b 2 4ac > 0 (1) 2 D = 0 D < 0 x + b 2a = ± b2 4ac 2a b ± b 2

1 Abstract 2 3 n a ax 2 + bx + c = 0 (a 0) (1) ( x + b ) 2 = b2 4ac 2a 4a 2 D = b 2 4ac > 0 (1) 2 D = 0 D < 0 x + b 2a = ± b2 4ac 2a b ± b 2 1 Abstract n 1 1.1 a ax + bx + c = 0 (a 0) (1) ( x + b ) = b 4ac a 4a D = b 4ac > 0 (1) D = 0 D < 0 x + b a = ± b 4ac a b ± b 4ac a b a b ± 4ac b i a D (1) ax + bx + c D 0 () () (015 8 1 ) 1. D = b 4ac

More information

I A A441 : April 15, 2013 Version : 1.1 I Kawahira, Tomoki TA (Shigehiro, Yoshida )

I A A441 : April 15, 2013 Version : 1.1 I   Kawahira, Tomoki TA (Shigehiro, Yoshida ) I013 00-1 : April 15, 013 Version : 1.1 I Kawahira, Tomoki TA (Shigehiro, Yoshida) http://www.math.nagoya-u.ac.jp/~kawahira/courses/13s-tenbou.html pdf * 4 15 4 5 13 e πi = 1 5 0 5 7 3 4 6 3 6 10 6 17

More information

215 11 13 1 2 1.1....................... 2 1.2.................... 2 1.3..................... 2 1.4...................... 3 1.5............... 3 1.6........................... 4 1.7.................. 4

More information

x, y x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = 15 xy (x y) (x + y) xy (x y) (x y) ( x 2 + xy + y 2) = 15 (x y)

x, y x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = 15 xy (x y) (x + y) xy (x y) (x y) ( x 2 + xy + y 2) = 15 (x y) x, y x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = 15 1 1977 x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = 15 xy (x y) (x + y) xy (x y) (x y) ( x 2 + xy + y 2) = 15 (x y) ( x 2 y + xy 2 x 2 2xy y 2) = 15 (x y) (x + y) (xy

More information

24 I ( ) 1. R 3 (i) C : x 2 + y 2 1 = 0 (ii) C : y = ± 1 x 2 ( 1 x 1) (iii) C : x = cos t, y = sin t (0 t 2π) 1.1. γ : [a, b] R n ; t γ(t) = (x

24 I ( ) 1. R 3 (i) C : x 2 + y 2 1 = 0 (ii) C : y = ± 1 x 2 ( 1 x 1) (iii) C : x = cos t, y = sin t (0 t 2π) 1.1. γ : [a, b] R n ; t γ(t) = (x 24 I 1.1.. ( ) 1. R 3 (i) C : x 2 + y 2 1 = 0 (ii) C : y = ± 1 x 2 ( 1 x 1) (iii) C : x = cos t, y = sin t (0 t 2π) 1.1. γ : [a, b] R n ; t γ(t) = (x 1 (t), x 2 (t),, x n (t)) ( ) ( ), γ : (i) x 1 (t),

More information

(MIRU2008) HOG Histograms of Oriented Gradients (HOG)

(MIRU2008) HOG Histograms of Oriented Gradients (HOG) (MIRU2008) 2008 7 HOG - - E-mail: [email protected], {takigu,ariki}@kobe-u.ac.jp Histograms of Oriented Gradients (HOG) HOG Shape Contexts HOG 5.5 Histograms of Oriented Gradients D Human

More information

all.dvi

all.dvi 38 5 Cauchy.,,,,., σ.,, 3,,. 5.1 Cauchy (a) (b) (a) (b) 5.1: 5.1. Cauchy 39 F Q Newton F F F Q F Q 5.2: n n ds df n ( 5.1). df n n df(n) df n, t n. t n = df n (5.1) ds 40 5 Cauchy t l n mds df n 5.3: t

More information

微分積分 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. このサンプルページの内容は, 初版 1 刷発行時のものです.

