MATLAB车牌定位实现系统算法研究和实现
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在MATLAB中实现车牌定位系统,可以按照以下步骤进行算法研究和实现:
1. 读取图像:使用MATLAB的imread函数读取车辆图像。
2. 图像预处理:对读取的图像进行预处理,包括图像灰度化、降噪、图像增强等。可以使用MATLAB的rgb2gray函数将图像转换为灰度图像,使用imnoise函数添加高斯噪声或者使用imfilter函数进行均值滤波等。
3. 边缘检测:使用边缘检测算法,例如Sobel、Canny等,对预处理后的图像进行边缘检测。在MATLAB中可以使用edge函数实现边缘检测。
4. 车牌区域提取:根据边缘检测结果,采用形态学操作、连通域分析等方法,提取出车牌区域。在MATLAB中可以使用imopen、imclose等形态学操作函数,使用bwlabel等连通域分析函数。
5. 车牌倾斜校正:如果车牌有倾斜,可以使用旋转矫正算法进行倾斜校正。MATLAB提供了imrotate函数用于图像旋转。
6. 字符分割:对车牌区域进行字符分割,将每个字符分割为一个单独的图像。可以使用连通域分析、投影法等方法进行字符分割。
7. 字符识别:对字符图像进行识别,可以使
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)