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ZIPMATLAB车牌定位实现系统算法研究和实现

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MATLAB车牌定位实现系统算法研究和实现.zip 大约有7个文件
  1. MATLAB车牌定位实现系统算法研究和实现/
  2. MATLAB车牌定位实现系统算法研究和实现/1.jpg 4.83KB
  3. MATLAB车牌定位实现系统算法研究和实现/finddomain.m 1.21KB
  4. MATLAB车牌定位实现系统算法研究和实现/main.m 2.67KB
  5. MATLAB车牌定位实现系统算法研究和实现/mainfc.p 202B
  6. MATLAB车牌定位实现系统算法研究和实现/removeLargeArea.m 2.75KB
  7. MATLAB车牌定位实现系统算法研究和实现/二值图结果.bmp 1.92KB

资源介绍:

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)
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