MATLAB车牌定位实现系统.zip

2401_86539719ZIPMATLAB车牌定位实现系统.zip  10.46KB

资源文件列表:

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

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)
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