微分積分 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます.   このサンプルページの内容は, 初版 1 刷発行時のものです. 微分積分 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. ttp://www.morikita.co.jp/books/mid/00571 このサンプルページの内容は, 初版 1 刷発行時のものです. i ii 014 10 iii [note] 1 3 iv 4 5 3 6 4 x 0 sin x x 1 5 6 z = f(x, y) 1 y = f(x)

More information

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

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 Vol. 52 No. 1 257 268 (Jan. 2011) 1 2, 1 1 measurement. In this paper, a dynamic road map making system is proposed. The proposition system uses probe-cars which has an in-vehicle camera and a GPS receiver.

More information

Armstrong culture Web

Armstrong culture Web 2004 5 10 M.A. Armstrong, Groups and Symmetry, Springer-Verlag, NewYork, 1988 (2000) (1989) (2001) (2002) 1 Armstrong culture Web 1 3 1.1................................. 3 1.2.................................

More information

Dynkin Serre Weyl

Dynkin Serre Weyl Dynkin Naoya Enomoto 2003.3. paper Dynkin Introduction Dynkin Lie Lie paper 1 0 Introduction 3 I ( ) Lie Dynkin 4 1 ( ) Lie 4 1.1 Lie ( )................................ 4 1.2 Killing form...........................................

More information

曲面のパラメタ表示と接線ベクトル

曲面のパラメタ表示と接線ベクトル L11(2011-07-06 Wed) :Time-stamp: 2011-07-06 Wed 13:08 JST hig 1,,. 2. http://hig3.net () (L11) 2011-07-06 Wed 1 / 18 ( ) 1 V = (xy2 ) x + (2y) y = y 2 + 2. 2 V = 4y., D V ds = 2 2 ( ) 4 x 2 4y dy dx =

More information

II 1 II 2012 II Gauss-Bonnet II

II 1 II 2012 II Gauss-Bonnet II II 1 II 212 II Gauss-Bonnet II 1 1 1.1......................................... 1 1.2............................................ 2 1.3.................................. 3 1.4.............................................

More information

x = a 1 f (a r, a + r) f(a) r a f f(a) 2 2. (a, b) 2 f (a, b) r f(a, b) r (a, b) f f(a, b)

x = a 1 f (a r, a + r) f(a) r a f f(a) 2 2. (a, b) 2 f (a, b) r f(a, b) r (a, b) f f(a, b) 2011 I 2 II III 17, 18, 19 7 7 1 2 2 2 1 2 1 1 1.1.............................. 2 1.2 : 1.................... 4 1.2.1 2............................... 5 1.3 : 2.................... 5 1.3.1 2.....................................

More information

,. Black-Scholes u t t, x c u 0 t, x x u t t, x c u t, x x u t t, x + σ x u t, x + rx ut, x rux, t 0 x x,,.,. Step 3, 7,,, Step 6., Step 4,. Step 5,,.

,. Black-Scholes u t t, x c u 0 t, x x u t t, x c u t, x x u t t, x + σ x u t, x + rx ut, x rux, t 0 x x,,.,. Step 3, 7,,, Step 6., Step 4,. Step 5,,. 9 α ν β Ξ ξ Γ γ o δ Π π ε ρ ζ Σ σ η τ Θ θ Υ υ ι Φ φ κ χ Λ λ Ψ ψ µ Ω ω Def, Prop, Th, Lem, Note, Remark, Ex,, Proof, R, N, Q, C [a, b {x R : a x b} : a, b {x R : a < x < b} : [a, b {x R : a x < b} : a,

More information

A(6, 13) B(1, 1) 65 y C 2 A(2, 1) B( 3, 2) C 66 x + 2y 1 = 0 2 A(1, 1) B(3, 0) P 67 3 A(3, 3) B(1, 2) C(4, 0) (1) ABC G (2) 3 A B C P 6

