# Documentation

### This is machine translation

Translated by
Mouse over text to see original. Click the button below to return to the English verison of the page.

## Correcting Nonuniform Illumination

This example shows how to correct nonuniform illumination in an image to make it easy to identify individual grains of rice in the image. You can then learn about the characteristics of the grains and easily compute statistics for all the grains in the image.

imshow(I)

### Step 2: Use Morphological Opening to Estimate the Background

Notice that the background illumination is brighter in the center of the image than at the bottom. Use imopen to estimate the background illumination.

background = imopen(I,strel('disk',15));

% Display the Background Approximation as a Surface
figure
surf(double(background(1:8:end,1:8:end))),zlim([0 255]);
ax = gca;
ax.YDir = 'reverse';

### Step 3: Subtract the Background Image from the Original Image

I2 = I - background;
imshow(I2)

Note that step 2 and step 3 together could be replaced by a single step using imtophat which first calculates the morphological opening and then subtracts it from the original image.

I2 = imtophat(I,strel('disk',15));

imshow(I3);

### Step 5: Threshold the Image

Create a new binary image by thresholding the adjusted image. Remove background noise with bwareaopen.

bw = imbinarize(I3);
bw = bwareaopen(bw, 50);
imshow(bw)

### Step 6: Identify Objects in the Image

The function bwconncomp finds all the connected components (objects) in the binary image. The accuracy of your results depend on the size of the objects, the connectivity parameter (4,8,or arbitrary), and whether or not any objects are touching (in which case they may be labeled as one object). Some of the rice grains in bw are touching.

cc = bwconncomp(bw, 4)
cc =

struct with fields:

Connectivity: 4
ImageSize: [256 256]
NumObjects: 95
PixelIdxList: {1×95 cell}

### Step 7: Examine One Object

Each distinct object is labeled with the same integer value. Show the grain that is the 50th connected component.

grain = false(size(bw));
grain(cc.PixelIdxList{50}) = true;
imshow(grain);

### Step 8: View All Objects

One way to visualize connected components is to create a label matrix and then display it as a pseudo-color indexed image.

Use labelmatrix to create a label matrix from the output of bwconncomp. Note that labelmatrix stores the label matrix in the smallest numeric class necessary for the number of objects.

labeled = labelmatrix(cc);
whos labeled
Name           Size             Bytes  Class    Attributes

labeled      256x256            65536  uint8

In the pseudo-color image, the label identifying each object in the label matrix maps to a different color in the associated colormap matrix. Use label2rgb to choose the colormap, the background color, and how objects in the label matrix map to colors in the colormap.

RGB_label = label2rgb(labeled, @spring, 'c', 'shuffle');
imshow(RGB_label)

### Step 9: Compute Area of Each Object

Each rice grain is one connected component in the cc structure. Use regionprops on cc to get the area.

graindata = regionprops(cc,'basic')
graindata =

95×1 struct array with fields:

Area
Centroid
BoundingBox

To find the area of the 50th component, use dot notation to access the Area field in the 50th element of graindata structure array.

graindata(50).Area
ans =

194

### Step 10: Compute Area-based Statistics

Create a new vector grain_areas, which holds the area measurement for each grain.

grain_areas = [graindata.Area];

Find the grain with the smallest area.

[min_area, idx] = min(grain_areas)
grain = false(size(bw));
grain(cc.PixelIdxList{idx}) = true;
imshow(grain);
min_area =

61

idx =

16

### Step 11: Create Histogram of the Area

figure
histogram(grain_areas)
title('Histogram of Rice Grain Area');

﻿