Image Segmentation. Longin Jan Latecki CIS 601. Image Segmentation. Segmentation divides an image into its constituent regions or objects. Segmentation of non trivial images is one of the difficult task in image processing. Still under research.
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Longin Jan Latecki
Image of a Finger Print with light background
Image after Segmentation
In Matlab histograms for images can be constructed using the imhist command.
I = imread('pout.tif');
figure, imhist(I) %look at the hist to get a threshold, e.g., 110
BW=roicolor(I, 110, 255); % makes a binary image
figure, imshow(BW) % all pixels in (110, 255) will be 1 and white
% the rest is 0 which is black
roicolor returns a region of interest selected as those pixels in I that match the values in the gray level interval.
BW is a binary image with 1's where the values of I match the values of the interval.
if T1 < (x,y) <= T2,
to the other object class
if f(x,y) > T2
and to the background
if f(x,y) <= T1.
1)Select an initial estimate for T
2)Segment the image using T. This will produce two groups of pixels. G1 consisting of all pixels with gray level values >T and G2 consisting of pixels with values <=T.
3)Compute the average gray level values mean1 and mean2 for the pixels in regions G1 and G2.
4)Compute a new threshold value
5)Repeat steps 2 through 4 until difference in T in successive iterations is smaller than a predefined parameter T0.
Discrete Curve Evolution
to find thresholds in the histogram.
It yields a sequence: P=P0, ..., Pm
Pi+1 is obtained from Pi by deleting the vertices of Pi that have minimal relevance measure
K(v, Pi) = |d(u,v)+d(v,w)-d(u,w)|
1) Two Gray scale image having bimodal histogram structure.
2) Gray scale image having multi-modal histogram structure.
3) Colour image having bimodal histogram structure.
Image of rice with black background
Image after segmentation
Image histogram of rice
Original Image of lena
Histogram of lena
Image after segmentation – we get a outline of her face, hat, shadow etc
Colour Image having a bimodal histogram
Histograms for the three colour spaces
Segmented image – giving us the outline of her face, hand etc
Each image point is mapped to a point in a color space, e.g.:
Color(i, j) = (R (i, j), G(i, j), B(i, j))
The points in the color space are grouped to clusters.
The clusters are then mapped back to regions in the image.
Original pictures segmented pictures
Mnp: 30, percent 0.05, cluster number 4
Mnp : 20, percent 0.05, cluster number 7
where xn is a vector representing the nth data point and mj is the geometric centroid of the data points in Sjk-means Clustering
An algorithm for partitioning (or clustering) N data points into K disjoint subsets Sj containing Nj data points so as to minimize the sum-of-squares criterion
Matlab programs are in
data=load('irises1.dat'); % loads a classic data set of Irises
[distance,cluster,tse] = kmeans1(data,3); %starts k-means clustering
showcluster(cluster,'irises1.dat'); % shows clusters in 3D projection obtained by PCA
[output_matrix] = test_tableform('ireses_gt.txt',cluster,3);
%if the ground truth is know, this function compares the clustering result to it