A(6, 13) B(1, 1) 65 y C 2 A(2, 1) B( 3, 2) C 66 x + 2y 1 = 0 2 A(1, 1) B(3, 0) P 67 3 A(3, 3) B(1, 2) C(4, 0) (1) ABC G (2) 3 A B C P 6 1 1 1.1 64 A6, 1) B1, 1) 65 C A, 1) B, ) C 66 + 1 = 0 A1, 1) B, 0) P 67 A, ) B1, ) C4, 0) 1) ABC G ) A B C P 64 A 1, 1) B, ) AB AB = 1) + 1) A 1, 1) 1 B, ) 1 65 66 65 C0, k) 66 1 p, p) 1 1 A B AB A 67

More information

1 No.1 5 C 1 I III F 1 F 2 F 1 F 2 2 Φ 2 (t) = Φ 1 (t) Φ 1 (t t). = Φ 1(t) t = ( 1.5e 0.5t 2.4e 4t 2e 10t ) τ < 0 t > τ Φ 2 (t) < 0 lim t Φ 2 (t) = 0

1 No.1 5 C 1 I III F 1 F 2 F 1 F 2 2 Φ 2 (t) = Φ 1 (t) Φ 1 (t t). = Φ 1(t) t = ( 1.5e 0.5t 2.4e 4t 2e 10t ) τ < 0 t > τ Φ 2 (t) < 0 lim t Φ 2 (t) = 0 1 No.1 5 C 1 I III F 1 F 2 F 1 F 2 2 Φ 2 (t) = Φ 1 (t) Φ 1 (t t). = Φ 1(t) t = ( 1.5e 0.5t 2.4e 4t 2e 10t ) τ < 0 t > τ Φ 2 (t) < 0 lim t Φ 2 (t) = 0 0 < t < τ I II 0 No.2 2 C x y x y > 0 x 0 x > b a dx

More information

重力方向に基づくコントローラの向き決定方法

重力方向に基づくコントローラの向き決定方法 ( ) 2/Sep 09 1 ( ) ( ) 3 2 X w, Y w, Z w +X w = +Y w = +Z w = 1 X c, Y c, Z c X c, Y c, Z c X w, Y w, Z w Y c Z c X c 1: X c, Y c, Z c Kentaro [email protected] 1 M M v 0, v 1, v 2 v 0 v

More information

1990 IMO 1990/1/15 1:00-4:00 1 N N N 1, N 1 N 2, N 2 N 3 N 3 2 x x + 52 = 3 x x , A, B, C 3,, A B, C 2,,,, 7, A, B, C

1990 IMO 1990/1/15 1:00-4:00 1 N N N 1, N 1 N 2, N 2 N 3 N 3 2 x x + 52 = 3 x x , A, B, C 3,, A B, C 2,,,, 7, A, B, C 0 9 (1990 1999 ) 10 (2000 ) 1900 1994 1995 1999 2 SAT ACT 1 1990 IMO 1990/1/15 1:00-4:00 1 N 1990 9 N N 1, N 1 N 2, N 2 N 3 N 3 2 x 2 + 25x + 52 = 3 x 2 + 25x + 80 3 2, 3 0 4 A, B, C 3,, A B, C 2,,,, 7,

More information

Convolutional Neural Network A Graduation Thesis of College of Engineering, Chubu University Investigation of feature extraction by Convolution

Convolutional Neural Network A Graduation Thesis of College of Engineering, Chubu University Investigation of feature extraction by Convolution Convolutional Neural Network 2014 3 A Graduation Thesis of College of Engineering, Chubu University Investigation of feature extraction by Convolutional Neural Network Fukui Hiroshi 1940 1980 [1] 90 3

More information

δ ij δ ij ˆx ˆx ŷ ŷ ẑ ẑ 0, ˆx ŷ ŷ ˆx ẑ, ŷ ẑ ẑ ŷ ẑ, ẑ ˆx ˆx ẑ ŷ, a b a x ˆx + a y ŷ + a z ẑ b x ˆx + b

