The document discusses digital image processing with a focus on edge detection, highlighting the significance of edges as boundaries in images and the various methods for detecting them, such as gradient and Gaussian-based operators. It also describes edge linking algorithms, categorizing them into local and global linkers, and introduces segmentation principles like automatic thresholding and region growing techniques for effective image interpretation. The process involves grouping connected pixels with similar properties to accurately represent different regions or objects within an image.

1. Nadar saraswathi college of arts
and science, Theni.
DIGITAL IMAGE PROCESSING
Edge models
By
G.NIBIYA.,II-MSC(IT)

2. First order edge defection
• Edges are significant local changes of intensity in a digital image. An
edge can be defined as a set of connected pixels that forms a
boundary between two disjoint regions. There are three types of
edges:
• Horizontal edges
• Vertical edges
• Diagonal edges

3. Edge operator performance
• Edge Detection is a method of segmenting an image into regions of
discontinuity. It is a widely used technique in digital image processing like
• pattern recognition
• image morphology
• feature extraction
• Edge detection allows users to observe the features of an image for a
significant change in the gray level. This texture indicating the end of one
region in the image and the beginning of another. It reduces the amount of
data in an image and preserves the structural properties of an image.

4. • Edge Detection Operators are of two types:
• Gradient – based operator which computes first-order derivations in
a digital image like, Sobel operator, Prewitt operator, Robert operator
• Gaussian – based operator which computes second-order derivations
in a digital image like, Canny edge detector, Laplacian of Gaussian

6. Edge linking algorithms
Edge detectors yield pixels in an image lie on edges.
The next step is to try to collect these pixels together into a set of edges.
Thus, our aim is to replace many points on edges with a few edges themselves.
The practical problem may be much more difficult than the idealised case.
•Small pieces of edges may be missing,
•Small edge segments may appear to be present due to noise where there is no real edge, etc.
In general, edge linking methods can be classified into two categories:
Local Edge Linkers
-- where edge points are grouped to form edges by considering each point's relationship to any
neighbouring edge points.
Global Edge Linkers
-- where all edge points in the image plane are considered at the same time and sets of edge points are
sought according to some similarity constraint, such as points which share the same edge equation.

7. • Local Edge Linking Methods
• Most edge detectors yield information about the magnitude of the
gradient at an edge point and, more importantly, the direction of the
edge in the locality of the point.
• This is obviously useful when deciding which edge points to link
together since edge points in a neighbourhood which have similar
gradients directions are likely to lie on the same edge.
• Local edge linking methods usually start at some arbitrary edge point
and consider points in a local neighbourhood for similarity of edge
direction

8. Principle of thresholding
• Automatic thresholding –
• To make segmentation more robust, the threshold should be
automatically selected by the system. –
• Knowledge about the objects, the application, the environment
should be used to choose the threshold automatically:
• * Intensity characteristics of the objects
• * Sizes of the objects
• * Fractions of an image occupied by the objects
• * Number of different types of objects appearing in an image

9. Principle of region
• A region in an image is a group of connected pixels with similar
properties. Regions are important for the interpretation of an image
because they may correspond to objects in a scene.
• An image may contain several objects and, in turn, each object may
contain several regions corresponding to different parts of the object.
For an image to be interpreted accurately, it must be partitioned into
regions that correspond to objects or parts of an object.

10. Growing
• Region Growing
• Region growing approach is the opposite of the split and merge approach:
•
An initial set of small areas are iteratively merged according to similarity constraints.
• Start by choosing an arbitrary seed pixel and compare it with neighbouring pixels (see Fig 37).
• Region is grown from the seed pixel by adding in neighbouring pixels that are similar, increasing the size of
the region.
• When the growth of one region stops we simply choose another seed pixel which does not yet belong to any
region and start again.
•
This whole process is continued until all pixels belong to some region.
• A bottom up method.

11. Thank you