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Binary Image Analysis
17
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Page 1: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

Binary Image Analysis

Page 2: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

YOU HAVE TO READ THE BOOK!

reminder

Page 3: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

What is a binary image?

• An image that has two possible values for each pixel.

Page 4: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

Thresholding

• A method that creates binary images.• An operation that divides pixels into two

groups: Foreground pixels and Background pixels

Page 5: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

Thresholding

• A simple threshold has one value t.• Usually:

g=image>t;– Pixels with values greater than t are:

foreground pixels.– Pixels with values smaller than t are:

background pixels.

• How else can we do it?

Page 6: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

Thresholding

• Threshold above and threshold below.• How do we choose the threshold value?– Simple: mean or median.– Histogram.

• Adaptive thresholding.• Multiband thresholding.

Page 7: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

Thresholding1. An initial threshold (T) is chosen, this can be done randomly or according

to any other method desired.2. The image is segmented into object and background pixels, creating two

sets: – G1 = {f(m,n):f(m,n)>T} (object pixels)– G2 = {f(m,n):f(m,n)T} (background pixels) (note, f(m,n) is the value of the pixel

located in the mth column, nth row)3. The average of each set is computed.

– m1 = average value of G1

– m2 = average value of G2

4. A new threshold is created that is the average of m1 and m2 – T’ = (m1 + m2)/2

5. Go back to step two, now using the new threshold computed in step four, keep repeating until the new threshold matches the one before it (i.e. until convergence has been reached).

Wikipedia (Thresholding)

Page 8: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

Histogram

• Display of frequencies of pixel intensity values in an image.

• The number of pixels found for every intensity value.

http://homepages.inf.ed.ac.uk/rbf/HIPR2/histgram.htm

Page 9: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

Multiband Thresholding

http://homepages.inf.ed.ac.uk/rbf/HIPR2/threshld.htm

Page 10: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

Adaptive Thresholding

• Use different threshold values for different regions of the image.

Page 11: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

Connected Components Labeling

• Used only with binary images.• It groups objects in images.• Scans the image for similar neighboring pixels.

http://homepages.inf.ed.ac.uk/rbf/HIPR2/label.htm

Page 12: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

Image Morphology

• Analysis and processing of geometrical structures.

• It is used in binary images.• Operations performed by structuring

elements on images.• Erosion, Dilation, Opening, Closing

Page 13: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

Image Morphology

• Structuring element example

Page 14: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

Image Morphology

• Dilation

Page 15: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

Image Morphology

• Erosion

Page 16: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

Image Morphology

• Opening

Page 17: Binary Image Analysis. YOU HAVE TO READ THE BOOK! reminder.

Image Morphology

• Closing