Extract the neighborhood of a contour pixel of an obstacle.
[M,N,D] = NL_V_PixelNeighborCont(X,Y,I,C)
Line index.
Column index.
Binary image.
Contour matrix.
Image neighborhood of defined pixel.
Contour neighborhood of defined pixel.
Number of modifications along the linear neighborhood.
NL_V_PixelNeighborCont extracts the neighborhood of the pixel [X,Y] from the contour C of the obstacle I. For each pixel in the neighborhood of [X,Y], we have 8 neighbors represented by their relative coordinates [-1 -1 0 1 1 1 0 -1 ; 0 1 1 1 0 -1 -1 -1 ]. For each successive neighbors, we add the absolute values of their difference I(N(i))-I(N(i+1)) (cycle) to D that gives an idea about the number of changes of the image along the linear neighborhood N of the pixel [X,Y].
dt=getdate(); seed=dt(10); rand('seed',seed);//initialization of the random values generator no=4;//quantity of obstacles (rectangle) L=1000;//squared area side hm=100;//minimal height hM=250;//maximal height wm=100;//minimal width wM=250;//maximal width Al=[0 %pi/2 %pi -%pi/2];//available angles for obstacles [Xs,Ys,X,Y,H,W,A]=NL_V_RectanglesCorners(no,L,hm,hM,wm,wM);//generation of obstacles [P]=NL_V_PotentialRectangles(X,Y,H,W,A,L);//generation of obstacle matrix z=10;//zoom factor o=2;// 1=mean, 2=max, 3=min, 4=median Pz=NL_V_MRA(P,z,o);//scale modification [Pzx,Pzy]=size(Pz);//image size w1=1;//window index w2=2;//window index scf(w1); clf(w1); grayplot(1:Pzx,1:Pzy,Pz);//graph visualization xset("colormap",graycolormap(128)); scf(w2); clf(w2); [PEz]=NL_V_Erosion(Pz);//contour performance Cont=Pz-PEz;//contour 1 [Contx,Conty]=size(Cont);//image size grayplot(1:Contx,1:Conty,Cont);//graph visualization xset("colormap",graycolormap(128)); [Indx Indy]=find(Cont==1);//coordinates of contour points [mat,nei,dch]=NL_V_PixelNeighborCont(Indx(1),Indy(1),Pz,Cont)//application of NL_V_PixelNeighborCont | ![]() | ![]() |