![Gray-level Slicing: approach 1
Solution:
x=imread('cameraman.tif');
y=x;
[w h]=size(x);
for i=1:w
for j=1:h
if x(i,j)>=100 && x(i,j)<=200 y(i,j)=255;
else y(i,j)=0;
end
end
end
figure, imshow(x);
figure, imshow(y);
Hanan Hardan 6](https://image.slidesharecdn.com/image-processing-ch3-part-3-230731083036-0927ba9f/85/Image-Processing-ch3-part-3-pdf-6-320.jpg)
![Gray-level Slicing: approach 2
Solution:
x=imread('cameraman.tif');
y=x;
[w h]=size(x);
for i=1:w
for j=1:h
if x(i,j)>=100 && x(i,j)<=200 y(i,j)=255;
else y(i,j)=x(i,j);
end
end
end
figure, imshow(x);
figure, imshow(y);
Hanan Hardan 8](https://image.slidesharecdn.com/image-processing-ch3-part-3-230731083036-0927ba9f/85/Image-Processing-ch3-part-3-pdf-8-320.jpg)
![Bit-plane Slicing- programmed
example: apply bit-plane slicing in Matlab to read cameraman
image , then extract the image of bit 6.
Solution:
x=imread('cameraman.tif');
y=x*0;
[w h]=size(x);
for i=1:w
for j=1:h
b=bitget(x(i,j),6);
y(i,j)=bitset(y(i,j),6,b);
end
end
figure, imshow(x);
figure, imshow(y);
Hanan Hardan 17](https://image.slidesharecdn.com/image-processing-ch3-part-3-230731083036-0927ba9f/85/Image-Processing-ch3-part-3-pdf-17-320.jpg)
![Histogram?
The histogram of a digital image with gray
levels in the range [0, L-1] is a discrete
function:
h(rk) = nk
Where:
rk : kth gray level
nk : # of pixels with having gray level rk
Hanan Hardan 19](https://image.slidesharecdn.com/image-processing-ch3-part-3-230731083036-0927ba9f/85/Image-Processing-ch3-part-3-pdf-19-320.jpg)
![Histogram in MATLAB
h = imhist (f, b)
Where f, is the input image, h is the histogram, b is number
of bins (tick marks) used in forming the histogram (b = 255
is the default)
A bin, is simply, a subdivision of the intensity scale. For
example, if we are working with uint8 images and we let b
= 2, then the intensity scale is subdivided into two ranges:
0 – 127 and 128 – 255. the resulting histograms will have
two values: h(1) equals to the number of pixels in the
image with values in the interval [0,127], and h(2) equal to
the number of pixels with values in the interval [128 255].
Hanan Hardan 26](https://image.slidesharecdn.com/image-processing-ch3-part-3-230731083036-0927ba9f/85/Image-Processing-ch3-part-3-pdf-26-320.jpg)
Image-Processing-ch3-part-3.pdf
- 1. Image Processing Chapter(3) Part 3:Intensity Transformation and spatial filters Prepared by: Hanan Hardan Hanan Hardan 1
- 2. Gray-level Slicing This technique is used to highlight a specific range of gray levels in a given image. – Similar to thresholding – Other levels can be suppressed or maintained – Useful for highlighting features in an image It can be implemented in several ways, but the two basic themes are: One approach is to display a high value for all gray levels in the range of interest and a low value for all other gray levels. The second approach, based on the transformation brightens the desired range of gray levels but preserves gray levels unchanged. Hanan Hardan 2
- 3. Gray-level Slicing display in one value(e.g white) all the values in the range of interest , and in another (e.g black) all other intensities Brightens or darkens the desired range of intensities but leaves all other intensity levels in the image unchanged Highlighting a specific range of intensities in an image. Approach 1 Approach 2 Hanan Hardan 3
- 5. Gray-level Slicing: approach 1 example: apply intensity level slicing in Matlab to read cameraman image , then If the pixel intensity in the old image is between (100 200) convert it in the new image into 255 (white). Otherwise convert it to 0 (black). Hanan Hardan 5
- 6. Gray-level Slicing: approach 1 Solution: x=imread('cameraman.tif'); y=x; [w h]=size(x); for i=1:w for j=1:h if x(i,j)>=100 && x(i,j)<=200 y(i,j)=255; else y(i,j)=0; end end end figure, imshow(x); figure, imshow(y); Hanan Hardan 6
- 7. example: apply intensity level slicing in Matlab to read cameraman image , then If the pixel intensity in the old image is between (100 200) convert it in the new image into 255 (white). Otherwise it leaves it the same. Gray-level Slicing: approach 2 Hanan Hardan 7
- 8. Gray-level Slicing: approach 2 Solution: x=imread('cameraman.tif'); y=x; [w h]=size(x); for i=1:w for j=1:h if x(i,j)>=100 && x(i,j)<=200 y(i,j)=255; else y(i,j)=x(i,j); end end end figure, imshow(x); figure, imshow(y); Hanan Hardan 8
- 9. example: apply intensity level slicing (approch2) in Matlab to read moon image , then If the pixel intensity in the old image is between (0 20) convert it in the new image into 130. Gray-level Slicing Homework Hanan Hardan 9
- 10. Bit-plane Slicing Pixels are digital numbers, each one composed of bits. Instead of highlighting gray-level range, we could highlight the contribution made by each bit. This method is useful and used in image compression. Most significant bits contain the majority of visually significant data. Hanan Hardan 10
- 11. Remember that pixels are digital numbers composed of bits. 8-bit Image composed of 8 1-bit planes Bit-plane Slicing Hanan Hardan 11
- 12. Bit-plane Slicing Often by isolating particular bits of the pixel values in an image we can highlight interesting aspects of that image – Higher-order bits usually contain most of the significant visual information – Lower-order bits contain subtle details Hanan Hardan 12
