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1、程序结果
2、Foerstner算子原理
算子是通过计算各像素的Robert's梯度值和以像素为中心的一个窗口的灰度协方差矩阵 , 在图像中寻找具有尽可能小而接近圆的误差椭圆的点作为特征点 。 它能给 出特征点的类型且精度较高,所 实际中应用比较广泛.
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3、程序
disp('Calling ip_fop ...');[win, corner, circ, noclass]=ip_fop( ...g, ... intensity image (one channel, grey-level image)'DETECTION_METHOD', 'foerstner', ... method for optimal search window: 'foerstner' (default) or 'koethe' 'SIGMA_N' ,1.0, ... standard deviation of (constant) image noise (default: 2.0)'DERIVATIVE_FILTER' ,'gaussian2d',... filter for gradient: 'gaussian2d'(default) oder 'gaussian1d''INTEGRATION_FILTER' ,'gaussian', ... integration kernel: 'box_filt' (default) oder 'gaussian' 'SIGMA_DERIVATIVE_FILTER' ,0.7, ... size of derivative filter (sigma) (default: 1.0)'SIGMA_INTEGRATION_FILTER' ,2, ... size of integration filter (default: 1.41 * SIGMA_DIFF)'PRECISION_THRESHOLD' ,0.5, ... threshold for precision of points (default: 0.5 Pixel) 'ROUNDNESS_THRESHOLD' ,0.3, ... threshold for roundness (default: 0.3)'SIGNIFICANCE_LEVEL' ,0.999, ... significance level for point classification (default: 0.999)'VISUALIZATION' ,'on'); ... visualization on or off (default : 'off')% 输出:disp('Results:');% a) integer positions of window centersdisp('Positions of window centers (actually were integers in the internally scaled image):');for i=1:length(win)fprintf('%5.1f %5.1f\n',win(i).r,win(i).c);end% b) subpixel positions of corners with covariance matrixdisp('Subpixel positions of corners with covariance matrix');for i=1:length(corner)r=corner(i).rc=corner(i).ccov=corner(i).covend% c) subpixel positions of circular points with covariance matrixdisp('Subpixel positions of circular points with covariance matrix:');for i=1:length(circ)r=circ(i).rc=circ(i).ccov=circ(i).covend% d) window centers of points with could not be classified unambiguously disp('Window centers of points with could not be classified unambiguously:');for i=1:length(noclass)r=noclass(i).rc=noclass(i).c end
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