1 1 Pixel 0 n 1 n=8 56 R G B RGB M RGB (1) M = 0.99R G B (1) () 4 π d 4 B = L cos φ () 4 ID B L d ID φ d / ID F R φ (3) R
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- まいか かやぬま
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1 I , When preparing the manuscript, read and observe carefully this sample as well as the instruction manual for the manuscript (1) OS of the Transaction of Japan Society of Mechanical Windows Engineers. 000/XP/Vista/7 This sample was prepared using MS-word () (3) (4)
2 1 1 Pixel 0 n 1 n=8 56 R G B RGB M RGB (1) M = 0.99R G B (1) () 4 π d 4 B = L cos φ () 4 ID B L d ID φ d / ID F R φ (3) R = tan 1 ID φ (3) () 10 3 f ( i, t ( i, (4)ij g t g 1, f ( i, > t i, = t 0, f ( i, j ) t ( (4) 1 4
3 f ( i, g( i, (5) 0, f ( i, g ( i, = (5) 1,
4 grayscale.exe 3 bmp txt gazo.bmp bunsho.txt Windows 8 Windows - f focus.exe f x y 113, Enter
5 -3 f shading.exe 8 Windows 3-1 histogram.exe () 3- Windows binarization.exe 4-1 isolation.exe
6 4- labeling.exe Windows segmentation.exe 5-1 erosion.exe 5- labeling.exe 0 4. (1) () D 1 I 1 D I ()(3) ID D 1 I 1 D I (3) (4) (5)
7
8 f ( i, g( i, (6) 1, f ( i, g ( i, = (6) 0, e (7) 4πS e = (7) L S L e (8) ax + bxy + cy + dx + ey + f = 0 (8) (8) xy(8)
9 n 1 5- n dilation.exe 5-1 n n 1 n 1 n () 6-1 edge.exe Windows 6-3 fitting.exe equation.exe
10 7- ellipse.exe a=1.9b=.0c=3.4d=4.e=5.8f= Enter 7-3 size.exe (1) () (3) (4) ax + bxy + cy + dx + ey + f = 0 θ x x ( ) ( ) = 1 A θ = (5) πab y y B 1 1 tan b c a af π A + B (3) size.exe (1) CGCG-ARTS () CG-ARTS (3) 00 (4)
11 (1) (1-1) C ImageAnalysis (1-) A 1B A B 1 1 A01.bmp B01.bmp 1 ZIP 1 ZIP (1-3) (1-1) A01.bmp B01.bmp C: ImageAnalysis (1-4) 15 1 (1-5) (1-1) 15 C: ImageAnalysis () cd C: ImageAnalysis (Enter) (1) () (3) (4)
12 1 grayscale a b focus c b d e f shading g b h f i histogram j i k l binarization m i n o isolation p o q labeling r q s t segmentation u t v w erosion x w y z labeling A z B C D grayscale a b focus c b d e f
13 shading g b h f i histogram j i k l binarization m i n o isolation p o q labeling r q s t segmentation u t v w erosion x w y z dilation A z B C edge D C E fitting F E G H I equation J I K a = b = c = d = e = f = ellipse L b M K N size O E P I1 Q R S T
Ⅰ-0X XX解析(タイトルを記載してください14ポイント)
I-07 画像解析 担当 臼杵 深 総 814 内線 1372, Email:dsusuki@ipc.shizuoka.ac.jp When preparing the manuscript, read and observe carefully this sample as well as the instruction manual for the 本実験の準備 manuscript of the
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yoshi@image.med.osaka-u.ac.jp http://www.image.med.osaka-u.ac.jp/member/yoshi/ II Excel, Mathematica Mathematica Osaka Electro-Communication University (2007 Apr) 09849-31503-64015-30704-18799-390 http://www.image.med.osaka-u.ac.jp/member/yoshi/
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145 13 13.1 13.1.1 0 m mg S 13.1 F 13.1 F /m S F F 13.1 F mg S F F mg 13.1: m d2 r 2 = F + F = 0 (13.1) 146 13 F = F (13.2) S S S S S P r S P r r = r 0 + r (13.3) r 0 S S m d2 r 2 = F (13.4) (13.3) d 2
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2 T140073 1 2 ax 2 + 2bxy + cy 2 + dx + ey + f = 0 a + b + c > 0 a, b, c A xy u = u 0 + a cos θ, v = v 0 + b sin θ 0 θ 2π u = u 0 ± a cos θ, v = v 0 + b tan θ π 2 < θ < π 2 u = u 0 + 2pt, v = v 0 + pt
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0. A A = 4 IC () det A () A () x + y + z = x y z X Y Z = A x y z ( 5) ( s5590) 0. a + b + c b c () a a + b + c c a b a + b + c 0 a b c () a 0 c b b c 0 a c b a 0 0. A A = 7 5 4 5 0 ( 5) ( s5590) () A ()
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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
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II 16 16.0 2 1 15 x α 16 x n 1 17 (x α) 2 16.1 16.1.1 2 x P (x) P (x) = 3x 3 4x + 4 369 Q(x) = x 4 ax + b ( ) 1 P (x) x Q(x) x P (x) x P (x) x = a P (a) P (x) = x 3 7x + 4 P (2) = 2 3 7 2 + 4 = 8 14 +
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情報工学総合演習 画像解析 久野義徳 小林貴訓 福田悠人 I. 概要画像解析は, 見つける 数える 形を測る 識別する 記号 文字を読む など複雑かつ多様な 作業を画像処理により実現する技術であり, 自然観測 生産現場 医療をはじめ様々な分野で利用されて いる. 本テーマではまず, 演習として顕微鏡で観察した粒子画像を用いた粒子の計数, 及び形状特徴の計測を 行う. その後, 画像中から指定物体を検出
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