卷积神经网络 - 如何获得特征映射？

我读了一些关于卷积神经网络的书籍和文章,似乎我理解了这个概念,但我不知道如何把它放在下面的图片中: alt text http://what-when-how.com/wp-内容/上传/ 2012/07/tmp725d63_thumb.png

从28x28标准化像素INPUT,我们得到4个大小为24x24的特征图.但如何得到它们？调整INPUT图像的大小？或执行图像转换？但是什么样的转变？或者将输入图像分成4个大小为24x24的4个角？我不明白这个过程,对我来说,似乎他们在每一步都将图像剪切或调整为较小的图像.请帮助谢谢.

这是用于CONV2函数的matlab帮助文件,它在CNN Matlab中使用(用于获取卷积层).仔细阅读,你会看到你的答案.

%CONV2 Two dimensional convolution.
%   C = CONV2(A, B) performs the 2-D convolution of matrices A and B.
%   If [ma,na] = size(A), [mb,nb] = size(B), and [mc,nc] = size(C), then
%   mc = max([ma+mb-1,ma,mb]) and nc = max([na+nb-1,na,nb]).
%
%   C = CONV2(H1, H2, A) convolves A first with the vector H1 along the
%   rows and then with the vector H2 along the columns. If n1 = length(H1)
%   and n2 = length(H2), then mc = max([ma+n1-1,ma,n1]) and 
%   nc = max([na+n2-1,na,n2]).
%
%   C = CONV2(..., SHAPE) returns a subsection of the 2-D
%   convolution with size specified by SHAPE:
%     'full'  - (default) returns the full 2-D convolution,
%     'same'  - returns the central part of the convolution
%               that is the same size as A.
%     'valid' - returns only those parts of the convolution
%               that are computed without the zero-padded edges.
%               **size(C) = max([ma-max(0,mb-1),na-max(0,nb-1)],0).**