Image representation

Introduction
• After an image is segmented into regions; the resulting
aggregate of segmented pixels is represented & described for
further computer processing.
• Representing a region involves two choices:
 in terms of its external characteristics
(boundary)
 in terms of its internal characteristics
(pixels comprising the region)
• Above scheme is only part of task of making data useful to
computer.
• Next task is to Describe the region based on representation.
• Ex. A region may be represented by its boundary & its boundary is
described by features such as its length, the orientation of straight line
joining its extreme points & number of concavities in the boundary.
2

Introduction
Q. Which to choose when?
• External representation is chosen when primary focus is on
shape characteristics.
• Internal representation is chosen when primary focus is on
regional properties such as color & texture.
• Sometimes it is necessary to choose both representations.
3

Representation
• It deals with compaction of segmented data into
representations that facilitate the computation of descriptors.
1) Boundary (Border) Following:
Most of the algorithms require that points in the boundary of a
region be ordered in a clockwise (or counterclockwise)
direction.
We assume
i) we work with binary images with object and background
represented as 1 & 0 respectively.
ii) Images are padded with borders of 0s to eliminate the
possibility of object merging with image border.
4

Representation
1) Let the starting point b0 be the uppermost, leftmost point in
the image. c0 the west neighbor of b0. Examine 8 neighbors
of b0 starting at c0 & proceed in clockwise direction.
1 1 1 1
1 1
1 1
1 1
1 1 1 1
Let b1 denote first neighbor encountered with value 1 & c1 be
background point immediately preceding b1 in the sequence.
5

Representation
2) Let b = b1 & c = c1
3) Let the 8-neighbors of b, starting at c & proceeding in
clockwise directions be denoted by n1, n2, …..n8. Find first nk
labeled 1.
4) Let b = nk & c = nk-1
c
c0 b0 1 1 1 b 1 1
1 1 1 1
1 1 1 1
1 1 1 1
1 1 1 1 1 1 1 1
6

Representation
2) Let b = b1 & c = c1
3) Let the 8-neighbors of b, starting at c & proceeding in
clockwise directions be denoted by n1, n2, …..n8. Find first nk
labeled 1.
4) Let b = nk & c = nk-1
c
b0 1 b 1 b 1 1
1 1 1 1
1 1 1 1
1 1 1 1
1 1 1 1 1 1 1 1
7

Representation
5) Repeat step 3 & 4 until b = b0 & next boundary point found is
b1. The sequence of b points found when the algorithm stops
constitutes the set of ordered boundary points.
The algorithm is also called as Moore boundary Tracking
algorithm.

Representation
2) Chain codes:
They are used to represent a boundary by a connected sequence
of straight line segments of specified length & direction.
Typically this representation is based on 4- or 8-connectivity of
segments.
The direction of each segment is coded by using a numbering
scheme.
1 3 2 1
2 0 4 0
3 5 6 7
9

Representation
• A boundary code formed as a sequence of such directional
numbers is referred to as a Freeman chain code.
• Digital images are acquired & processed in a grid format with
equal spacing in x and y directions.
• So a chain code can be generated by following a boundary
(say clockwise direction) and assigning a direction to the
segments connecting every pair of pixels.
Unacceptable method: (because)
1) Resulting chain tends to be quite long
2) Any small disturbances along the boundary due to noise or
imperfect segmentation can cause changes in code.
10

Representation
11
0033333323221211101101
076666553321212

Representation
• A solution to this problem is to resample the boundary by
selecting a larger grid spacing.
• Then, as the boundary is traversed, a boundary point is
assigned to each node of the large grid, depending upon the
proximity of original boundary to that node.
• The re-sampled boundary can now be represented by a 4- or
8-code.
• The accuracy of the resulting code representation depends
on the spacing of the sampling grid.
12

Representation
3) Polygon Approximation using Min. Perimeter Polygons:
• A digital boundary can be approximated with arbitrary
accuracy by a polygon.
• For a closed boundary, approx becomes exact when no. of
segments of polygon = no. of points in the boundary.
• Goal of poly. Approx is to capture the essence of the shape in
a given boundary using fewest no. of segments.
• Min. Perimeter Polygon (MPP):
– An approach for generating an algorithm to compute MPPs is
to enclose a boundary by a set of concatenated cells.
– Think boundary as a r u b b e r b a n d .
– As allowed to shrink, it will be constrained by the inner & outer
walls of the bounding regions.
13

Representation
• This shrinking produces the shape of a polygon of min.
perimeter.
• Size of cells determine the accuracy of the polygonal
approximation.
• In the limit if size of each cell corresponds to a pixel in the
boundary , the error in each cell between the boundary &
the MPP approx. at most would be √2d, where d-min
possible pixel distance.
15

Representation
• The objective is to use the largest possible cell size
acceptable in a given application.
• Thus, producing MPPs with fewest no. of vertices.
• The cellular approach reduces the shape of the object
enclosed by the original boundary to the area circumscribed
by the gray wall.
• Fig. shows shape in dark gray.
• Its boundary consists of 4-connected straight line segments.
16

Representation
• If we traverse the boundary in counter clockwise direction.
• Every turn encountered in the transversal will be either a
convex (white) or concave(black) vertex, with the angle of
vertex being an interior angle of the 4-connected boundary.
• Every concave vertex has a corresponding mirror vertex in
light gray wall, located diagonally opposite.
• MPP vertices coincide with the convex vertices of inner wall
and mirror concave vertices of outer wall.
17

Image representation

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