δ ij δ ij ˆx ˆx ŷ ŷ ẑ ẑ 0, ˆx ŷ ŷ ˆx ẑ, ŷ ẑ ẑ ŷ ẑ, ẑ ˆx ˆx ẑ ŷ, a b a x ˆx + a y ŷ + a z ẑ b x ˆx + b 23 2 2.1 n n r x, y, z ˆx ŷ ẑ 1 a a x ˆx + a y ŷ + a z ẑ 2.1.1 3 a iˆx i. 2.1.2 i1 i j k e x e y e z 3 a b a i b i i 1, 2, 3 x y z ˆx i ˆx j δ ij, 2.1.3 n a b a i b i a i b i a x b x + a y b y + a z b

More information

JKR Point loading of an elastic half-space 2 3 Pressure applied to a circular region Boussinesq, n =

JKR Point loading of an elastic half-space 2 3 Pressure applied to a circular region Boussinesq, n = JKR 17 9 15 1 Point loading of an elastic half-space Pressure applied to a circular region 4.1 Boussinesq, n = 1.............................. 4. Hertz, n = 1.................................. 6 4 Hertz

More information

,.,. 2, R 2, ( )., I R. c : I R 2, : (1) c C -, (2) t I, c (t) (0, 0). c(i). c (t)., c(t) = (x(t), y(t)) c (t) = (x (t), y (t)) : (1)

,.,. 2, R 2, ( )., I R. c : I R 2, : (1) c C -, (2) t I, c (t) (0, 0). c(i). c (t)., c(t) = (x(t), y(t)) c (t) = (x (t), y (t)) : (1) ( ) 1., : ;, ;, ; =. ( ).,.,,,., 2.,.,,.,.,,., y = f(x), f ( ).,,.,.,., U R m, F : U R n, M, f : M R p M, p,, R m,,, R m. 2009 A tamaru math.sci.hiroshima-u.ac.jp 1 ,.,. 2, R 2, ( ).,. 2.1 2.1. I R. c

More information

I A A441 : April 21, 2014 Version : Kawahira, Tomoki TA (Kondo, Hirotaka ) Google

I A A441 : April 21, 2014 Version : Kawahira, Tomoki TA (Kondo, Hirotaka ) Google I4 - : April, 4 Version :. Kwhir, Tomoki TA (Kondo, Hirotk) Google http://www.mth.ngoy-u.c.jp/~kwhir/courses/4s-biseki.html pdf 4 4 4 4 8 e 5 5 9 etc. 5 6 6 6 9 n etc. 6 6 6 3 6 3 7 7 etc 7 4 7 7 8 5 59

More information

zz + 3i(z z) + 5 = 0 + i z + i = z 2i z z z y zz + 3i (z z) + 5 = 0 (z 3i) (z + 3i) = 9 5 = 4 z 3i = 2 (3i) zz i (z z) + 1 = a 2 {

zz + 3i(z z) + 5 = 0 + i z + i = z 2i z z z y zz + 3i (z z) + 5 = 0 (z 3i) (z + 3i) = 9 5 = 4 z 3i = 2 (3i) zz i (z z) + 1 = a 2 { 04 zz + iz z) + 5 = 0 + i z + i = z i z z z 970 0 y zz + i z z) + 5 = 0 z i) z + i) = 9 5 = 4 z i = i) zz i z z) + = a {zz + i z z) + 4} a ) zz + a + ) z z) + 4a = 0 4a a = 5 a = x i) i) : c Darumafactory

More information

最新耐震構造解析 ( 第 3 版 ) サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. このサンプルページの内容は, 第 3 版 1 刷発行時のものです.

