The document provides an overview of image segmentation, which is the process of partitioning an image into meaningful regions based on characteristics such as color, texture, or depth. It outlines various methods including edge detection, region-based segmentation, and algorithms used for detecting objects, particularly in applications like medical imaging and fruit disease detection. Additionally, it emphasizes the importance of segmentation in retaining crucial data while minimizing irrelevant information.

1. Image Segmentation
Presented by:
Bulbul Agrawal
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2. Introduction to image
segmentation
• The purpose of image segmentation is to
partition an image into meaningful regions
with respect to a particular application.
• The segmentation is based on
measurements taken from the image and
might be greylevel, colour, texture, depth
or motion.
• Segmentation refers to the process of
partitioning a digital image into multiple
regions (sets of pixels).
• Image segmentation is typically used to
locate objects and boundaries in images 2

3. Introduction to image segmentation
Usually image segmentation pixels in a
region are similar with respect to some
characteristic or computed property, such as
color, intensity, or texture.
Applications of image segmentation
include:
Identifying objects in a scene (size and
shape)
Identifying objects in a moving scene
(dynamic object)
Identifying objects which are at different
distances
Some applications of image
segmentation in medical field includes: –
Locate tumors 3

4. Introduction to image
segmentation
• Segmentation divides an image into its
constituent parts.
• Level of subdivision depends on the
problem being solved.
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5. Introduction to image
segmentation
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6. Introduction to image
segmentation
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7. Edge detection
• Edge detecting an image significantly
reduces the amount of data and filters out
useless information, while preserving the
important structural properties in an
image.
• Detects discontinuities of the grey level.
• Detection of the edge boundary between
the two regions.
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8. Isolated point detection
• Only those points which are large enough to be
considered are only selected and considered and find
out the value for it
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9. Line detection
• Line detection includes a variety of mathematical
methods that aim at identifying points in a digital
image at which the image brightness changes sharply
or, more formally, has discontinuities. The points at
which image brightness changes sharply are typically
organized into a set of curved line segments
termed edges.
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10. Region based segmentation
• Its main goal is to partition an image into regions.
• Region based segmentation is the technique that
allows us to determine the region directly.
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11. Region Growing
• Group pixels or sub-regions into larger
regions when homogeneity criterion is
satisfied.
• Region grows around the seed point based
on similar properties (grey level, texture,
color).
• Select single random pixel to find region
and work on its values.
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12. Region Splitting
• Rule to be followed strictly so that we can split the
regions.
• Region growing starts from a set of seed points.
• An alternative is to start with the whole image as a
single region and subdivide the regions that do not
satisfy a condition of homogeneity.
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13. Region Merging
• Region merging is the opposite of region
splitting.
• Start with small regions (e.g. 2x2 or 4x4
regions) and merge the regions that have similar
characteristics (such as gray level, variance).
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14. Split and Merge
• This is a 2 step procedure:
• Top-down: split image into homogeneous
quadrant regions – bottom-up: merge similar
adjacent regions
• Top-down – successively subdivide image into
quadrant regions Ri – stop when all regions are
homogeneous: P(Ri ) = TRUE) obtain quadtree
structure
• Bottom-up – at each level, merge adjacent regions
Ri and Rj if P(Ri[ Rj) = TRUE • Iterate until no
further splitting/merging is possible
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15. Segmentation Algorithms
• Segmentation algorithms are based on one of
the basic properties of gray level values,
1. Discontinuity: partition in abrupt
discontinuity:
-> Detection of isolated points
->Detection of lines and edges in an image
2. Similarity: detect similar regions
(Discontinuity + Similarity ) = gray level pixel
values static and dynamic
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16. Implementation of image
segmentation in fruit disease
detection
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• Training apple fruit image
• Image segmentation
• Feature extraction
• Result

17. Conclusion
• Image segmentation is useful to detect the
details of the image
• It is used to find the points of the image which
are important
• Deletes the unnecessary data
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18. References
• Digital image processing by Gonzalez and
woods
• Digital image processing by S Jayaraman
• https://www.techopedia.com/definition/26314/
image segmentation
• https://www.slideshare.net/Ayaelshiwi/image-
segmentation-29760056
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