CIS581-Presentation

Contour finding

Presented by: Wang , Qiang

Supervised by: Dr. Longin Jan Latecki

Agenda

Introduction

Five steps to reach the goal

(processing the image)

Conclusion

Contour finding

Introduction

•Output: Sequence of boundary pixels of the largest object

•Input: Binary image containing segmented objects as black pixels

•Apply to MPEG-7 Shape 1 data set. (1400 images)

Introduction Sample images:

•Pre-processing: erase the noise•Edge detection•Contour detection•Delete the redundant points•Simplify the contours

Five steps:

Processing

Pre-processing Pre-processing: Erasing the noisefor num_bmp=1:1400 tmp=imread(strcat(int2str(num_bmp),'.bmp')); tmp=medfilt2(tmp,[10 10]); x = zeros(size(tmp,1)+6,size(tmp,2)+6); x(4:size(tmp,1)+3,4:size(tmp,2)+3)=tmp;

x=x~=0; x=im2bw(x); …End% some other choices of filters: % tmp=filter2(1/25*ones(5,5),tmp);% tmp=ordfilt2(tmp,36,ones(6,6));

Pre-processing Pre-processing: Erasing the noiseAnother way to erase the noise: Object LabelingFind out all those pixels connected to each other, and label them with the same number.

1 1 1 0 0 0 0 01 1 1 0 1 1 0 01 1 1 0 1 1 0 01 1 1 0 0 0 1 01 1 1 0 0 0 1 01 1 1 0 0 0 1 01 1 1 0 0 1 1 01 1 1 0 0 0 0 0

[labeled,numObjects] = bwlabel(bw,4)

1 1 1 0 0 0 0 01 1 1 0 2 2 0 01 1 1 0 2 2 0 01 1 1 0 0 0 0

1 1 1 0 0 0 3 01 1 1 0 0 0 3 01 1 1 0 0 3 3 01 1 1 0 0 0 0 0

Pre-processing Pre-processing: Erasing the noise

Edge detection

Edge detection Fortunately… there is a very useful function in matlab: img_edge=edge(

x);

Edge detection

Contour detection

Problem: after edge detection, we just get an image with all those points on the contour as 1 while all the others as 0,we need to get the coordinates of those 1 pixels (vertices).

Basic idea: just travel around all the contour, find out all the pixels of 1.

Contour detectionHow to travel??

•Need a starting point;•Need to decide the direction to travel;•Need to avoid any circle in traveling.

(The last one is the most tricky thing)

Contour detection

Solution:

A matrix and

a vector

Contour detection

The matrix: Anti-clockwise

[1 1 0 -1 -1 -1 0 1 for x 0 1 1 1 0 -1 -1 -1]; for y

Contour detection

The vector:

1 2 3 4 5 6 7 8 Current neighbor index

[8 8 2 2 4 4 6 6] New neighbor indexMake sure we know which one will be next neighbor to check!Keep anti-clockwise direction.

Contour detectionCode:

[a b]=find(img_edge); counter=size(find(img_edge),1); point_x(1)=a(1); point_y(1)=b(1); % find out the starting point neighbor_offset=[1 1 0 -1 -1 -1 0 1;0 1 1 1 0 -1 -1 -1]; new_neighbor=[8 8 2 2 4 4 6 6]; neighbor=1;

for i=1:counter %try to find out all those pixels on the contour while img_edge(point_x(i)+neighbor_offset(1,neighbor),point_y(i)+neighbor_offset(2,neighbor))==0 neighbor=mod(neighbor,8)+1; end

point_x(i+1)=point_x(i)+neighbor_offset(1,neighbor); point_y(i+1)=point_y(i)+neighbor_offset(2,neighbor); neighbor=new_neighbor(neighbor);

if and(point_x(1)==point_x(i+1),point_y(1)==point_y(i+1)) break end end

Contour detection

redundant points

Delete the redundant points When there are many points on exact the same straight line,

we just need to keep the starting and ending points.

for i=1:size(point_x,2)-2; if or(and(point_x(i)==point_x(i+1),point_x(i+1)==point_x(i+2)), and(point_y(i)==point_y(i+1),point_y(i+1)==point_y(i+2))) point_x(i+1)=[]; point_y(i+1)=[]; end end

Simplify the contour

for i1=1:n-pointsleftevomaass=evo(Zneu);%Function: computes a relvance measure for every point"[y,I]=sort(evomaass);Zneu(I(1))=[];% in Zneu the points with the smallest relevance measure is % removedend

Simplify the Contour: using a small number of edges to represent the contour.

Simplify the contour

function K=evo(Z)n=length(Z);LM=norm(Z(2)-Z(n));LR=norm(Z(1)-Z(2));LL=norm(Z(n-1)-Z(1));His(1)=LL+LR-LM;for j=2:n-1 LM=norm(Z(j-1)-Z(j+1)); LR=norm(Z(j)-Z(j+1)); LL=norm(Z(j-1)-Z(j)); His(j)=LL+LR-LM;end;LM=norm(Z(n-1)-Z(1));LR=norm(Z(n)-Z(1));LL=norm(Z(n-1)-Z(n));His(n)=LL+LR-LM;K=His;

Simplify the contour

Conclusion

input image: 512 * 512 pixels

Output file: 50 points

input size: 250 KB Output size: < 400 bytes

Based on the output: we can do•Image compression•Image processing•Feature extraction and analysis

...

The end

Thanks!