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On Feb 10, 12:44 pm, 'NIcholas ' <dsfa.@aol.com> wrote: > I want to run a loop like this > for i=1:100 >. do something. > pause; > end > > How do I detect the key the person pressed that broke the pause? In the example. Function message = pass_or_fail(StudentData) Exam_avg = mean(StudentData.Exam); if(Exam_avg >= 70) message = 'You pass!'; else message = 'You fail!'; end return; 7.2. Input assertion routines Good programming style dictates. Clc clear M=100; Re=5000; L=0.444; dx=L/M; x(1)=0; for i=1:M; x(i+1)=x(i)+dx; delta(i)=5*x(i+1)/((Re)^0.5) end plot(x(1:M),delta) %axis equal grid on title('Relationship between Boundary Layer Thicknes and Reynolds Number.
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Clc clear E=69e12; D=0.005; A=0.25*(pi)*(D)^2; I=(pi/4)*(D)^4; L=1; P=-100000; PP=(P/(12*E*I)); PPP=3/4 M=100; DX=L/M; for i=1:M/2; X(i)=i*DX; Y(i)=PP*X(i)*(PPP*(L^2)-(X(i))^2) end ii=M/2 for i=M/2:M-2; ii=ii-1 X(i)=i*DX; Y(i. This example shows how to detect and count cars in a video sequence using foreground detector based on Gaussian mixture models (GMMs).
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Let's start from the end. Here is the final output of this chapter. When we play this video, we'll see the white cars are tagged with red marks. Your browser does not support the video tag. Canny edge detector algorithm matlab codes. This part gives the algorithm of Canny edge detector. The outputs are six subfigures shown in the same figure: Subfigure 1: The initial 'lena' Subfigure 2: Edge detection along X. Setting Up the C Compiler..2-6 Setting Up File Infrastructure and Paths.. 2-7 Compile Path Search Order..2-7 Can I Add Files to the Embedded MATLAB Path?. 2-7 When to Use.
MATLAB Function Reference : Index. Symbols!-% & <1> <2> && ' <1> <2> * +. /: < = == > \ ^ | <1> <2> || ~ <1> <2> ~= Numerics 1-norm <1> <2>. Open source tools, information and interactive games related to laser optics, numerical modeling and detecting gravitational waves.
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Canny edge detector algorithm matlab codes This part gives the algorithm of Canny edge detector. The outputs are. six subfigures shown in the same figure: Subfigure 1: The initial "lena". Subfigure 2: Edge detection along X- axis direction. Subfigure 3: Edge detection along Y- axis direction. Subfigure 4: The Norm of the image gradient. Subfigure 5: The Norm of the gradient after thresholding.
Subfigure 6: The edges detected by thinning. The matlab codes. The main. m file %%%%%%%%%%%%%%%.
The algorithm parameters. Parameters of edge detecting filters. X- axis direction filter. Nx. 1=1. 0; Sigmax.
Nx. 2=1. 0; Sigmax. Theta. 1=pi/2. % Y- axis direction filter. Ny. 1=1. 0; Sigmay. Ny. 2=1. 0; Sigmay. Theta. 2=0. % 2. The thresholding parameter alfa. Get the initial image lena. Image: lena. gif').
X- axis direction edge detection. Nx. 1,Sigmax. 1,Nx. Sigmax. 2,Theta. 1). Ix= conv. 2(w,filterx,'same'). Y- axis direction edge detection. Ny. 1,Sigmay. 1,Ny. Sigmay. 2,Theta. 2).
Iy=conv. 2(w,filtery,'same'). Norm of the gradient (Combining the X and Y directional derivatives). NVI=sqrt(Ix.*Ix+Iy.*Iy). Norm of Gradient'). I_max=max(max(NVI)). I_min=min(min(NVI)). I_max- I_min)+I_min.
Ibw=max(NVI,level.*ones(size(NVI))). After Thresholding').
Thinning (Using interpolation to find the pixels where the norms of. Ibw). if Ibw(i,j) > level. X=[- 1,0,+1; -1,0,+1; -1,0,+1]. Y=[- 1,- 1,- 1; 0,0,0; +1,+1,+1]. Z=[Ibw(i- 1,j- 1),Ibw(i- 1,j),Ibw(i- 1,j+1).
Ibw(i,j- 1),Ibw(i,j),Ibw(i,j+1). Ibw(i+1,j- 1),Ibw(i+1,j),Ibw(i+1,j+1)]. XI=[Ix(i,j)/NVI(i,j), - Ix(i,j)/NVI(i,j)]. YI=[Iy(i,j)/NVI(i,j), - Iy(i,j)/NVI(i,j)]. ZI=interp. 2(X,Y,Z,XI,YI).
Ibw(i,j) > = ZI(1) & Ibw(i,j) > = ZI(2). I_temp(i,j)=I_max. I_temp(i,j)=I_min. I_temp(i,j)=I_min. I_temp). title('After Thinning').
End of the main. m file %%%%%%%%%%%%%%%. The functions used in the main.
Function "d. 2dgauss. This function returns a 2. D edge detector (first order derivative. D Gaussian function) with size n. Function "gauss. m".
Function "dgauss. Back End of document.
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