DIGITAL IMAGE PROCESSING

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# DIGITAL IMAGE PROCESSING - PowerPoint PPT Presentation

Chapter 9 – Morphological Image Processing. DIGITAL IMAGE PROCESSING. J. Shanbehzadeh, M. Mahdijo Shanbehzadeh@gmail.com. Khwarizmi University of Tehran. Table of Contents. Preview. Morphology : form and structure Extracting image components for:

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Chapter 9 – Morphological Image Processing

### DIGITAL IMAGE PROCESSING

Khwarizmi University of Tehran

Preview
• Morphology : form and structure
• Extracting image components for:

representation and description of region shape

• From

Image processing methods

Input: Image Output: Image

• To

Image processing methods

Input: Image Output: Attributes

9.1 Preliminaries
• Reflection and Translation:
• Translation :

(B)z = {c | c = b + z, for b є B}

9.1 Preliminaries
• Structuring Elements:
9.1 Preliminaries
• Structuring Elements:
9.2 Erosion and Dilation
• Erosion

(B)z = {c | c = b + z, for b є B}

Computing the Erosion of a Binary Image
• For each foreground pixel, we superimpose the structuring element on top of the input image so that the origin of the structuring element coincides with the input pixel coordinates.
• If for every pixel in the structuring element, the corresponding pixel in the image underneath is
• a foreground pixel, then the input pixel is left as it is.
• Otherwise, it is set to background value.
The effect of Erosion
• removing any foreground pixel that is not completely surrounded by other white pixels (assuming 8-connectedness).
• Such pixels must lie at the edges of white regions, and so the practical upshot is that foreground regions shrink (and holes inside a region grow).
9.2 Erosion
• Erosion (More examples):
9.2 Erosion
• Erosion (More examples):
9.2 Dilation

(B)z = {c | c = b + z, for b є B}

• Dilation
Dilation
• The basic effect of the operator on a binary image is to gradually enlarge the boundaries of regions of foreground pixels.
• Thus areas of foreground pixels grow in size while holes within those regions become smaller
Gray level erosion /dilation

􀂄 Erosion:

􀂄 Chose the local minimum over the region defined by

the structure element

􀂄 Put the minimums value in the same pixel position in

the out image Results in darker images and light details are removed

Closing

Opening

Dilation

Erosion

Isthmus

An isthmus (/ˈɪsθməs/ or /ˈɪsməs/; plural: isthmuses; from Ancient Greek:ἰσθμόςisthmos “neck”) is a narrow strip of land connecting two larger land areas, usually with water on either side.[1] A tombolo is an isthmus where the strip of land consists of a spit or bar.

The Suez Canal goes across the western side of the Sinai Peninsula

The Isthmus of Panama

pro·trude  (prō-tro̅o̅d′)v.pro·trud·ed, pro·trud·ing, pro·trudespro·trude  (prō-tro̅o̅d′)v.pro·trud·ed, pro·trud·ing, pro·trudes

v.tr.To push or thrust outward.

v.intr.To jut out; project. See Synonyms at bulge.

Example sentences

The air-conditioner does not protrude  into the alley.

They are large-bodied and display a mouthful ofsharp teeth that protrude  in

Samantha's face and paws protrude  from thecutout door.

Scrub the mussels, and use a paring knife toremove any beards that protrude

9.3 Opening and Closing
• Opening:
• smoothes the contour of an object , breaks narrow isthmuses, and eliminates thin protrusions.

Erosion

Dilation

• Opening is defined as an erosion followed by a dilation using the same structuring element for both operations.
Opening
• Opening removes some of the foreground (bright) pixels from the edges of regions of foreground pixels.
• Opening is less destructive than erosion in general.
• The exact operation is determined by a structuring element.
Opening
• The effect of the operator is:
• to preserve foreground regions that
• have a similar shape to this structuring element,
• or can completely contain the structuring element,
• Eliminating all other regions of foreground pixels.
Opening

Effect of opening using a 3×3 square structuring element

Opening
• opening can be very useful for separating out particularly shaped objects from the background,
• opening is far from being a universal 2-D object recognizer/segmenter.
• E.g. if we use a long thin structuring element to locate a pencils in our image, any one such element will only find pencils at a particular orientation.
Opening
• If it is necessary to find pencils at other orientations then differently oriented elements must be used to look for each desired orientation.
• It is also necessary to be very careful that the structuring element chosen does not eliminate
• too many desirable objects, or
• retain too many undesirable ones, and
• sometimes this can be a delicate or even impossible balance
9.3 Opening

Opening:

9.3 Opening

Opening:

Separate out the circles from the lines, so that they can be counted.Opening with a disk shaped structuring element 11 pixels in diameter gives

9.3 Opening

Opening:

Extracting the horizontal and vertical linesThe results of an Openingwith a 3×9 vertically and 9x3 horizontally oriented structuring elementis shown.

9.3 Opening and Closing

Opening in removing salt noise:

9.3 Opening and Closing

Opening in removing pepper noise:

9.3 Closing

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• Closing:
• Smooth sections of contours, but as opposed to Opening, it generally fuses narrow breaks and long thin gulfs, eliminates small holes, and fills gaps in the contour

Dilation

Erosion

Closing
• Closing is opening performed in reverse.
• Closing is the dual of opening,
• i.e. closing the foreground pixels with a particular structuring element, is equivalent to closing the background with the same element.
• a dilation followed by an erosion using the same structuring element for both operations.
• The closing operator requires two inputs:
• an image to be closed
• a structuring element.
Closing

Effect of closing using a 3×3 square structuring element

9.3 Closing

Closing:

9.3 Closing: exapmles

Removing the small holes while retaining the large holes

Closingwitha 22 pixel diameter disk

• Closing with a disk-shaped structuring element with a diameter larger than the smaller holes
9.3 Closing: examples

Enhance binary images of objects obtained from thresholding

9.3 Closing

Closing for pepper noise:

9.3 Closing

Closing for salt noise:

9.3 Opening and Closing

(a) A o B is a subset (subimage) of A.

(b) If C is a subset of D, then C o B is a subset of D o B.

(c) (A o B) o B = A o B.

(a) A is a subset (subimage) of A • B.

(b) If C is a subset of D, then C • B is a subset of D • B.

(c) (A•B)•B=A•B.

9.4 The Hit-or-Miss Transformation

The hit-and-miss transform is a general binary morphological operation that can be used to look for particular patterns of foreground and background pixels in an image. It is actually the basic operation of binary morphology since almost all the other binary morphological operators can be derived from it.

9.4 The Hit-or-Miss Transformation

The structuring element used in the hit-and-miss is a slight extension to the type that has been introduced for erosion and dilation, in that it can contain both foreground and background pixels, rather than just foreground pixels.

Example of the extended type of structuring element used in hit-and-miss operations. This particular element can be used to find corner points.

9.4 The Hit-or-Miss Transformation

Four structuring elements used for corner finding in binary images using the hit-and-miss transform. Note that they are really all the same element, but rotated by different amounts.

After obtaining the locations of corners in each orientation, We can then simplyORall these images together to get the final result showing the locations of all right angle convex corners in any orientation.

Closing

Opening

Erosion

Dilation

The Hit-or-Miss Transformation

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