MATLAB车牌定位实现系统.zip
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资源介绍:
Matlab车牌识别系统是一个使用Matlab编程语言开发的程序,用来识别汽车车牌上的字符和数字。该系统可以通过读取车牌图像,并使用图像处理、模式识别和机器学习算法来识别和解析车牌上的字符和数字。
以下是一个基本的车牌识别系统的工作流程:
1. 图像预处理:对车牌图像进行预处理,例如去噪、增强对比度、调整亮度等。
2. 车牌定位:使用图像处理算法定位车牌在图像中的位置。
3. 字符分割:将车牌图像分割成单个字符,以便对每个字符进行识别。
4. 字符识别:使用模式识别或机器学习算法对每个字符进行识别和分类。
5. 车牌解析:将识别出的字符组合成完整的车牌号码。
6. 输出结果:将识别结果输出到屏幕或保存到文件中。
在开发Matlab车牌识别系统时,可以使用Matlab的图像处理工具箱、模式识别工具箱和机器学习工具箱来实现各种功能。
此外,还可以使用深度学习模型如卷积神经网络(CNN)来提高字符识别的准确性。可以使用现有的开源深度学习框架(如TensorFlow或PyTorch)来训练和部署深度学习模型,并将其与Matlab集成。
总之,Matlab车牌识别系统是一个使用Matl
function bw2 = removeLargeArea(varargin)
%BWAREAOPEN Remove small objects from binary image.
% BW2 = BWAREAOPEN(BW,P) removes from a binary image all connected
% components (objects) that have fewer than P pixels, producing another
% binary image BW2. This operation is known as an area opening. The
% default connectivity is 8 for two dimensions, 26 for three dimensions,
% and CONNDEF(NDIMS(BW),'maximal') for higher dimensions.
%
% BW2 = BWAREAOPEN(BW,P,CONN) specifies the desired connectivity. CONN
% may have the following scalar values:
%
% 4 two-dimensional four-connected neighborhood
% 8 two-dimensional eight-connected neighborhood
% 6 three-dimensional six-connected neighborhood
% 18 three-dimensional 18-connected neighborhood
% 26 three-dimensional 26-connected neighborhood
%
% Connectivity may be defined in a more general way for any dimension by
% using for CONN a 3-by-3-by- ... -by-3 matrix of 0s and 1s. The
% 1-valued elements define neighborhood locations relative to the center
% element of CONN. CONN must be symmetric about its center element.
%
% Class Support
% -------------
% BW can be a logical or numeric array of any dimension, and it must be
% nonsparse.
%
% BW2 is logical.
%
% Example
% -------
% Remove all objects in the image text.png containing fewer than 50
% pixels.
%
% BW = imread('text.png');
% BW2 = bwareaopen(BW,50);
% imshow(BW);
% figure, imshow(BW2)
%
% See also BWCONNCOMP, CONNDEF, REGIONPROPS.
% Copyright 1993-2011 The MathWorks, Inc.
% $Revision: 1.10.4.9 $ $Date: 2011/11/09 16:48:52 $
% Input/output specs
% ------------------
% BW: N-D real full matrix
% any numeric class
% sparse not allowed
% anything that's not logical is converted first using
% bw = BW ~= 0
% Empty ok
% Inf's ok, treated as 1
% NaN's ok, treated as 1
%
% P: double scalar
% nonnegative integer
%
% CONN: connectivity
%
% BW2: logical, same size as BW
% contains only 0s and 1s.
[bw,p,conn] = parse_inputs(varargin{:});
CC = bwconncomp(bw,conn);
area = cellfun(@numel, CC.PixelIdxList);
idxToKeep = CC.PixelIdxList(area <= p);
idxToKeep = vertcat(idxToKeep{:});
bw2 = false(size(bw));
bw2(idxToKeep) = true;
%%%
%%% parse_inputs
%%%
function [bw,p,conn] = parse_inputs(varargin)
narginchk(2,3)
bw = varargin{1};
validateattributes(bw,{'numeric' 'logical'},{'nonsparse'},mfilename,'BW',1);
if ~islogical(bw)
bw = bw ~= 0;
end
p = varargin{2};
validateattributes(p,{'double'},{'scalar' 'integer' 'nonnegative'},...
mfilename,'P',2);
if (nargin >= 3)
conn = varargin{3};
else
conn = conndef(ndims(bw),'maximal');
end
iptcheckconn(conn,mfilename,'CONN',3)