- 16. Bit-plane Slicing(example) We have to use bit get and bit set to extract 8 images; 100 0 1 1 0 0 1 0 0 0 0 4 0 0 32 64 0 Image of bit1: 00000000 Image of bit2: 00000000 Image of bit3: 00000100 Image of bit4: 00000000 Image of bit5: 00000000 Image of bit6: 00100000 Image of bit7: 01000000 Image of bit8: 00000000 Hanan Hardan 16
- 17. Bit-plane Slicing- programmed example: apply bit-plane slicing in Matlab to read cameraman image , then extract the image of bit 6. Solution: x=imread('cameraman.tif'); y=x*0; [w h]=size(x); for i=1:w for j=1:h b=bitget(x(i,j),6); y(i,j)=bitset(y(i,j),6,b); end end figure, imshow(x); figure, imshow(y); Hanan Hardan 17
- 18. Histogram Processing What is a Histogram? In Statistics, Histogram is a graphical representation showing a visual impression of the distribution of data. An Image Histogram is a type of histogram that acts as a graphical representation of the lightness/color distribution in a digital image. It plots the number of pixels for each value. Hanan Hardan 18
- 19. Histogram? The histogram of a digital image with gray levels in the range [0, L-1] is a discrete function: h(rk) = nk Where: rk : kth gray level nk : # of pixels with having gray level rk Hanan Hardan 19
- 20. Image Histogram Hanan Hardan 20
- 21. Histogram Processing It is common practice to normalize a histogram by dividing each of its values by the total number of pixels in the image, denoted by n. Thus, a normalized histogram is given by p(rk) = nk / n, for k = 0, 1, …, L -1. Thus, p(rk) gives an estimate of the probability of occurrence of gray level rk. Note that the sum of all components of a normalized histogram is equal to 1. Hanan Hardan 21
- 22. Why Histogram? Histograms are the basis for numerous spatial domain processing techniques Histogram manipulation can be used effectively for image enhancement Histograms can be used to provide useful image statistics Information derived from histograms are quite useful in other image processing applications, such as image compression and segmentation. Hanan Hardan 22
- 23. Histogram of the image: histogram h(rk) = nk Where: rk : kth gray level nk : # of pixels with having gray level rk كل في البكسل لعدد تمثيل هو درجات من لونية قيمة gray levels Hanan Hardan 23
- 24. Histogram of the image: For example: we have 600 pixels having the intensity value ≈ 160 Hanan Hardan 24
- 25. Histogram of the image: low contrast image low contrast image low contrast image high-contrast image An image whose pixels tend to occupy the entire range of possible gray levels and, in addition, tend to be distributed uniformly, will have an appearance of high contrast and will exhibit a large variety of gray tones. Hanan Hardan 25
- 26. Histogram in MATLAB h = imhist (f, b) Where f, is the input image, h is the histogram, b is number of bins (tick marks) used in forming the histogram (b = 255 is the default) A bin, is simply, a subdivision of the intensity scale. For example, if we are working with uint8 images and we let b = 2, then the intensity scale is subdivided into two ranges: 0 – 127 and 128 – 255. the resulting histograms will have two values: h(1) equals to the number of pixels in the image with values in the interval [0,127], and h(2) equal to the number of pixels with values in the interval [128 255]. Hanan Hardan 26
- 27. Histogram in MATLAB We obtain the normalized histogram simply by using the expression. p = imhist (f, b) / numel(f) numel (f): a MATLAB function that gives the number of elements in array f (i.e. the number of pixels in an image). Hanan Hardan 27
- 28. Other ways to display Histograms Consider an image f. The simplest way to plot its histogram is to use imhist with no output specified: >> imhist (f); Figure 3.7(a) shows the result. Hanan Hardan 28
- 29. Other ways to display Histograms A stem graph A bar graph A Plot graph >> h = imhist(f); >> bar (h); >> plot (h); >> stem (h); Hanan Hardan 29
- 30. Histogram equalization of the image: We have this image in matlab called pout.tif, when we plot its histogram it is showed like this: Notice that the pixels intensity values are concentrated on the middle (low contrast) Hanan Hardan 30
- 31. Histogram equalization of the image: histogram equalization : is the process of adjusting intensity values of pixels. The process which increases the dynamic range of the gray level in a law contrast image to cover full range of gray levels. Im matlab : we use histeq function Histogram produces pixels having values that are distributed throughout the range Hanan Hardan 31
- 32. Histogram equalization of the image: Notice that histogram equalization does not always produce a good result Hanan Hardan 32
- 33. Equalization (mathematically) g(x) = (L/n). T(X) -1 Where, G(X) : the new image after equalization L: No of gray levels 2n n: No of pixels T(x): cumulative sum of each gray level Hanan Hardan 33
- 34. Equalization (mathematically) G(x) T(X) تراكمي مجموع للبكسل X لكل البكسل عدد Graylevel L grayl evels 0 1 1 0 0 4 3 1 1 9 5 2 2 15 6 3 4 21 6 4 5 27 6 5 6 29 2 6 7 32 3 7 Assume that we have (3bits per pixels) or 8 levels of grayscale, and we want to equalize the following image example. G(x)=(L/n). T(X) -1 =(8/32). T(x) -1 No of pixels الكلي البكسالت عدد 8 ال عدد graylevel Hanan Hardan 34