最新耐震構造解析 ( 第 3 版 ) サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます.   このサンプルページの内容は, 第 3 版 1 刷発行時のものです. 最新耐震構造解析 ( 第 3 版 ) サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. http://www.morikita.co.jp/books/mid/052093 このサンプルページの内容は, 第 3 版 1 刷発行時のものです. i 3 10 3 2000 2007 26 8 2 SI SI 20 1996 2000 SI 15 3 ii 1 56 6

More information

II No.01 [n/2] [1]H n (x) H n (x) = ( 1) r n! r!(n 2r)! (2x)n 2r. r=0 [2]H n (x) n,, H n ( x) = ( 1) n H n (x). [3] H n (x) = ( 1) n dn x2 e dx n e x2

II No.01 [n/2] [1]H n (x) H n (x) = ( 1) r n! r!(n 2r)! (2x)n 2r. r=0 [2]H n (x) n,, H n ( x) = ( 1) n H n (x). [3] H n (x) = ( 1) n dn x2 e dx n e x2 II No.1 [n/] [1]H n x) H n x) = 1) r n! r!n r)! x)n r r= []H n x) n,, H n x) = 1) n H n x) [3] H n x) = 1) n dn x e dx n e x [4] H n+1 x) = xh n x) nh n 1 x) ) d dx x H n x) = H n+1 x) d dx H nx) = nh

More information

2000年度『数学展望 I』講義録

2000年度『数学展望 I』講義録 2000 I I IV I II 2000 I I IV I-IV. i ii 3.10 (http://www.math.nagoya-u.ac.jp/ kanai/) 2000 A....1 B....4 C....10 D....13 E....17 Brouwer A....21 B....26 C....33 D....39 E. Sperner...45 F....48 A....53

More information

QCD 1 QCD GeV 2014 QCD 2015 QCD SU(3) QCD A µ g µν QCD 1

QCD 1 QCD GeV 2014 QCD 2015 QCD SU(3) QCD A µ g µν QCD 1 QCD 1 QCD GeV 2014 QCD 2015 QCD SU(3) QCD A µ g µν QCD 1 (vierbein) QCD QCD 1 1: QCD QCD Γ ρ µν A µ R σ µνρ F µν g µν A µ Lagrangian gr TrFµν F µν No. Yes. Yes. No. No! Yes! [1] Nash & Sen [2] Riemann

More information

II (10 4 ) 1. p (x, y) (a, b) ε(x, y; a, b) 0 f (x, y) f (a, b) A, B (6.5) y = b f (x, b) f (a, b) x a = A + ε(x, b; a, b) x a 2 x a 0 A = f x (

II (10 4 ) 1. p (x, y) (a, b) ε(x, y; a, b) 0 f (x, y) f (a, b) A, B (6.5) y = b f (x, b) f (a, b) x a = A + ε(x, b; a, b) x a 2 x a 0 A = f x ( II (1 4 ) 1. p.13 1 (x, y) (a, b) ε(x, y; a, b) f (x, y) f (a, b) A, B (6.5) y = b f (x, b) f (a, b) x a = A + ε(x, b; a, b) x a x a A = f x (a, b) y x 3 3y 3 (x, y) (, ) f (x, y) = x + y (x, y) = (, )

More information

7. y fx, z gy z gfx dz dx dz dy dy dx. g f a g bf a b fa 7., chain ule Ω, D R n, R m a Ω, f : Ω R m, g : D R l, fω D, b fa, f a g b g f a g f a g bf a

7. y fx, z gy z gfx dz dx dz dy dy dx. g f a g bf a b fa 7., chain ule Ω, D R n, R m a Ω, f : Ω R m, g : D R l, fω D, b fa, f a g b g f a g f a g bf a 9 203 6 7 WWW http://www.math.meiji.ac.jp/~mk/lectue/tahensuu-203/ 2 8 8 7. 7 7. y fx, z gy z gfx dz dx dz dy dy dx. g f a g bf a b fa 7., chain ule Ω, D R n, R m a Ω, f : Ω R m, g : D R l, fω D, b fa,

