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( E, j. c: G! B' r# S: X. Y2 Hmatlab中的conv2是用于对二维数据进行卷积运算，有三个参数可供选择，下面是help content of conv2. q9 ~8 g) s/ C0 r5 x7 D/ O
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conv2 Two dimensional convolution.* @7 T) V$ `5 S& t2 e
. J+ G& F. C+ ]$ d1 O0 C: D C = conv2(A, B) peRForms the 2-D convolution of matrices A and B.
" u) b1 v/ ]& M! n If [ma,na] = size(A), [mb,nb] = size(B), and [mc,nc] = size(C), then4 x; g$ @. K) D% a/ S
mc = max([ma+mb-1,ma,mb]) and nc = max([na+nb-1,na,nb]).
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C = conv2(H1, H2, A) first convolves each column of A with the vector
' _7 C7 ^& W0 B7 w! F9 V7 p A H1 and then convolves each row of the result with the vector H2. If- {* n+ T' R% Y! }' W9 V
n1 = length(H1), n2 = length(H2), and [mc,nc] = size(C) then
1 p( e% e% {0 K9 n" P) _ mc = max([ma+n1-1,ma,n1]) and nc = max([na+n2-1,na,n2]).
9 {7 @2 d/ t# V: ]$ a8 P conv2(H1, H2, A) is equivalent to conv2(H1(：)*H2(：).', A) up to4 }- D5 X; ?0 k+ m
round-off.
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6 X! N$ _) B9 f! w [$ k9 n C = conv2(..., SHAPE) returns a subsection of the 2-D
b5 U0 Q4 v$ e! ]/ z8 I6 W! f convolution with size specified by SHAPE:
8 l) f2 ~$ e8 ?5 V6 n1 R. N 'full' - (default) returns the full 2-D convolution,- Q6 L0 L8 p0 v/ b
'same' - returns the central part of the convolution
9 _/ `4 X _0 F% x" K* r# U% T! G that is the same size as A.
y7 V* d0 D2 O; B: w, P, r 'valid' - returns only those parts of the convolution; b1 W4 v) k: K- E/ F3 ]
that are computed without the zero-padded edges.
* T5 G- W9 H' Q% s size(C) = max([ma-max(0,mb-1),na-max(0,nb-1)],0).
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7 P/ S8 V) {9 h6 B- I1 c关于full, same以及valid三种参数的区别，如下面的实例所示：* d' S [1 c8 _4 e0 p2 O" I, o
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full
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5 ^2 {4 \1 M6 A: D. H7 U2 msame
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valid; _9 D7 n8 f) _( c F
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发表于 2020-1-14 10:27
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