matlab设计车牌定位.zip

m0_59833680ZIPmatlab设计车牌定位.zip  10.29KB

资源文件列表:

ZIP matlab设计车牌定位.zip 大约有7个文件
  1. matlab设计车牌定位/1.jpg 4.83KB
  2. matlab设计车牌定位/finddomain.m 1.21KB
  3. matlab设计车牌定位/main.m 2.67KB
  4. matlab设计车牌定位/mainfc.p 202B
  5. matlab设计车牌定位/removeLargeArea.m 2.75KB
  6. matlab设计车牌定位/二值图结果.bmp 1.92KB
  7. matlab设计车牌定位/

资源介绍:

MATLAB车牌识别系统可以使用计算机视觉和图像处理技术来自动识别车辆的车牌号码。 下面是一个简单的MATLAB车牌识别系统的步骤： 1. 车牌检测：首先，使用图像处理技术在图像中检测出车牌的位置。可以使用边缘检测、颜色过滤等技术来实现车牌的检测。 2. 车牌分割：一旦检测到车牌的位置，接下来需要将车牌分割成单个字符。可以使用图像处理技术，如二值化、形态学运算等方法来实现车牌字符的分割。 3. 字符识别：一旦车牌字符被成功分割出来，接下来需要对每个字符进行识别。可以使用机器学习算法，如支持向量机（SVM）或卷积神经网络（CNN）来训练一个字符识别模型。 4. 车牌号码识别：最后，将识别出的字符组合在一起，就可以得到完整的车牌号码。 需要注意的是，MATLAB提供了很多图像处理和机器学习的工具包，可以帮助开发者实现车牌识别系统。同时，还可以结合其他技术，如模板匹配、字符特征提取等方法来提高识别准确率。
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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