More information

y π π O π x 9 s94.5 y dy dx. y = x + 3 y = x logx + 9 s9.6 z z x, z y. z = xy + y 3 z = sinx y 9 s x dx π x cos xdx 9 s93.8 a, fx = e x ax,. a =

y π π O π x 9 s94.5 y dy dx. y = x + 3 y = x logx + 9 s9.6 z z x, z y. z = xy + y 3 z = sinx y 9 s x dx π x cos xdx 9 s93.8 a, fx = e x ax,. a = [ ] 9 IC. dx = 3x 4y dt dy dt = x y u xt = expλt u yt λ u u t = u u u + u = xt yt 6 3. u = x, y, z = x + y + z u u 9 s9 grad u ux, y, z = c c : grad u = u x i + u y j + u k i, j, k z x, y, z grad u v =

More information

(3) (2),,. ( 20) ( s200103) 0.7 x C,, x 2 + y 2 + ax = 0 a.. D,. D, y C, C (x, y) (y 0) C m. (2) D y = y(x) (x ± y 0), (x, y) D, m, m = 1., D. (x 2 y

(3) (2),,. ( 20) ( s200103) 0.7 x C,, x 2 + y 2 + ax = 0 a.. D,. D, y C, C (x, y) (y 0) C m. (2) D y = y(x) (x ± y 0), (x, y) D, m, m = 1., D. (x 2 y [ ] 7 0.1 2 2 + y = t sin t IC ( 9) ( s090101) 0.2 y = d2 y 2, y = x 3 y + y 2 = 0 (2) y + 2y 3y = e 2x 0.3 1 ( y ) = f x C u = y x ( 15) ( s150102) [ ] y/x du x = Cexp f(u) u (2) x y = xey/x ( 16) ( s160101)

More information

( 12 ( ( ( ( Levi-Civita grad div rot ( ( = 4 : 6 3 1 1.1 f(x n f (n (x, d n f(x (1.1 dxn f (2 (x f (x 1.1 f(x = e x f (n (x = e x d dx (fg = f g + fg (1.2 d dx d 2 dx (fg = f g + 2f g + fg 2... d n n

More information

(2) Fisher α (α) α Fisher α ( α) 0 Levi Civita (1) ( 1) e m (e) (m) ([1], [2], [13]) Poincaré e m Poincaré e m Kähler-like 2 Kähler-like

(2) Fisher α (α) α Fisher α ( α) 0 Levi Civita (1) ( 1) e m (e) (m) ([1], [2], [13]) Poincaré e m Poincaré e m Kähler-like 2 Kähler-like () 10 9 30 1 Fisher α (α) α Fisher α ( α) 0 Levi Civita (1) ( 1) e m (e) (m) ([1], [], [13]) Poincaré e m Poincaré e m Kähler-like Kähler-like Kähler M g M X, Y, Z (.1) Xg(Y, Z) = g( X Y, Z) + g(y, XZ)

More information

b3e2003.dvi

b3e2003.dvi 15 II 5 5.1 (1) p, q p = (x + 2y, xy, 1), q = (x 2 + 3y 2, xyz, ) (i) p rotq (ii) p gradq D (2) a, b rot(a b) div [11, p.75] (3) (i) f f grad f = 1 2 grad( f 2) (ii) f f gradf 1 2 grad ( f 2) rotf 5.2

More information

xx/xx Vol. Jxx A No. xx 1 Fig. 1 PAL(Panoramic Annular Lens) PAL(Panoramic Annular Lens) PAL (2) PAL PAL 2 PAL 3 2 PAL 1 PAL 3 PAL PAL 2. 1 PAL

xx/xx Vol. Jxx A No. xx 1 Fig. 1 PAL(Panoramic Annular Lens) PAL(Panoramic Annular Lens) PAL (2) PAL PAL 2 PAL 3 2 PAL 1 PAL 3 PAL PAL 2. 1 PAL PAL On the Precision of 3D Measurement by Stereo PAL Images Hiroyuki HASE,HirofumiKAWAI,FrankEKPAR, Masaaki YONEDA,andJien KATO PAL 3 PAL Panoramic Annular Lens 1985 Greguss PAL 1 PAL PAL 2 3 2 PAL DP

More information